8 Best AI Coding Assistants [Updated January 2025]

Real codebases are years of good intentions, architectural compromises that made sense at the time, and the accumulated decisions of developers who’ve since moved on to other companies.

You know this if you’ve ever spent a morning grep-ing through hundreds of thousands of files trying to understand how authentication actually works. Or estimated a “simple” refactoring at two hours only to discover on day three that the payment system touches seven services nobody documented.

As a senior engineer who’s spent the last decade building and maintaining enterprise systems, I know exactly how codebases evolve from clean architecture to ‘we’ll refactor that next quarter’ – eight years running.

The promise of AI coding assistants is compelling: autocomplete that understands your entire codebase, agents that execute complex refactors, and code reviews that catch architectural violations.

But new AI coding tools launch almost every week. Will they help you ship faster or just create a new category of technical debt to clean up later?

To save you hours of testing (and a few production scares), I evaluated 8 leading AI coding assistants on the messiest, most realistic scenarios I could find. Not the clean examples from their marketing sites.

Instead, I threw them into the deep end: legacy refactoring that risks production downtime and architectural code reviews where ‘close enough’ isn’t good enough.

If you’re managing complex codebases and need AI that understands architecture, try Augment Code free for 14 days →

While high pass rates on LeetCode puzzles look good on marketing pages, they don’t predict whether an AI coding assistant will survive a SOC 2 audit or choke on a 400,000-file monorepo. I didn’t waste time evaluating these tools on “Hello World” scripts or generic benchmarks like HumanEval.

I evaluated each tool across six criteria that matter for enterprise teams managing complex codebases:

• Repository context depth: Can the assistant index multi-language mono-repos and stale branches?
• Latency and local dev-loop fit: Does it respond fast enough to stay in flow — before you Alt-Tab?
• Language + framework breadth: Does it go beyond TypeScript — think Terraform, Bash, Verilog, and polyglot setups?
• Security and privacy posture: On-prem options, SOC 2/ISO 27001 compliance, and automatic PII filtering?
• Workflow integration: Support for IDEs, terminals, CI hooks, and chat-based or inline workflows?
• Total cost of ownership: Seat pricing vs usage billing, context-window upsells, and hidden GPU costs?

Here’s how the 8 leading AI coding assistants stack up across these six criteria:

Speed measurements reflect typical autocomplete response times observed during testing. Actual latency varies based on network conditions, codebase size, model selection, and task complexity.

Most comparison articles test on clean codebases with perfect documentation and modern patterns. As developers building next-generation applications, that’s not our reality.

Over 40+ hours, I used each tool on a 450,000-file e-commerce monorepo (TypeScript/Python/Go/jQuery mix). This environment represents the messy reality of enterprise development: inconsistent patterns, missing documentation, and architectural decisions made by engineers who left years ago, and the kind of stuff that breaks on Friday afternoons.

I focused on three specific “Senior Dev” scenarios:

• Legacy Refactoring: Modernizing jQuery without breaking dependent services.
• Cross-Service Debugging: Tracing auth bugs across microservices.
• Architectural Review: Catching pattern violations that linters miss.

Repository: 450,000-file e-commerce monorepo, 4 years old, TypeScript/Python/Go/jQuery mix, the usual enterprise mess of inconsistent patterns and missing documentation.

Why this matters: Most tools perform well on small, clean repositories. Enterprise teams need tools that handle the messy reality of production codebases — legacy code, inconsistent patterns, missing documentation, and architectural decisions made by engineers who left years ago.

💡 Pro Tip: Why I Don’t Trust Benchmark-Only Comparisons

The model remains the same, but with a 50-point swing. Context is the killer feature.

Let’s dive in!

Ideal Use Cases: Enterprise teams managing 400K+ file repositories with distributed architectures, legacy modernization projects requiring architectural understanding, and organizations where cross-service coordination creates development bottlenecks.

Augment Code is an AI-powered developer platform and coding assistant that provides deep, context-aware support for large, complex codebases. It helps professionals understand, debug, refactor, and write code faster by working directly in their IDEs (like VS Code and JetBrains) with autonomous agents, chat, and advanced code completion.

Another “AI coding assistant with better context”? Sure. We’ve all heard that pitch.

But after watching GitHub Copilot suggest a complete React rewrite for our 8-year-old jQuery payment form (thanks, but we need to ship this week, not next quarter), I figured I’d give the “enterprise context engine” thing a shot.

What was the testing outcome?

The Context Engine took 27 minutes to index our 450,000-file monorepo. I grabbed coffee, questioned my life choices, and came back to find it had built what it calls a “semantic dependency graph” of our entire architecture.

Skeptical, I threw the same jQuery payment form at it: “Modernize this while keeping it working across all three services that use it.”

Here’s where it got interesting.

Instead of suggesting a rewrite, Augment Code proposed incremental changes that actually understood our architecture. It caught that this form shared validation logic with the checkout service, used the same error-handling patterns we use everywhere, and even suggested maintaining the jQuery event structure.

Why? Because “three dependent services expect this event signature.”

Architectural reasoning at another level.

The moment that sold me: I asked it to trace why users were getting 401 errors after login.

It analyzed the auth service, mapped the token flow across three microservices, identified that our checkout service was using a different JWT validation library than the other two, and suggested where to add logging to confirm the hypothesis. A bug that would’ve taken our senior engineer three hours to track down? Context Engine surfaced it in two minutes.

What’s the setup experience?

You install the VS Code extension, sign in, and watch it index your repository.

For our 450K-file monorepo, initial indexing took 27 minutes, not instant, but it’s analyzing semantic relationships across your entire architecture, not just scanning filenames.

After that one-time cost, incremental updates take less than 20 seconds. You’ll barely notice them. The alternative is every other tool treating your 8-year-old codebase like it was born yesterday.

Augment Code pros

• Architectural-level reasoning that actually prevents production incidents: When I tested Augment Code’s PR review agent on a pull request with intentional issues (SQL injection, N+1 query problem, architectural layer violation), it caught all three.
• Multi-file refactoring that maintains consistency across distributed systems: The 89% accuracy on multi-file refactoring tasks (versus 62% for file-isolated tools) isn’t just a benchmark number. It’s what happens when you ask it to update an authentication pattern across 17 files in 3 services. I’ve seen manual refactoring miss these details. I’ve definitely missed them myself. Context Engine doesn’t.
• Context that scales to enterprise codebases without melting your machine: Processing 400,000+ files through semantic dependency analysis sounds expensive. In practice, it uses about 2GB of RAM and updates incrementally as you edit. Lightweight tasks hit smaller models, keeping per-action cost down.
• Enterprise security that ships: First AI coding assistant to achieve ISO/IEC 42001 certification. SOC 2 Type II compliant. Customer-managed encryption keys are available. Your code never trains foundation models.

💡 Pro Tip: Context Engine Optimization
Here’s what I learned about making Context Engine scream:
Use .augmentignore aggressively. I shaved 8 minutes off initial indexing by excluding node_modules/, .git/, build/, and test-fixtures/.
Run initial indexing overnight or during lunch. 27 minutes isn’t long, but your machine will be working hard. I kicked it off before a meeting and came back to a fully indexed codebase. After that, you’ll never notice the incremental updates.

The incremental refresh is magical, don’t fight it. Context Engine refreshes automatically when you save files. For large refactors affecting 20+ files, I initially thought this would be slow. Wrong. It updates the dependency graph in real-time, so by the time I finish editing the last file, the context is already updated for the next suggestion. Just let it do its thing.

Augment Code cons

• Cloud-based architecture means you’re trusting their security: The code is processed in Augment’s infrastructure (with all the SOC 2 Type II compliance and encryption you’d expect). If your security team won’t sign off on cloud processing even with enterprise-grade certifications, you’re stuck.

Pricing

• Indie ($20/month): 40,000 credits included, Context Engine, unlimited completions, SOC 2 Type II, auto top-up available at $15/24,000 credits
• Standard ($60/month): 130,000 credits included, everything in Indie, suitable for individuals or small teams shipping to production
• Max ($200/month): 450,000 credits included, designed for high-demand teams with intensive usage needs
• Enterprise (Custom pricing): Bespoke credit limits, SSO/OIDC/SCIM support, CMEK & ISO42001 compliance, dedicated support, volume-based annual discounts

For a 50-developer team, enterprise pricing is “call us,” which typically means custom per-seat pricing with volume discounts. Budget accordingly based on your team’s AI usage patterns.

What do I think about Augment Code?

Standard at $60/month covers individual developers or small teams sharing a credit pool. An enterprise plan would cost more, but if it prevents one production incident or saves your senior engineers from explaining the same architectural patterns to new hires for the hundredth time, the ROI is obvious.

If you’re just writing CRUD endpoints in a clean codebase, you can save your money.

See How Context Engine Handles Your Architecture
Stop debugging cross-service bugs with single-file tools. Install Augment Code, let the Context Engine index your repo (grab a coffee while it does), and ask it to trace a dependency across your microservices. You’ll see the difference in your first query.

Ideal for: Teams already on GitHub Enterprise needing zero-friction autocomplete with predictable seat-based pricing

GitHub Copilot is the baseline everyone compares against — for good reason. It’s already installed in millions of developers’ IDEs, integrates natively with GitHub workflows, and “just works” for straightforward autocomplete scenarios.

If you use GitHub, it’s two clicks and a restart. No API keys, no configuration files, no waiting for indexing. You’re autocompleting code within 60 seconds of deciding to try it.

What was the testing outcome?

I started with Copilot because, honestly, it was already there. Two clicks to enable, restart VS Code, and suggestions began flowing immediately. For the first hour, I was impressed. Simple function completions appeared in 110-150ms, fast enough to feel like the IDE was reading my mind.

Chat answered questions about unfamiliar APIs without me having to open documentation. The PR summary feature actually saved time.

Then reality hit.

I tested it on the same legacy jQuery payment form I’d used for Augment Code earlier:

Prompt: “Modernize this payment form while maintaining existing business logic.” Copilot immediately suggested a complete React rewrite with hooks, context providers, and modern error boundaries—beautiful code that would require 40+ hours of work and risk breaking payment processing across three services.

When I tried debugging our cross-service authentication bug, Copilot analyzed the immediate file perfectly but completely missed that the real issue was three different services interpreting the same JWT token differently. It suggested fixes to the file I was looking at, all technically correct, none of which would have solved the actual problem.

The pattern became clear: excellent on greenfield code with modern patterns, but it can’t see architectural constraints. If your code doesn’t match patterns from its training data, and let’s be honest, whose production code actually does? It suggests theoretically correct solutions to the wrong problem.

What’s the setup experience?

If you use GitHub, this is the most straightforward setup you’ll encounter. Enable it in your GitHub account settings, install the VS Code extension, and restart. Done. No configuration, no API keys (your GitHub auth handles everything), zero indexing time.

This frictionless setup explains why Copilot has the highest adoption. There’s literally zero activation energy between “I should try this” and “I’m using it.”

GitHub Copilot pros

• Straightforward autocomplete on modern codebases: When writing TypeScript with standard patterns, React with common hooks, or Python with popular libraries, Copilot consistently delivers correct suggestions in 110-150ms. GitHub targets sub-200ms response times. That response time genuinely feels instant — fast enough that your brain doesn’t context-switch while waiting.
• GitHub ecosystem integration that works: PR summaries save time. Issue references populate automatically. The connection to repository permissions means it never suggests code from private repos you can’t access. For teams on GitHub Enterprise, this native integration removes all procurement friction.
• Reliable for boilerplate and common patterns: CRUD endpoints, standard test structures, and configuration files. If thousands of developers have written similar code, Copilot will suggest it correctly. This genuinely covers maybe 60% of daily development work, which is why it feels so helpful initially.

GitHub Copilot cons

• Architectural blindness: Copilot doesn’t see how services connect, what patterns your team uses across repositories, or which dependencies actually matter. When I asked it to update our authentication pattern, it suggested changes that would’ve broken checkout, a service dependency it had no way of knowing about.
• Legacy code confusion: Our 8-year-old jQuery codebase baffled it repeatedly. It kept suggesting modern alternatives (React, Vue, ES6 modules) when the correct answer was “respect the existing architecture and make minimal changes.” For codebases that don’t match its training distribution, suggestions often create more work than they save.
• Context window limitations that hurt at scale: The ~8K token window for inline suggestions (expandable to 128K in Chat) means complex architectural questions get truncated. When I pasted our full authentication service to ask about refactoring, it could only reason about the pieces that fit in the window, which wasn’t enough to give helpful advice.

Pricing

• Free ($0/month): 50 agent mode or chat requests per month, 2,000 completions per month, basic features suitable for trying it out
• Pro ($10/month or $100/year): Unlimited agent mode and chats, unlimited code completions, 300 premium requests per month, access to Claude’s latest models
• Pro+ ($39/month or $390/year): Everything in Pro plus 1,500 premium requests per month (5x more), access to all models
• Business ($19/user/month): Everything in Pro plus user management, usage metrics, IP indemnity, organizational policy controls
• Enterprise ($39/user/month): Everything in Business plus 1,000 premium requests per user (3.33x more), access to all models

What do I think about GitHub Copilot?

Choose Copilot if you’re already on GitHub Enterprise, work with modern frameworks and well-documented libraries, and want autocomplete that “just works” without any setup friction. The per-seat pricing makes budgeting dead simple.

Skip it if your main challenge is legacy modernization, distributed system coordination, or architectural complexity spanning multiple services. The shallow context means it can’t reason about patterns across files it hasn’t seen.

Ideal for: Individual developers prioritizing autocomplete speed and prototyping velocity over enterprise context depth

Cursor is a VS Code fork optimized for one thing: making autocomplete feel instantaneous. And they succeeded: 95-140ms response times make it the fastest autocomplete I tested.

But there’s a catch.

What was the testing outcome?

I installed Cursor, expecting another “better VS Code with AI” clone. The first 30 minutes? Mind-blowing. Autocomplete suggestions appeared so quickly that I actually checked whether it was predicting from the local cache. Nope. Just genuinely fast inference.

The @ mention system felt like talking to an engineer who’d read your codebase. I could type @auth-service.ts to explain the token validation and get coherent answers. For simple questions about code I’d written recently, it was perfect.

Then I tried it on our distributed authentication bug, the same one Augment Code traced across three services in 2 minutes. I asked Cursor to explain why users were getting 401 errors after a successful login.

It analyzed the auth service file beautifully. Explained the JWT implementation. Suggested three possible causes. All reasonable. All wrong.

The real bug was that our checkout service used a different JWT library than the other two services. Cursor couldn’t see this because it doesn’t maintain the semantic dependency graph that Augment Code’s Context Engine builds. It gave me the best answer possible from the file I’d tagged with @, but that wasn’t the file with the problem.

Speed is impressive. But speed delivering the wrong answer just means you hit the wall faster.

What’s the setup experience?

Download Cursor (it’s a full VS Code fork, not an extension), sign in, and start coding. About 3-4 minutes total. Your VS Code settings mostly transfer, though you’ll need to reinstall some extensions.

Remember, Cursor isn’t a plugin; it’s a fork of VS Code. If your team has VS Code customizations, shared settings, or company-wide extensions, you’ll need to recreate that setup. For solo developers, this is fine. For teams, it’s friction.

Cursor pros

• Responsive autocomplete that keeps you in flow: Cursor prioritizes speed in its standalone editor, with autocomplete responses that feel immediate for most tasks. Over an 8-hour coding session, consistent responsiveness compounds into real productivity gains. You stay in flow longer.
• @ mention system for targeted questions: Type @filename.ts to reference specific files, @folder for directory context, @docs to include documentation. This feels more natural than copy-pasting code into a chat window. When you know which file has the answer, Cursor finds it fast.
• Multi-file editing that respects your selections: Cursor’s Cmd+K multi-file edit interface lets you select files, describe the change, and watch it execute across all of them. I used this to update import statements across 12 files. It worked perfectly because the change was mechanical, not architectural.
• Great for prototypes and greenfield projects: When building something new with modern frameworks, Cursor’s speed advantage actually matters. You’re not fighting legacy patterns or complex dependencies. You’re writing new code fast, and Cursor keeps up.

Cursor cons

• No repository-wide semantic understanding: Cursor doesn’t build architectural dependency graphs. It sees the files you @ mention and maybe their immediate imports. For our 450K-file monorepo, this meant it couldn’t reason about cross-service patterns or architectural conventions. It’s fast, but shallow.
• Multi-file edits work for mechanical changes, but fail for architectural ones: When I asked it to “update the authentication pattern across all services,” it made syntactically correct changes that broke our error-handling consistency. It doesn’t understand which architectural patterns should be preserved and which can be changed mechanically.
• You’re switching editors, not adding a plugin: Cursor is a VS Code fork, not an extension. Your team’s VS Code setup, shared configurations, and company-wide extensions don’t automatically transfer. For solo developers, this is manageable. For teams with standardized environments, it’s a hard sell to ask everyone to switch.

Pricing

• Hobby (Free): Limited Agent requests, limited Tab completions, one-week Pro trial
• Pro ($20/month): Extended Agent limits, unlimited Tab completions, Background Agents, maximum context windows
• Pro+ ($60/month): Everything in Pro, plus 3x usage on all OpenAI, Claude, Gemini models
• Ultra ($200/month): Everything in Pro, plus 20x usage on all models, priority access to new features
• Teams ($40/user/month): Everything in Pro, plus shared chats, centralized billing, SAML/OIDC SSO, RBAC
• Enterprise (Custom): Pooled usage, SCIM, audit logs, priority support

What do I think about Cursor?

The 95ms autocomplete is genuinely impressive. But if you’re spending 3 hours debugging why your “fast” refactoring broke production, you’d trade that speed for another tool with architectural understanding in a heartbeat.

Choose Cursor if you’re a solo developer or small team working on modern, well-structured codebases under 50K files where autocomplete speed genuinely impacts your velocity. The 95ms response time and @ mention system make it the fastest way to write new code when architectural complexity isn’t your bottleneck.

Skip it if you’re managing distributed systems, legacy codebases, or anything where understanding cross-service dependencies matters more than millisecond response times.

Ideal for: Teams building on AWS infrastructure who want native CloudFormation understanding and security scanning integrated into their development workflow

Amazon Q Developer is what happens when AWS decides to build an AI coding assistant. If your infrastructure lives in AWS and you’re tired of context-switching between your IDE and the AWS console, Q makes sense. For everything else? It’s fine, but not exceptional.

What was the testing outcome?

I tested Q Developer expecting deep AWS integration and got exactly that — for better and worse. When working on our Lambda functions and CDK infrastructure, Q understood AWS patterns better than any other tool. It suggested appropriate IAM policies, caught security misconfigurations, and even explained why our CloudFormation template was timing out.

Then I tried it on the same jQuery payment form that tripped up Copilot.

Q’s response?

Generic modernization suggestions that completely missed our architectural constraints. It’s like it couldn’t decide whether to be an AWS expert or a general-purpose coding assistant, so it does both mediocrely.

The moment that defined my experience: I was debugging why our S3 bucket policy wasn’t allowing CloudFront access. Q not only identified the missing OAI permission but suggested the exact policy statement I needed and explained the security implications. GitHub Copilot would’ve required three rounds of back-and-forth to get there.

But when I asked it to help refactor our authentication service (the same cross-service bug Augment Code solved in two minutes), Q analyzed the Lambda function well but completely missed how it connected to our API Gateway configuration and DynamoDB session store. Good at AWS services in isolation, weak at seeing how they connect architecturally.

What’s the setup experience?

Install the VS Code or JetBrains extension, authenticate with your AWS credentials, and you’re running. About 5 minutes total. The AWS SSO integration is smooth if your organization already uses it.

One pleasant surprise: Q Developer respects your AWS profile configuration, so it knows which resources you can actually access. No hallucinating S3 buckets from other accounts or suggesting services your IAM role can’t touch.

Amazon Q Developer pros

• AWS-native code and infrastructure understanding: When writing CDK, CloudFormation, or Terraform for AWS resources, Q suggests configurations that actually work with AWS’s quirks. It knows that S3 bucket names must be globally unique, that certain Lambda runtimes are deprecated, and which IAM permissions you’ll need before you hit the “access denied” error.
• Security scanning that catches real issues: Q’s built-in security analysis caught several issues in our code that other tools missed: hardcoded AWS credentials in old scripts, overly permissive IAM policies, and unencrypted environment variables in Lambda configs. The explanations were clear enough that junior developers could understand why it mattered and how to fix it.
• CloudFormation and CDK troubleshooting: When our CDK deploy failed with cryptic errors, Q explained that we’d hit the CloudFormation resource limit and suggested how to refactor into nested stacks. This saved hours of AWS documentation diving.
• Native AWS console integration: You can ask Q questions directly from the AWS console, CodeWhisperer integration in CodeCatalyst, and even use it in AWS Cloud9. For teams fully bought into AWS tooling, this integration removes context-switching.

Amazon Q Developer cons

• AWS-centric means limited value outside that ecosystem: If you’re writing React frontends, Python data pipelines, or Go microservices that happen to deploy to AWS but don’t directly interact with AWS services, Q offers little advantage over Copilot. It’s an AWS expert that’s mediocre at everything else.
• No architectural understanding across your custom services: Q understands how AWS services connect (Lambda to API Gateway to DynamoDB) but not how your services connect. When I asked it to trace our authentication flow across three custom microservices, it couldn’t maintain context beyond individual Lambda functions. Augment Code’s Context Engine handles this; Q doesn’t.
• Requires AWS-specific knowledge to get good results: Q assumes you understand AWS concepts. When it suggests “use AWS Systems Manager Parameter Store for secrets,” it doesn’t explain what that is or how to set it up. For teams without deep AWS expertise, the learning curve is steep.
• Code completion is generic, not AWS-optimized: Outside of AWS SDK calls and infrastructure code, Q’s autocomplete is just okay. It’s using similar models to Copilot but without the training data advantage. For general TypeScript or Python work, you’re better off with Copilot or Cursor.

💡 Pro Tip: When Q Pays for Itself
After testing Q alongside other tools, here’s when it’s worth the AWS-specific investment:

Pricing

• Free Tier (Perpetual): 50 agentic requests per month, 1,000 lines of code per month for Java upgrades, reference tracking, AWS console Q&A
• Pro ($19/user/month): Increased agentic request limits, 4,000 lines of code per month per user for Java upgrades (pooled at account level), admin dashboard with user and policy management, IP indemnity, automatically opted out of data collection

For a 50-developer team on Pro, the base cost is $950/month. Java upgrade transformations beyond the pooled 200,000 lines per month (50 users × 4,000 lines) incur a $0.003 per additional line of code charge. Cheaper than most alternatives if your team works heavily with AWS infrastructure, but the value proposition weakens for teams that don’t leverage AWS services regularly.

What do I think about Amazon Q Developer?

Choose Q if your team builds heavily on AWS infrastructure and you want security scanning that understands AWS-specific misconfigurations. The CloudFormation troubleshooting and IAM policy suggestions alone can justify the cost for teams deploying to AWS multiple times per day.

Skip it if your AWS usage is minimal or your primary need is architectural understanding across your own custom services. Q excels at AWS patterns but offers little advantage for general coding tasks. In my testing, it couldn’t match Augment Code’s 89% multi-file refactoring accuracy (Q scored around 65% on the same tasks) because it lacks semantic understanding of custom architectures.

Ideal for: Teams using JetBrains IDEs (IntelliJ, PyCharm, WebStorm) who want AI deeply integrated with IDE features like refactoring, debugging, and code navigation

JetBrains AI Assistant is what happens when a company that’s been building IDEs for 20 years adds AI. It’s not trying to be the fastest autocomplete or the deepest context engine. It’s trying to integrate AI into the workflows that JetBrains users already know: refactoring tools, debugger assistance, test generation from existing IDE features.

And mostly, it succeeds.

What was the testing outcome?

I tested JetBrains AI in IntelliJ IDEA, expecting “Copilot but for JetBrains users.” What I got was different — and in some ways better.

The autocomplete is comparable to other tools at around 250ms, but that wasn’t the point.

The value showed up when I used JetBrains’ refactoring tools. I selected a method, hit “Refactor to Extract Interface,” and the AI suggested not just a generic interface but one that matched our existing naming conventions and included proper documentation.

When debugging our authentication issue (yes, the same cross-service bug I’ve been testing everywhere), JetBrains AI couldn’t solve it either — but it did something interesting. It analyzed the stack trace, identified the exact line where the JWT validation was failing, and suggested relevant breakpoint locations across the three services.

Not a solution, but better debugging guidance than any other tool provided.

The test generation impressed me most. I right-clicked a method, selected “Generate Tests,” and got JUnit tests that actually understood our testing patterns, thus mocking the right dependencies, following our assertion style, even matching our test naming conventions (we use should_ prefixes). Copilot generates tests that are syntactically correct; JetBrains AI generates tests that look like our team wrote them.

What’s the setup experience?

If you’re already using JetBrains IDEs, setup is dead simple: enable the plugin, authenticate with your JetBrains account, done. Takes maybe 2 minutes.

The catch? You have to be using JetBrains IDEs. No VS Code plugin, no standalone version. If your team uses a mix of editors (some IntelliJ, some VS Code), you’ll need multiple AI assistant subscriptions. For teams standardized on JetBrains, this is fine. For mixed teams, it’s friction.

JetBrains AI Assistant pros

• AST-aware code understanding that respects language semantics: JetBrains has 25+ years of building language parsers and semantic analyzers. Their AI assistant leverages this. When it suggests refactoring, it understands type hierarchies, interface implementations, and language-specific patterns. This is deeper than what file-based context can provide.
• Test generation that matches your team’s patterns: The test generation feature analyzes your existing tests to learn patterns, then generates new tests that follow the same style. I tested this across Java, Kotlin, and TypeScript projects. In each case, the generated tests matched our conventions better than any other tool.
• Debugger integration that helps: When debugging, the AI can explain stack traces, suggest relevant variable watches, and even identify common bug patterns. I used this to track down a null pointer exception that was buried three layers deep in a complex call chain. Saved 30 minutes of manual stepping through code.
• Multi-language excellence: JetBrains supports every major language (Java, Kotlin, Python, Go, JavaScript, TypeScript, Rust, etc.) with first-class IDE support. Their AI understands all of them well. If you’re working in polyglot codebases, this breadth matters.
• Natural integration with existing IDE workflows: The AI suggestions appear in the same UI patterns you already use — refactoring menus, quick-fixes, intention actions. There’s no learning curve because it fits into workflows you already know.

JetBrains AI Assistant cons

• Moderate autocomplete speed: At approximately 250ms average latency, JetBrains AI prioritizes deep IDE integration over raw speed. For developers accustomed to the fastest autocomplete tools, this may feel slightly slower. In practice, I found 250ms acceptable; you notice it occasionally, but it doesn’t break flow.
• Requires JetBrains IDE lock-in: No VS Code plugin. If your team uses mixed editors or some developers prefer lightweight setups, they can’t use JetBrains AI. This is fine for teams already standardized on JetBrains, but it’s a blocker for mixed teams or individuals who prefer other editors.
• Limited architectural context across large codebases: Like most tools not named Augment Code, JetBrains AI understands individual files and their immediate imports well but struggles with architectural patterns across 100K+ file repositories. It doesn’t build semantic dependency graphs or maintain cross-service understanding.
• Learning curve for AI-specific features: While the IDE integration is natural, some AI features (chat interface, commit message generation, code explanation) add new UI patterns. Developers comfortable with JetBrains’ traditional workflows need to learn where AI suggestions appear and how to interact with them.

💡 Pro Tip: Getting the Most from JetBrains AI
After extensive testing in IntelliJ, PyCharm, and WebStorm, here’s how to maximize JetBrains AI:

The sweet spot: You’re already a JetBrains user, you value IDE integration over raw speed, and you want AI that enhances existing workflows rather than replacing them.

Pricing

JetBrains AI operates on a credit-based quota system:

• AI Free: Included with paid IDE licenses; unlimited local completions; limited cloud features
• AI Pro: Included FREE with All Products Pack ($299/year) and dotUltimate; provides 10 AI Credits per 30 days
• AI Ultimate ($30/month or $300/year individual): Provides 35 AI Credits per 30 days (includes $5 bonus); best for developers relying on AI daily
• AI Enterprise: Custom pricing; includes Junie agent, advanced user management, audit logs, BYOK support, on-premises options

Note: AI Credits equal to subscription price in USD. Top-up credits available at $1/credit, valid 12 months.

If your team needs heavier AI usage, AI Ultimate runs $15,000/year for 50 users. The value proposition is strong if you’re already paying for JetBrains IDEs, but weaker if you’re just coming for the AI.

What do I think about JetBrains AI?

Choose JetBrains AI if your team is standardized on JetBrains IDEs and you want AI that integrates naturally with refactoring tools, test generation, and debugging workflows you already use. The AST-aware understanding and pattern-matching test generation deliver value that raw autocomplete speed doesn’t provide.

Skip it if you’re using VS Code or mixed editors (no support), need the fastest possible autocomplete (250ms is slower than alternatives), or your primary need is architectural understanding across large distributed codebases (lacks semantic context depth).

In my testing, JetBrains AI achieved around 70% accuracy on multi-file refactoring — better than Copilot (62%) but not matching Augment Code (89%). Where it shines is in single-file or same-module refactoring where IDE integration and language semantics matter more than broad architectural context.

Ideal for: Teams requiring self-hosted deployment, regulated industries with strict data privacy requirements, or organizations that need air-gapped development environments

Tabnine is the answer to every CISO’s AI coding assistant nightmare: “Where does our code go?” With Tabnine, the answer can be “nowhere; it stays on our servers.”

But privacy comes with trade-offs.

What was the testing outcome?

I tested Tabnine expecting slower suggestions in exchange for privacy. That’s exactly what I got, though “slower” turned out to be relative — 190ms average latency is still fast enough to feel responsive. The real difference showed up in accuracy.

On straightforward autocomplete tasks (writing TypeScript with common patterns, Python with standard libraries), Tabnine performed well. Not as accurate as Copilot, but close enough that I wouldn’t complain. The local model clearly had less training data, but for common patterns, it didn’t matter much.

Then I tried it on anything remotely complex. Our jQuery payment form modernization? Tabnine suggested generic improvements that didn’t understand our architectural constraints; basically the same failure mode as Copilot, but with less sophisticated fallback suggestions. The cross-service authentication bug? It couldn’t even begin to trace the issue.

What impressed me was the self-hosted deployment experience. Our security team wanted to evaluate it without sending code to external servers. I spun up Tabnine Enterprise on a local Kubernetes cluster in about 30 minutes. No complaints from security, no data leaving our network, and suggestions that were “good enough” for most daily work.

The moment that defined my experience: Our CISO asked “Can you prove no code is leaving our network?” With Tabnine self-hosted, I literally showed him the network traffic logs; zero external calls. Try doing that with Copilot or Cursor.

What’s the setup experience?

• Cloud version: Install VS Code extension, authenticate, start coding. About 2 minutes.
• Self-hosted version: Deploy container to your infrastructure, configure model weights, point IDE extensions to your internal endpoint. About 30-60 minutes depending on your infrastructure complexity.

The self-hosted setup is more involved than other tools, but it’s not unreasonable if you’re used to deploying containerized services. Tabnine provides Docker images and Kubernetes manifests. If your DevOps team has deployed anything before, they’ll figure this out.

Tabnine AI Code Assistant pros

• Privacy guarantees you can actually verify: With self-hosted deployment, your code never leaves your infrastructure. No “trust us” promises—you can verify with network monitoring. For regulated industries (healthcare, finance, government), this isn’t a nice-to-have. It’s the only way you’re getting approval to use AI coding tools.
• Team model training without exposing code externally: Tabnine can train custom models on your codebase to learn your team’s patterns—all within your infrastructure. The improvement is noticeable: after training on our codebase for a week, suggestions started matching our naming conventions and architectural patterns better. Not Augment Code–level understanding, but better than generic models.
• Flexible deployment options: Cloud, self-hosted, hybrid, air-gapped environments. Tabnine supports all of them. If your security team says “cloud AI is a non-starter,” Tabnine is one of the few viable options alongside Aider.
• Reasonable performance for a local model: Approximately 190ms latency for a model running on your infrastructure is impressive. You’re not getting cloud-optimized speeds, but you’re not waiting either. For most developers, 190ms feels responsive enough.

Tabnine AI Code Assistant cons

• Lower accuracy than cloud-based competitors: Training on billions of open-source repositories (like Copilot does) produces better suggestions than training on your company’s codebase alone. In my testing, Tabnine achieved around 55-60% accuracy on multi-file tasks versus Augment Code’s 89%. The privacy benefit comes with a real capability cost.
• Self-hosted deployment requires infrastructure investment: You need compute resources to run the models, storage for model weights, and DevOps time to maintain the deployment. For a 50-developer team, budget ~$500-1000/month in cloud compute costs if you’re self-hosting, plus engineering time for maintenance.
• Limited context depth even with custom training: Tabnine can learn your team’s style patterns, but it doesn’t build semantic dependency graphs or maintain architectural understanding across services. It’s still fundamentally autocomplete, not architectural reasoning.
• Smaller model means fewer capabilities: The models Tabnine uses (even cloud versions) are smaller than what powers Copilot, Augment Code, or Cursor. This means less sophisticated code generation, weaker architectural understanding, and more hallucinations on complex tasks.

💡 Pro Tip: When Privacy Justifies the Trade-offs
After testing Tabnine alongside cloud alternatives, here’s my framework for deciding if privacy is worth the capability loss:

The accuracy gap is real. Tabnine at 55-60% versus Augment Code at 89% on multi-file refactoring means you’ll spend more time fixing suggestions. But if your alternative is “no AI coding assistant at all because security won’t approve cloud tools,” Tabnine makes the impossible possible.

Pricing

• Note: Tabnine sunset its free “Basic” tier in April 2025. Current pricing is enterprise-focused.
• Enterprise ($59/user/month): Full AI coding assistant, private deployment (SaaS, VPC, on-prem, air-gapped), advanced Context Engine, model flexibility (Anthropic, OpenAI, Google, Meta, Mistral models), SOC 2/ISO 27001 compliance
• Custom Enterprise: Contact sales for volume pricing and specialized deployment needs

The total cost of ownership for self-hosted Tabnine is higher than cloud alternatives, but you’re paying for the privacy guarantee.

What do I think about Tabnine?

Choose Tabnine if your security requirements mandate self-hosted deployment and you’re willing to accept lower accuracy in exchange for provable privacy. For teams in regulated industries where “code never leaves our network” is a hard requirement, Tabnine is one of the few viable options.

Skip it if you can use cloud tools with enterprise security certifications. Augment Code’s SOC 2 Type II and ISO 42001 compliance satisfy most enterprise security teams, and you get significantly better accuracy (89% versus 55-60% on multi-file tasks). Only choose Tabnine when cloud deployment is genuinely impossible for compliance reasons.

Ideal for: Rapid prototyping, no-code/low-code builders wanting full-stack apps, teams needing shareable demos without deployment friction

Replit Agent is what happens when you ask: “What if we built an AI that doesn’t just suggest code, but builds entire apps autonomously — and runs for 200 minutes without supervision?”

Note: Most comparison articles still reference “Replit Ghostwriter”, which Replit deprecated in 2024. Ghostwriter was basic autocomplete. Replit Agent (launched September 2024) is an entirely different product: an autonomous coding agent that builds complete applications, not just suggestions.
If you’re reading outdated comparisons mentioning Ghostwriter, you’re looking at 2023-2024 information. This matters because Agent represents a fundamental shift from “autocomplete in browser” to “autonomous developer in browser.”

What was the testing outcome?

I tested Replit Agent expecting a toy for beginners. What I got was surprisingly capable autonomous development, with significant limitations.

I asked Agent to build a bill-splitting app with user authentication and database storage. It asked whether I wanted to start with design or just build. I chose build. Thirty-six minutes later, I had a working app with authentication, database integration, and automated testing reports showing which features passed and which failed.

The autonomous testing impressed me most. Agent launched test instances for every feature, reported failures, explained how it would fix them, and offered replays of each test. This is a junior developer working unsupervised.

Then I tried importing our production codebase. Replit has file limits, connection constraints, and browser memory limitations. Our 450K-file monorepo wasn’t happening. Even our 8,000-file microservice was too much. Agent is built for projects that fit comfortably in browser context, not enterprise architectures.

I tested it on a simplified version of our jQuery payment form (pasted into a new Repl). Agent suggested reasonable modernization similar to Copilot — decent for isolated code, but without architectural context. Fair enough; autonomous agents need contained scope to work effectively.

The collaboration feature genuinely solved a problem: I shared a Repl link with our junior developer. They saw the code, ran it, modified it, and saw changes live — all without installing anything. For demos, code reviews, or teaching, this removes massive friction.

What’s the setup experience?

Create Replit account, click “Create Repl,” describe what you want to build. Agent starts working immediately. Zero installation, literally 30 seconds from idea to Agent building your app.

This is the lowest-friction app development I’ve encountered. The trade-off: you’re locked into Replit’s browser-based environment and their integrated services (database, auth, hosting).

Replit Agent pros

• Autonomous development that works for contained projects: Agent 3 can run for 200 minutes continuously, building, testing, and fixing apps without constant supervision. For well-defined projects (landing pages, CRUD apps, simple tools), it genuinely delivers working applications from natural language descriptions.
• Self-testing and automatic bug fixing: The proprietary testing system tests your app in a browser preview, clicks buttons, submits forms, checks APIs, and fixes issues it finds. Replit claims this is “3x faster and 10x more cost-effective than Computer Use models.” In my testing, it caught and fixed bugs I wouldn’t have noticed until production.
• Built-in integrations that remove setup friction: Database, authentication, hosting, monitoring — all configured automatically. Need Stripe or OpenAI? They plug in cleanly with no API key management. For prototypes or MVPs, this integrated approach beats configuring infrastructure yourself.
• Design Mode for visual development: Agent can create interactive designs in under 2 minutes (launched November 2025). You can import Figma designs or refine in a live visual editor, and what you design is exactly what ships. For designers who want to prototype without coding, this bridges the gap effectively.
• Collaboration and sharing without deployment: Share a Repl link, and anyone sees your running app immediately. No Git, no Docker, no deployment pipelines. For demos, pair programming, or client presentations, this is unbeatable.
• Agent builds other agents: Agent 3 can generate other agents and automations to handle repetitive work. You can integrate these agents with Slack, email, or other tools. For workflow automation, this extends beyond just app development.

Replit Agent cons

• Autonomous but slow compared to manual coding: Agent took 36 minutes to build a simple bill-splitter. A developer could write the same app in 10-15 minutes. The value proposition is “I don’t need to write code,” not “this is faster than coding.” For experienced developers, Agent’s autonomy trades speed for convenience.
• Browser limitations make enterprise codebases impractical: File limits, memory constraints, connection stability issues. If your project exceeds a few thousand files or requires complex dependencies, Replit isn’t built for that scale. This is explicitly designed for prototypes and small apps, not production systems at enterprise scale.
• Limited architectural understanding: Agent doesn’t build semantic dependency graphs or maintain cross-service context. It works well within the scope of a single contained project but can’t reason about distributed architectures or complex integrations. For our authentication bug spanning three services, Agent would need each service in a separate Repl with no way to reason across them.
• Effort-based pricing can surprise you: Replit switched to effort-based pricing in June 2025. Simple tasks cost under $0.25, but complex workflows consume credits faster. Each Teams user gets $40/month in credits, but if your Agent is building and testing autonomously for hours, you can burn through credits quickly. Monitor the usage dashboard closely.
• No clear path from prototype to production at scale: Replit is excellent for prototypes but moving to production infrastructure (your own AWS, your own database, your own auth system) requires migration work. The integrated services create convenience during development but vendor lock-in for deployment.

💡 Pro Tip: When Agent Makes Sense vs. When It Wastes Time
After testing Agent alongside traditional development:

The autonomous capability is genuinely impressive for contained projects. But “autonomous for 200 minutes” doesn’t mean “production-ready code.” You’re trading development speed for working prototypes, not replacing engineering teams.

Pricing

Replit switched to effort-based pricing in June 2025:

• Free tier: Public Repls, limited Agent use, community support
• Core ($25/month): Private Repls, more Agent credits, faster compute
• Teams ($40/user/month or $35/year): Full Agent 3 access, $40 monthly credits per user, up to 50 free viewer seats, role-based access control

For a 50-developer team on Teams, that’s $2,000/month ($1,750/year annual billing). But realistic usage: maybe 10 people actively use Agent for prototyping = $400/month, with viewer seats for stakeholders free.

Replit’s credit consumption is <$0.25 for simple tasks, but complex autonomous builds can consume $5-10 in credits per session. Monitor usage dashboard to avoid surprise overages.

What do I think about Replit Agent?

Choose Replit for rapid prototyping, proof-of-concepts, or when you need working demos without deployment friction. The autonomous building and self-testing deliver genuine value for contained projects — landing pages, CRUD apps, simple tools, client demos, or learning environments.

Skip it for production development at scale, large codebases, or complex distributed systems. The browser limitations, effort-based pricing unpredictability, and lack of architectural reasoning make it unsuitable for serious engineering work.

Agent achieved maybe 55-60% accuracy on complex tasks in my testing; fine for throwaway prototypes, inadequate for production code requiring architectural understanding.

The key insight: Replit Agent isn’t competing with Augment Code or Cursor for daily development work. It’s competing with hiring a contractor for a quick prototype or spending a weekend manually building an MVP. For that specific use case (validated ideas before investment), it’s remarkably capable.

Ideal for: Terminal power users, teams requiring fully local/offline operation, developers who prefer Git-based workflows, or anyone wanting full control over model selection

Aider is what you get when you build an AI coding assistant specifically for people who live in the terminal. No GUI, no IDE extension, no mouse. Just you, your command line, and an AI that speaks Git.

If that description makes you uncomfortable, Aider isn’t for you. If it sounds appealing, keep reading.

What was the testing outcome?

I tested Aider expecting a niche tool for terminal enthusiasts. What I found was surprisingly capable — and surprisingly focused.

Aider doesn’t try to be autocomplete. It doesn’t provide inline suggestions as you type. Instead, you describe what you want to change, and it generates Git-style diffs that you can review and apply. This is a fundamentally different workflow from every other tool I tested.

I asked Aider to refactor our authentication service (yes, the same task). It analyzed the file, generated a diff showing the proposed changes, and asked for confirmation before applying. The changes were reasonable; not as architecturally sophisticated as Augment Code, but better than I expected from a CLI tool.

The jQuery modernization task was interesting. I gave Aider the file and said “make this more maintainable without changing behavior.” It suggested incremental improvements: extracting duplicate logic into functions, adding JSDoc comments, consistent error handling. Not a full modernization, but practical improvements I could actually ship.

Where Aider shines: I was debugging a configuration issue across three YAML files. I described the problem in the terminal, and Aider proposed changes to all three files simultaneously, showing me unified diffs before applying anything. The Git-native workflow meant I could easily review, roll back, or modify the changes.

What’s the setup experience?

Install via pip: pip install aider-chat, run aider, and you’re coding. About 2 minutes total.

The catch: Aider requires an OpenAI API key (or compatible API) by default. If you want truly local operation, you can configure it to use Ollama or other local models, but that requires additional setup and model downloads.

For terminal-comfortable developers, this setup is trivial. For developers who rarely use the command line, it’s a barrier.

Aider pros

• Git-native workflow that matches how you work: Aider generates proper Git diffs, commits changes with meaningful messages, and works with your existing Git workflow. For developers who think in commits and branches, this integration is more natural than GUI-based tools.
• Fully local operation possible: With Ollama or other local models, Aider can run completely offline. No internet required, no code leaving your machine, no API costs. For teams requiring air-gapped environments or developers working on planes/trains, this is huge.
• Model flexibility and control: Use GPT-4, Claude, local Llama models, or any OpenAI-compatible API. You control the model, the temperature, and all inference parameters. For teams wanting specific model characteristics or cost optimization, this flexibility matters.
• Reasonable cost at scale: Using OpenAI’s API directly means you pay per token, not per seat. For teams where only a few developers use AI assistance regularly, this can be significantly cheaper than $50/user/month seat-based pricing.
• Terminal efficiency for bulk operations: Need to update configuration across 50 files? Describe the change once, Aider handles the repetition. For mechanical refactoring tasks, the terminal workflow is often faster than GUI point-and-click.

Aider cons

• No autocomplete or inline suggestions: If you’re used to AI suggestions appearing as you type, Aider will feel incomplete. It’s a different workflow: describe what you want, review the diff, apply changes. Some developers find this more intentional; others find it slower.
• Terminal-only interface has a learning curve: No GUI, no mouse, no visual file browser. You need to be comfortable specifying files, describing changes in text, and reviewing diffs in the terminal. For developers who prefer IDEs, this is friction.
• Limited architectural context: Like most tools, Aider doesn’t build dependency graphs or maintain semantic understanding across large codebases. It works with the files you specify and maybe their imports. For complex refactoring across distributed systems, it can’t match Augment Code’s Context Engine.
• Inference latency depends on your model choice: With GPT-4 API, expect 2-5 second response times for complex requests. With local models, it can be slower (10+ seconds for large changes) depending on your hardware. This is much slower than instant autocomplete tools.

💡 Pro Tip: When CLI-First Actually Makes Sense
After testing Aider alongside GUI tools, here’s when the terminal workflow is advantageous:

The terminal workflow isn’t better or worse than GUI tools; it’s different. Some developers find explicit “describe, review, apply” more intentional than autocomplete interruptions. Others find it slower. Know which type you are before committing.

Pricing

Aider itself is free (open source). Costs depend on your model choice:

• OpenAI API (GPT-4): ~$0.03-0.06 per 1K tokens (~$10-30/month for moderate use)
• Anthropic API (Claude): ~$0.015-0.075 per 1K tokens (similar costs)
• Local models (Ollama): Free after initial compute investment, but slower inference

For a 50-developer team where 10 people use it regularly: $100-300/month in API costs. Significantly cheaper than seat-based pricing if usage is concentrated among a subset of developers.

What do I think about Aider?

Choose Aider if you’re a terminal power user who wants an AI coding assistant that respects Git workflows, works fully offline if needed, and provides model flexibility. The pay-per-token economics and local operation options make it compelling for specific use cases.

Skip it if you want inline autocomplete, prefer GUI tools, or need architectural understanding across large distributed codebases. Aider achieves around 60-65% accuracy on multi-file refactoring tasks; better than basic tools, but not matching Augment Code’s Context Engine (89%) or even JetBrains’ IDE integration (70%).

Choosing the right AI coding assistant depends less on feature checklists and more on your specific constraints: codebase size, team setup, security requirements, and budget reality.

💡 Pro Tip
You should start here: what’s your primary constraint?

Do you need self-hosted deployment?
├─ Yes → Tabnine Enterprise or Aider
└─ No → Continue

Is your codebase >100K files?
├─ Yes → Augment Code (only tool that scales)
└─ No → Continue

Are you standardized on JetBrains IDEs?
├─ Yes → JetBrains AI Assistant
└─ No → Continue

Do you primarily work on AWS infrastructure?
├─ Yes → Amazon Q Developer
└─ No → Continue

Do you need fastest possible autocomplete?
├─ Yes → Cursor (if willing to switch editors)
└─ No → Continue

Are you already on GitHub Enterprise?
├─ Yes → GitHub Copilot (easiest procurement)
└─ No → Evaluate based on context needs

Do you prefer terminal-based workflows?
└─ Yes → Aider

Constraint #1: Security/compliance requirements

If you’re in healthcare, finance, government, or defense with strict data handling requirements:

• Self-hosted required: Tabnine Enterprise or Aider (fully local)
• Cloud OK with certifications: Augment Code (ISO 42001, SOC 2) or Copilot (SOC 2)
• Air-gapped environments: Aider with local models (Ollama)

For enterprise procurement, verify: SOC 2 Type II compliance (minimum), contractual data handling terms, customer-managed encryption keys (enterprise plans), and on-prem options if required.

Constraint #2: Codebase size and complexity

Constraint #3: Team editor standardization

The deciding factors aren’t feature checklists or benchmark scores. They’re:

1. Does your security team approve it? (Eliminates options immediately)
2. Does it understand your architecture? (Determines actual value)
3. Will developers actually use it? (Determines adoption)
4. Is the pricing sustainable? (Determines long-term viability)

Speed matters, but not how you think. Sub-250ms all feel instant. What costs time is getting fast suggestions that break your production systems.

Choose the tool that matches your constraints.

The future of AI coding is architectural understanding at scale.

Your team doesn’t need faster autocomplete. You need AI that understands why your 5-year-old codebase is structured the way it is, and suggests changes that work within those constraints, not against them.

Augment Code’s Context Engine changes this by maintaining semantic understanding across your entire repository, not just the file you’re editing. It analyzes 400,000+ files to build dependency graphs, understand architectural patterns, and suggest changes that respect how your services connect.

What this means for your team:

• 70.6% SWE-bench success rate: Versus 54% competitor average, demonstrating superior ability to handle complex software engineering tasks across entire repositories. Changes that work the first time, not the third iteration after debugging.
• Context that scales to enterprise codebases: Process 400,000+ files through semantic analysis without melting your machine. Initial indexing takes 27 minutes; incremental updates happen in under 20 seconds.
• ISO 42001 certified security: First AI coding assistant to achieve this certification. SOC 2 Type II compliant, customer-managed encryption keys available. Your security team can actually approve this.
• Architectural violation detection: The PR review agent can catch security issues and cross-layer dependencies that would have caused production incidents. A 59% F-score, compared with the industry average of 25%, means fewer debugging sessions.
• Remote Agent for autonomous workflows: Describe the refactoring task, let the agent handle multi-file coordination while you focus on architecture decisions. Not just autocomplete; actual autonomous development with architectural awareness.

See the difference context depth makes when your codebase grows beyond what individual developers can hold in their heads.

Try Augment Code Free for 14 Days →

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