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
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Helicone
✓ verifiedFreemium
LLM observability platform and AI gateway that lets teams route, log, debug and analyze their model requests.
100K visits/mo
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Openlit
✓ verifiedFreemium
Open-source, OpenTelemetry-native platform for LLM observability, tracing, evaluation and prompt management.
9.1K visits/mo
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portkey.ai
✓ verified
AI gateway and observability suite for governing and optimizing LLM apps; strong dev-tool traffic.
266K visits/mo
Pricing
Hobby: Free (10,000 requests/mo)
Pro: $79/mo (unlimited seats)
Team: $799/mo (SOC-2 & HIPAA)
Free trial available
Self-Hosted: $0 (Apache 2.0, no usage limits)
No public pricing
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
- ✦OpenTelemetry-native distributed tracing
- ✦Token usage and cost tracking
- ✦LLM evaluations (online/offline)
- ✦Prompt management and versioning
- ✦GPU and vector-DB monitoring
- ✦60+ LLM/framework integrations
- ✦Self-hostable via Docker; export to Grafana/Datadog
- ✦AI Gateway for reliable LLM routing
- ✦Prompt Engineering for collaborative prompt management
- ✦Guardrails for enforcing reliable LLM behavior
- ✦Observability Suite for monitoring costs, quality, and latency
- ✦MCP Client for building AI agents with real-world tool access
Use cases
- →Monitoring and debugging LLM apps
- →Analyzing model usage and cost
- →Caching responses to cut spend
- →Managing prompts and testing datasets
- →Trace and debug LLM applications
- →Monitor AI cost and performance
- →Evaluate prompts and models
- →Add observability without code changes
- →Monitor costs, quality, and latency of AI applications.
- →Route to 250+ LLMs reliably with a single endpoint.
- →Streamline and scale prompt engineering.
- →Enforce reliable LLM behavior with guardrails.
- →Build agents with access to real-world tools.
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