Kite (Discontinued) vs Vellum
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Former AI code completion assistant for editors like VS Code and PyCharm. The company ended development and support and published a farewell note.
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
- Legacy editor plugins for popular IDEs during active years
- Local context indexing to improve token suggestions
- Early language model work for Python and more
- Documentation lookups and API hinting in editor
- Telemetry options and privacy settings historically
- Official shutdown and end of support by the founder
- 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
- Audit machines and remove old plugins to avoid confusion
- Review legacy repos created during Kite usage periods
- Educate teams on evolution toward modern copilots
- Map migration to maintained assistants with security fixes
- Discuss pricing and PMF lessons in internal tech talks
- Document editor integration approaches that worked and failed
- 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
engineering managers developer advocates and students studying the history of AI coding assistants and planning migrations to supported tools
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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