Codeium vs Vellum
Compare coding AI Tools
Free AI coding toolkit with autocomplete chat and refactor inside popular IDEs plus an optional Windsurf editor for agentic coding and larger contexts.
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
- Autocomplete in IDE with multi line suggestions and local awareness
- Chat to explain errors generate tests and draft refactors inline
- Repo search to find symbols usages and similar implementations
- Lightweight install for VS Code JetBrains and other editors
- Privacy controls with clear docs for local context handling
- Enterprise options for policy controls and deployment flexibility
- 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
- Speed feature work with context aware suggestions inside IDE
- Explain unfamiliar code paths and propose refactors during reviews
- Search large repos to map usages before risky edits
- Generate unit tests and scaffolds that match local patterns
- Fix build breaks by asking chat to trace the failing step
- Prep interviews and katas with quick hints in side panel
- 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
individual developers startup teams platform engineers and enterprise shops that want free autocomplete plus optional agent workflows in a dedicated editor
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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