JetBrains AI Assistant vs Vellum
Compare coding AI Tools
JetBrains AI Assistant adds context-aware code completion, AI chat, and code generation inside JetBrains IDEs, using JetBrains models such as Mellum plus optional cloud features, with an AI Free tier that includes unlimited completion and limited cloud credits.
Vellum is an AI agent building platform that combines a prompt playground, evaluation tools, and hosted agent apps so teams can iterate on LLM workflows with debugging and knowledge base support, starting with a free tier and upgrading for more credits.
Feature Tags Comparison
Key Features
- Unlimited code completion: Mellum powered suggestions inside the IDE for fast autocomplete that adapts to your project context
- Cloud completion mode: Autocomplete lines blocks and whole functions using AI Assistant when cloud credits are available
- Context-aware AI chat: Ask questions about code and get answers that reflect open files and the current project structure
- Commit message help: Generate or refine commit messages using IDE context so summaries stay aligned with the actual diff
- Test generation support: Produce starter unit tests and edge cases from selected code to speed coverage and reduce regressions
- Local AI support: AI Free includes local model support so some assistance can run without sending code to cloud services
- Free and Pro plans: Pricing starts at $0 with 50 credits and Pro at $25 with 200 builder credits so solo builders can scale testing
- Prompt playground: Compare models side by side and iterate prompts systematically instead of relying on subjective testing
- Evaluations framework: Run repeatable quality tests at scale to detect regressions and track improvements across prompt versions
- Hosted agent apps: Share working agents with teammates through hosted apps for demos
- reviews
- and stakeholder feedback cycles
Use Cases
- Inline coding flow: Accept completions for repetitive patterns so you can focus on domain logic and architecture decisions
- Refactor support: Ask the chat to explain a class or function then implement changes with IDE refactor tools and verify quickly
- Test coverage boost: Generate starter tests for critical paths then adjust assertions to match your business rules and data shapes
- Commit hygiene: Draft concise commit messages that reflect the actual diff and reduce review friction in pull requests during merges
- Learning a codebase: Use chat with open file context to understand unfamiliar modules and navigation paths faster during onboarding
- Bug fixing: Request a minimal change suggestion then step through in debugger to confirm behavior matches the intent in production
- Agent prototyping: Build an agent by chatting with AI then refine logic with low code steps and controlled prompt versions
- Prompt iteration: Compare LLM outputs side by side and select prompts that improve accuracy and reduce unwanted variation
- Regression testing: Run evaluations on a saved dataset before release to catch quality drops after model or prompt changes
- RAG apps: Attach a knowledge base and test retrieval behavior with representative questions and strict document scope rules
- Stakeholder demos: Publish hosted agent apps so product and compliance reviewers can test behavior without local setup steps
- Model selection: Evaluate providers and self hosted options with the same tasks to choose the best cost and latency mix for production
Perfect For
software developers, Java and Kotlin engineers, Python developers, data scientists using JetBrains IDEs, teams needing IDE native assistance, tech leads managing code quality and onboarding
product managers, ML engineers, software engineers, data scientists, AI platform teams, prompt engineers, QA and reliability teams, startups building LLM features, teams shipping agent workflows
Capabilities
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