Jules by Google vs Vellum
Compare coding AI Tools
Experimental coding agent from Google that clones a repo to a secure cloud VM plans a change with Gemini executes edits runs tests and opens a review so you supervise reliable PRs end to end.
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
- Issue to PR workflow with explicit plan steps and file lists that you approve before execution and merge
- Secure cloud VM per task so no local setup and a clean environment with ephemeral resources and logs
- Deep repo understanding via Gemini planning that maps tasks to files and tests with clear acceptance checks
- Automated edits runs and test execution with visible output so reviewers trust the proposed changes
- Pull request creation with structured summary rationale and diffs to streamline team review flows
- Scoped permissions using repo tokens and granular access so risk is minimized during automated work
- 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
- Upgrade framework versions across services with reproducible steps and validation evidence for reviewers
- Apply mechanical refactors at scale such as path changes or API shifts while preserving behavior with tests
- Fix flaky test suites by instrumenting runs and proposing targeted stabilizations that ship quickly
- Generate missing documentation and examples that match code reality to reduce onboarding time
- Patch security alerts by bumping dependencies and running checks to validate the supply chain change
- Create scaffolds for small features based on an issue template that encodes acceptance criteria
- 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 senior developers DevOps and platform teams who want dependable agentic automation that produces auditable PRs under clear guardrails
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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