Free AI Code Generators That Are Worth Using in 2025
Free AI code generators can speed up scaffolding, but they must be used with strong guardrails. This guide covers the free options worth trying in 2025, how to keep output secure, and where research helpers like Google Antigravity add value without touching your codebase. Use Propel Code to review and enforce policies on every generated change so free tooling cannot bypass standards. For broader stacks, see our guides on AI dev tools and review automation.
TL;DR
- Free generators are best for boilerplate and test scaffolding.
- Never trust output without linting, tests, and review.
- Keep secrets out of prompts; disable training where possible.
- Use Antigravity for research context, not code ingestion.
- Upgrade when rate limits or compliance requirements block progress.
Free options that are usable
Cursor and Windsurf offer limited daily credits that work for small tasks. Open model playgrounds can draft snippets, but you must filter out unsafe patterns. Local model runtimes are improving and avoid data egress, though quality varies.
Security and governance
Enforce secret scanning and linting in CI so any generated code is checked automatically. Keep a short register of which tools are allowed and what data can be shared with them.
Pairing with research assistants
Antigravity can summarize docs, standards, and API references so you avoid pasting code into prompts. Treat it as a research companion that feeds requirements to your generator, not as a code writer itself.
When to move to paid tiers
- You need SSO, audit logs, and guaranteed data controls.
- Daily quotas slow your team or block CI usage.
- You want model selection, custom policies, and org-wide prompt libraries.
Safe usage checklist
- Run lint, type checks, and tests on every AI generated change.
- Keep generated code behind human review with Propel enforcing policies.
- Restrict prompts to non-sensitive context.
- Log tool usage and rotate tokens regularly.
FAQ
Can I use free generators in CI?
Most free tiers limit CI usage. Use them locally for scaffolding, then rely on tests and review in CI. For CI generation, move to paid tiers with org policies.
How do I avoid licensing issues?
Keep provenance logs and prefer models that avoid training on restrictive code. Review outputs for copied snippets and rely on internal templates where possible.
When you are ready to enforce review quality on every pull request, add Propel to GitHub. It pairs well with AI generators by keeping policy and security checks consistent.
Guardrails that catch common failures
- License: scan outputs for copied headers and incompatible licenses.
- Security: run secret scanning and SAST on every AI-generated change.
- Quality: require tests for new logic paths; reject diff-only code without coverage.
- Performance: set budget checks (bundle size, query count) in CI for frontend/backends.
Prompt patterns that reduce risk
Use short, specific prompts that include acceptance criteria and testing requirements. Instead of asking for a full feature, ask for a test plus a minimal function, then iterate. Keep prompts free of secrets and use placeholders for tokens and URLs.
CI usage considerations
- Avoid running free generators in CI due to rate limits; keep generation local.
- Run lint, type checks, SAST, and tests in CI to gate merges regardless of origin.
- Log provenance: note in PR descriptions when code was generated to aid review.
Sources and further reading
- OWASP Top 10 as a baseline for reviewing AI generated code safely.
- GitHub secret scanning to keep credentials out of prompts and commits.
- GNU license list to double-check license compatibility when reusing generated snippets.
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