Compare tools
Side-by-side features, use cases and pricing — because the right pick depends on your job and budget, not just the ranking.
⇄ Comparison dimension — pick the market you're actually shopping in
✕
Helicone
✓ verifiedFreemium
LLM observability platform and AI gateway that lets teams route, log, debug and analyze their model requests.
100K visits/mo
✕
Openlayer
✓ verifiedFreemium
AI governance and observability platform with 100+ automated tests and real-time guardrails to evaluate and monitor ML/LLM systems.
24K visits/mo
✕
Kiro AI
✓ verifiedFreemium
Kiro is a spec-driven agentic coding tool for IDE, CLI and web that turns prompts into specs and catches bugs with property-based tests.
3.8M visits/mo
Pricing
Hobby: Free (10,000 requests/mo)
Pro: $79/mo (unlimited seats)
Team: $799/mo (SOC-2 & HIPAA)
Free trial available
Basic: Free (20k inferences/mo, 1 member, 5 projects)
Free: $0/mo (50 credits)
Pro: $20/user/mo (1,000 credits)
Pro+: $40/user/mo (2,000 credits)
Pro Max: $100/user/mo (5,000 credits)
Power: $200/user/mo (10,000 credits)
Core features
- ✦Request logging and LLM observability
- ✦AI gateway with routing and automatic fallbacks
- ✦Caching and rate limiting
- ✦Session, user and custom-property analytics
- ✦Prompts, playground and datasets for testing
- ✦Integrations with OpenAI, Anthropic, Azure and more
- ✦100+ automated AI tests
- ✦Offline evaluation and CI/CD for AI
- ✦Real-time observability and tracing
- ✦Guardrails against PII leaks, injection, hallucination
- ✦Data-quality and drift monitoring
- ✦Compliance/governance alignment
- ✦Git, SDK, CLI and REST API integration
- ✦Spec-driven development (requirements, design, tasks)
- ✦Parallel agents, local or cloud
- ✦Property-based and correctness testing
- ✦Works in IDE, CLI, web and mobile
- ✦Multiple models (Claude, open-weight, Auto)
- ✦Headless CLI for CI/CD
- ✦Context from tools like Figma and Terraform
Use cases
- →Monitoring and debugging LLM apps
- →Analyzing model usage and cost
- →Caching responses to cut spend
- →Managing prompts and testing datasets
- →Evaluate models before production
- →Monitor live AI systems for issues
- →Prevent unsafe or non-compliant outputs
- →Catch data drift and quality problems
- →Turning prompts into maintainable, spec-matched code
- →Catching bugs unit tests miss
- →Reviewing PRs and fixing bugs in CI/CD
Visit