11 Best AI Coding Tools for Data Science & ML in 2025

Context window size determines which AI coding tools actually work in production versus which ones just demo well. When debugging spans multiple repositories, 200k context processing versus 64k is the difference between architectural understanding and expensive autocomplete.

Most programmers think AI coding assistants are about typing less code, but the real test comes when you're debugging a microservices issue where the original developer left two years ago. A junior developer can write pandas transformations after a few tutorials, but understanding how UserService.validateAuth() affects your entire ML pipeline? That architectural knowledge takes months to develop, and most AI tools have the memory span of that junior developer.

Engineering teams now manage 50-500 repositories with 60% of original authors gone. AI coding assistants will hit $47.3B by 2034, but Forrester warns that "GenAI reality bites back for software developers" when tools hit real complexity. The kind where ML pipelines process multi-GB parquet files across distributed systems while handling PII in regulated industries.

Academic studies confirm productivity gains for data science teams, but only when AI tools understand complete workflows rather than isolated code snippets.

The following analysis covers 11 tools with their best use cases, core features, honest pros and cons, and current pricing so you can evaluate your options and make the best choice for your specific needs.

1. Augment Code

Best Use Case: Ideal for professional software engineers working with large, complex codebases who need an AI assistant that deeply understands their entire project structure and can perform end-to-end tasks autonomously.

Augment Code processes 200k tokens at once — that's your entire codebase, not just the file you're editing. It uses Proof-of-Possession API verification so your code never leaves your infrastructure. The agents can actually open pull requests for you instead of just suggesting code snippets. Latency can be noticeable on massive codebases, but most teams find the architectural understanding worth the wait.

Core Features:

• Industry-leading context engine that indexes and understands entire codebases up to 200,000 tokens
• Autonomous AI agents that can plan, build, and open PRs for complete feature implementation
• Multi-modal inputs supporting screenshots, Figma files, and wireframes for additional context
• Advanced memory system that learns coding patterns and preferences across conversations
• Terminal integration where agents can run commands with approval or automatically

Pros:

• Superior codebase understanding compared to competitors
• Ranked #1 on SWE-Bench Verified benchmark (65.4% score)
• Strong performance with large codebases (handles 3,000+ line files effortlessly)
• Advanced context retrieval with persistent memories

Cons:

• Higher cost compared to alternatives ($20-$250/month vs competitors)
• Recent pricing changes have frustrated some users (67% increase from $30 to $50)
• Smaller user community compared to established tools
• Limited language support compared to competitors

Pricing:

• Community: Free (50 user messages/month)
• Indie: $20/month (125 user messages, solo developers only)
• Developer: $50/month (600 user messages, team management)
• Pro: $100/month (1,500 user messages)
• Max: $250/month (4,500 user messages)
• Enterprise: Custom pricing with unlimited messages

2. GitHub Copilot

Best Use Case: Perfect for developers seeking a well-established AI pair programmer that integrates seamlessly with popular IDEs and offers reliable code completion and chat assistance.

GitHub Copilot is the default choice most developers start with. It works great for traditional software development but struggles with data science notebooks where context spans multiple cells. Jupyter support requires community workarounds that break regularly. The 64k context window hits limits fast when you're debugging across multiple services.

Core Features:

• Real-time code completion that suggests whole lines or entire functions as you type
• Multi-model access to GPT-4.1, Claude Sonnet 4, Claude Opus 4, Gemini 2.0 Flash, and more
• Code review capabilities with automated pull request reviews and suggestions
• Extensive IDE support with native integration across VS Code, Visual Studio, JetBrains, Xcode
• Agent mode that can make sweeping changes across multiple files with analysis and validation

Pros:

• World's most widely adopted AI developer tool with millions of users
• Excellent documentation and community support
• Strong product direction and continuous improvement
• IP indemnification for business users

Cons:

• Limited codebase context compared to specialized tools like Augment Code
• Can struggle with complex project architectures
• Sometimes loses context in large, multi-file applications
• Suggestions may occasionally match public code

Pricing:

• Free: $0 (2,000 completions + 50 chat requests/month)
• Pro: $10/month or $100/year (unlimited completions/chats)
• Pro+: $39/month or $390/year (all models, 30x more premium requests)
• Business: $19/user/month (team management)
• Enterprise: $39/user/month (advanced features)

3. Amazon Q Developer

Best Use Case: Best suited for development teams already working within the AWS ecosystem who need integrated cloud resource management, cost optimization, and development assistance.

Amazon Q Developer makes sense if you live in AWS. It generates boto3 code faster than you can Google the docs and understands your SageMaker pipelines. The moment you need GCP or Azure integration, you're on your own. The free tier is generous, but the AWS lock-in is real. If you're multi-cloud or cloud-agnostic, look elsewhere.

Core Features:

• AWS-native integration with expert knowledge of AWS services, billing, and architecture patterns
• Agentic coding capabilities for autonomous task completion including testing and documentation
• Code transformation agents for automated Java upgrades and .NET porting from Windows to Linux
• Security scanning with built-in vulnerability detection and remediation suggestions
• Multi-environment support across IDEs, CLI, AWS Console, Microsoft Teams, and Slack

Pros:

• Deep AWS integration and expertise for cloud-native development
• Strong performance on SWE-Bench Leaderboard
• Comprehensive free tier with perpetual access
• Built-in compliance features for regulated industries

Cons:

• Heavily AWS-focused, limiting usefulness for non-AWS projects
• Accuracy issues reported by users leading to misinformation
• Limited language support compared to GitHub Copilot
• Inconsistent results requiring detailed prompts for accuracy

Pricing:

• Free Tier: $0 (50 agentic requests/month, perpetual)
• Pro Tier: $19/user/month (unlimited agentic requests, 4,000 LOC transformations/month)

4. Tabnine

Best Use Case: Ideal for professional developers and teams who prioritize code privacy and security, especially those working in regulated industries or large enterprises that need AI assistance without compromising sensitive code repositories.

Tabnine solves the "code can't leave the building" problem with air-gapped deployments that run entirely on your infrastructure. You'll need serious hardware though — 4x L40S GPUs for small teams, and 10x H100 GPUs for enterprise scale. So this isn't a software purchase, it's an infrastructure project. The privacy comes at a steep cost, but it's often the only option for regulated industries.

Core Features:

• AI Code Completion with context-aware suggestions using hybrid universal and personalized AI models
• Team Learning Algorithm that creates private AI models trained exclusively on your team's codebase
• Multi-Language Support covering 80+ programming languages including Python, JavaScript, Java, C++, Go
• IDE Integration with native support for VS Code, IntelliJ, WebStorm, PyCharm, Android Studio
• Privacy-First Architecture with zero data retention policy and option for local-only processing

Pros:

• Strong privacy controls with local and private model options
• Learns from your specific codebase for personalized suggestions
• Excellent multi-language support across 80+ languages
• Fast and responsive code completions

Cons:

• Higher pricing compared to some competitors ($39/month for Enterprise)
• Setup can be more complex than alternatives
• Some users report occasional performance issues and inaccurate suggestions
• Limited free tier functionality compared to paid versions

Pricing:

• Basic (Free): Limited features, basic code completion
• Dev Plan: $9/user/month (AI chat, personalized agents, basic admin tools)
• Enterprise Plan: $39/user/month (private deployment, fine-tuned models, advanced governance)

5. Google Gemini Code Assist

Best Use Case: Best suited for teams already using Google Cloud services or Google Workspace, particularly those needing comprehensive AI assistance across the entire development lifecycle from coding to deployment and operations.

Google Gemini Code Assist integrates well if you're already using Google Cloud and Colab. The free tier gives you 180k completions monthly, which is generous. TensorFlow support is solid, but PySpark capabilities remain limited. Your code gets processed in Google's cloud, so check your compliance requirements.

Core Features:

• Multi-Modal AI Assistant using Gemini 2.5 model for code completion, generation, and conversational assistance
• Agent Mode with multi-step collaborative reasoning for complex, multi-file coding tasks
• Local Codebase Awareness leveraging large context window for deep understanding of project structure
• Google Cloud Integration with native support for BigQuery, Firebase, Cloud Run, and Apigee
• Enterprise Security with enterprise-grade security, data governance, and IP indemnification

Pros:

• Free tier for individuals with 180,000 monthly code completions
• Excellent integration with Google Cloud ecosystem
• Agent mode for complex multi-step tasks
• Strong enterprise security and compliance features

Cons:

• Limited to Google ecosystem integration
• Higher enterprise pricing
• Some users report accuracy issues and slow performance
• Relatively new tool with evolving feature set

Pricing:

• Individual Edition: Free (180,000 code completions/month)
• Standard Edition: Monthly: $22.80/user/month | Annual: $19/user/month
• Enterprise Edition: Monthly: $54/user/month | Annual: $45/user/month

6. Databricks Assistant

Best Use Case: Perfect for data scientists, ML engineers, and analytics teams already using Databricks who need AI assistance specifically for data science workflows, SQL queries, and big data processing tasks.

Databricks Assistant is completely free and built specifically for Spark workflows. It understands your Unity Catalog schemas and can debug Spark execution plans. The catch is it only works inside Databricks notebooks. Need help with local development or non-Spark tasks? Then this is the wrong tool for you.

Core Features:

• Data-Aware Context powered by Databricks Intelligence Engine with knowledge of Unity Catalog schemas
• Multi-Language Support generating SQL, Python, and R code optimized for Databricks environment
• Code Explanation & Debugging that explains complex queries and identifies/fixes errors in data processing
• Notebook Integration natively built into Databricks notebooks, SQL editor, and file editor
• Data Visualization capabilities that can create visualizations and dashboards with natural language prompts

Pros:

• Completely free with all Databricks subscriptions
• Deep integration with Unity Catalog for context-aware suggestions
• Optimized specifically for data science and analytics workflows
• Can handle complex big data processing tasks

Cons:

• Limited to Databricks environment only
• Not suitable for general software development
• Requires existing Databricks subscription and knowledge
• Focused only on data/ML use cases, not web development or general coding

Pricing: Completely free — available at no additional cost to all Databricks customers across all tiers

7. IBM watsonx Code Assistant

Best Use Case: Ideal for enterprise developers focused on legacy system modernization (especially COBOL to Java) and IT automation tasks, particularly those requiring high levels of security, compliance, and IP protection in regulated environments.

IBM watsonx Code Assistant focuses heavily on COBOL-to-Java migrations and enterprise compliance. If you're modernizing mainframe applications, it might be useful. For modern ML development, the tooling feels dated and the community is nearly nonexistent. Plus, pricing starts at $3,000/month, which reflects its enterprise-only positioning.

Core Features:

• AI-powered code generation with IBM Granite models and IP indemnification
• Legacy code modernization with COBOL to Java translation and mainframe application support
• Context-aware autocomplete with real-time similarity checks and transparency features
• Code explanation and documentation generation with enterprise-grade security
• Multi-language support for Python, Java, C, C++, Go, JavaScript, TypeScript, and more

Pros:

• Enterprise-grade security with IP indemnity protection
• Excellent for legacy system modernization (especially COBOL/mainframe)
• Hybrid deployment options (cloud, on-premises, hybrid)
• Transparent code origin tracking and compliance features

Cons:

• Limited language ecosystem compared to competitors
• Interface feels clunky compared to modern alternatives
• Higher cost barrier for smaller teams
• Less broad community support than general-purpose tools

Pricing:

• Essentials Plan: Starting at $2 per 20 task prompts
• Standard Plan: $3,000/month (includes ~3,000 task prompts for unlimited users)
• Additional usage: ~$2 per extra 20 task prompts
• 30-day free trial available

8. Cursor IDE

Best Use Case: Perfect for developers who want a seamless AI-first coding experience with advanced context awareness and multi-file editing capabilities, especially those working on complex projects requiring frequent AI assistance.

Cursor is basically VS Code with better AI integration. The Composer mode handles multi-file changes well, and it supports multiple frontier models. The credit-based pricing gets expensive fast if you use it heavily. Enterprise documentation is sparse, which might be a problem for larger teams.

Core Features:

• Advanced AI autocomplete with multi-line predictions and context from entire codebase
• Composer mode for multi-file code generation and project-wide changes
• AI chat with codebase awareness supporting multiple frontier models (Claude Sonnet 4, GPT-4.1, Gemini 2.5 Pro)
• Built-in background agents for autonomous coding tasks and bug fixes
• VS Code compatibility with full extension ecosystem support

Pros:

• Exceptional context awareness across large codebases
• Seamless VS Code-like interface with familiar workflows
• Support for multiple cutting-edge AI models
• Powerful Composer mode for complex multi-file operations

Cons:

• Expensive pricing can escalate quickly with heavy usage
• Complex credit system that's difficult to predict costs
• Limited features when using own API keys
• Linux support requires manual installation workarounds

Pricing:

• Hobby Plan: Free (limited completions, 50 requests/month)
• Pro Plan: $20/month (500 fast requests, unlimited completions)
• Pro+ Plan: $60/month (increased quotas, ~675 Sonnet 4 requests)
• Ultra Plan: $200/month (power users, ~4,500 Sonnet 4 requests)
• Teams Plan: $40/user/month (500 requests per user)
• Enterprise: Custom pricing

9. JetBrains AI Assistant

Best Use Case: Best suited for developers already using JetBrains IDEs who want integrated AI assistance without switching tools, particularly for Java, Kotlin, and other JetBrains-supported language development.

JetBrains AI Assistant works if you're already using their IDEs and don't want to switch tools. The Jupyter integration is native, unlike most competitors. However, users report frequent performance issues and frustration with the tool auto-installing without permission. Credit-based pricing also makes costs unpredictable.

Core Features:

• Seamless IDE integration across all JetBrains IDEs (IntelliJ IDEA, PyCharm, WebStorm, etc.)
• Intelligent code completion with context-aware suggestions
• AI chat with project context supporting multiple models (GPT-5, Claude 4, Gemini 2.5 Pro)
• Automated testing and refactoring with AI-generated unit tests
• Junie coding agent for autonomous task execution (in development)

Pros:

• Deep integration with JetBrains IDE ecosystem
• Familiar interface for existing JetBrains users
• Strong code analysis and refactoring capabilities
• Free tier available with IDE licenses

Cons:

• Poor marketplace rating due to performance issues
• Limited third-party model provider support
• Noticeable latency and frequent delays reported
• Auto-installation without permission frustrates users

Pricing:

• AI Free: Included with IDE licenses (unlimited code completion, local models, limited cloud credits)
• AI Pro: Credit-based system (~$10-15/month estimated, exact pricing varies by usage)
• AI Ultimate: Higher credit allowance (~$20-30/month estimated)
• AI Enterprise: Custom pricing with BYOK options

10. Replit AI

Best Use Case: Ideal for beginners, students, and solo developers who need a cloud-based coding environment with AI assistance for rapid prototyping and collaborative development. Perfect for those who want to build web applications quickly without complex setup requirements.

Replit AI runs entirely in your browser with zero setup required. Great for prototyping and collaboration, but the AI often produces low-quality code. The new effort-based pricing makes costs unpredictable. Enterprise security documentation doesn't exist, so this is really for personal projects and education.

Core Features:

• Replit Agent with AI-powered code generation and application building using natural language prompts
• Real-time collaboration with multi-user coding, voice chat, and cursor tracking
• Instant deployment with one-click hosting and deployment for web apps and APIs
• 50+ language support for coding in Python, JavaScript, Java, C++, and many other languages directly in browser
• AI-powered debugging and code completion with context-aware suggestions using Claude Sonnet 4 & OpenAI GPT-4o

Pros:

• Easy setup with zero installation required; works entirely in browser
• Excellent real-time collaboration features for teams and classrooms
• Time-saving AI features for rapid prototyping and development
• User-friendly interface particularly appealing to beginners

Cons:

• Expensive AI usage with new effort-based pricing model
• Poor coding quality and reliability issues reported by users
• Slow performance, especially on mobile devices like iPads
• AI often fails to follow instructions precisely and generates redundant changes

Pricing:

• Starter: FREE (10 development apps public only, Replit Agent trial, 2 GiB storage)
• Replit Core: $20/month annual or $25/month (unlimited apps, $25 monthly credits, full AI access)
• Teams: $35/user/month annual (everything in Core, $40 monthly credits per user, 50 viewer seats)
• Enterprise: Custom pricing (minimum 20 users)

11. DeepCode AI by Snyk

Best Use Case: Ideal for development teams and security-conscious organizations needing comprehensive application security scanning with AI-powered vulnerability detection and automated fixes throughout the software development lifecycle.

DeepCode AI by Snyk scans for security vulnerabilities, not general coding help. It finds real issues and suggests fixes, but warns the fixes might break your application. The tool uses 8 years of security-specific AI training to achieve 80% accuracy in automated fixes. Think of it as a security complement to your main coding assistant, not a replacement.

Core Features:

• AI-powered static analysis using DeepCode AI engine for real-time vulnerability detection with minimal false positives
• Automated security fixes with Snyk Agent Fix providing up to 5 AI-generated fix suggestions with one-click application
• Multi-language support covering 19+ programming languages with comprehensive vulnerability database
• IDE integration working directly in popular IDEs with real-time scanning and inline fix suggestions
• SDLC integration with seamless CI/CD pipelines, GitHub, GitLab, and other development tools

Pros:

• Highly accurate vulnerability detection with comprehensive security database
• Easy setup and integration with existing development workflows
• Excellent CI/CD pipeline integration and automated scanning capabilities
• Strong remediation guidance with actionable fix suggestions

Cons:

• Can be expensive, especially for smaller teams requiring enterprise features
• Occasional false positives and missed genuine vulnerabilities reported
• Aggressive sales team and sometimes unresponsive customer support
• Limited language support for some technologies (e.g., GoLang coverage not as comprehensive)

Pricing:

• Free: $0 (unlimited contributing developers, limited tests per product)
• Team: $25/month per contributing developer (minimum 5 developers, 1000 tests per product)
• Enterprise: Custom pricing (unlimited tests, advanced analytics, SSO/SAML, custom rules)

Top Dealbreakers for AI Coding Tools

Three factors separate useful AI coding tools from expensive autocomplete:

1. Context Architecture: Most tools handle simple autocomplete, but complex system analysis requires architectural understanding. Context window evolution shows convergence around 64k-200k tokens, with 200k representing the current high-end for enterprise analysis. The difference matters when debugging spans multiple repositories.
2. Enterprise Security: SOC 2 certification is table stakes, but implementation varies significantly. Proof-of-Possession verification, where code never leaves your infrastructure, becomes critical for regulated industries handling sensitive data.
3. Data Science Workflow Support: Jupyter notebook context persistence, pandas DataFrame transformations across cells, and TensorFlow model architecture understanding aren't edge cases — they're daily requirements. Research shows productivity gains when AI tools are designed for data science workflows rather than adapted from general coding.

Understanding these dealbreakers before evaluating tools will save you from purchasing solutions that look impressive in demos but fail on the complexity that matters most to your specific workflows.

How to Evaluate AI Coding Tools Beyond Vendor Demos

Vendor demos showcase simple scenarios, but production environments demand more rigorous evaluation. Test AI coding tools with these three specific scenarios that reveal architectural understanding capabilities:

1. Multi-Repository Debugging: Pick a bug spanning three repositories in your ML pipeline and track which tools maintain context across the debugging session. Tools that lose context mid-investigation will leave you manually connecting dots between services.
2. Notebook Context Persistence: Create a feature engineering notebook with 20+ cells, then ask for optimization suggestions for cell 15. Check whether the tool remembers data schema definitions from cell 3. Most tools fail this test because they don't understand notebook execution flow.
3. Legacy Integration Understanding: Test an ML model that integrates with legacy microservices written by developers who left the team. See which tools understand existing patterns and suggest implementations that match established architectural decisions rather than introducing inconsistencies.

Tools that pass these tests handle real production work. The ones that don't are expensive autocomplete with good marketing.

Choose the Best AI Coding Tool for Data Science

The AI coding assistant market is dividing into two distinct categories: pattern-matching tools and system-understanding tools. Context window size has become the defining limitation that determines which category a tool falls into and what kinds of problems it can actually solve.

Pattern-matching tools excel at common programming tasks: writing standard functions, fixing syntax errors, generating boilerplate code. They work well for individual developers working on self-contained features. System-understanding tools can navigate architectural complexity, maintain context across multiple services, and suggest solutions that align with existing patterns.

Most teams discover this distinction only after purchasing tools that looked impressive in demos but failed on real complexity. As codebases grow larger and more distributed, the context limitation becomes the primary factor determining tool effectiveness.

Multi-agent development and enterprise adoption are accelerating, but the fundamental constraint remains: you can't understand what you can't remember. The solution isn't better algorithms — it's bigger context windows and smarter context selection.

The tools getting this architectural understanding right today will define how distributed systems get built tomorrow. Teams that recognize this early will build better systems faster than those still debugging with expensive autocomplete.

Ready to test context-aware AI coding? If your team manages complex codebases with multiple repositories, try Augment Code's 200k context processing with their free Community plan. See how architectural understanding compares to pattern matching on your actual debugging scenarios by starting a free trial today — no demos, just real work on your real codebase.