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
Open-source AI gateway giving dev teams unified access, fallbacks and spend tracking across 100+ LLMs.
Open-source LLMOps platform uniting prompt management, evaluation and observability for teams shipping reliable LLM apps.
LLM observability platform and AI gateway that lets teams route, log, debug and analyze their model requests.
Open-source AI-agent observability platform for tracing sessions, clustering failures and running evals on live traffic.
AI observability and evaluation platform to trace, evaluate and improve LLM agents in production, with an open-source Phoenix core.
Free trial available
No public pricing
Free trial available
No public pricing
- ✦Unified access to 100+ LLMs in OpenAI format
- ✦Cost/spend tracking per key, user and team
- ✦Budgets and rate limiting
- ✦Automatic provider fallbacks and retries
- ✦Virtual keys and team management
- ✦Logging and observability integrations
- ✦Prompt management as a single source of truth
- ✦Playground for prompt experimentation
- ✦Evaluation to measure changes before production
- ✦Observability and tracing for debugging
- ✦Collaboration across technical and non-technical roles
- ✦Open-source and self-hostable
- ✦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
- ✦Agent trace capture and conversation intelligence
- ✦Semantic and exact-text search across all traces
- ✦Automatic issue discovery with Slack/email/webhook alerts
- ✦OpenTelemetry-compatible SDK with no lock-in
- ✦Automated evals and golden dataset generation
- ✦Failure-mode clustering and MCP server integration
- ✦Agent and LLM tracing
- ✦Large-scale evaluations
- ✦Open-source Phoenix observability
- ✦Alyx AI engineering agent
- ✦OpenTelemetry-based instrumentation
- ✦Experiments and prompt playgrounds
- →Giving developers governed access to many LLMs
- →Attributing and controlling LLM spend
- →Keeping apps running during provider outages
- →Version and manage prompts centrally
- →Benchmark and evaluate LLM outputs
- →Debug and trace production LLM issues
- →Collaborate across a team on LLM apps
- →Monitoring and debugging LLM apps
- →Analyzing model usage and cost
- →Caching responses to cut spend
- →Managing prompts and testing datasets
- →Monitoring AI agents in production
- →Debugging and triaging agent failures
- →Building regression evals from real traffic
- →Getting alerted on new or escalating issues
- →Debugging AI agents in production
- →Measuring LLM output quality
- →Catching regressions before deploy