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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liteLLM
✓ verifiedFreemium
Open-source AI gateway giving dev teams unified access, fallbacks and spend tracking across 100+ LLMs.
703K visits/mo
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Arize AI
✓ verifiedFreemium
AI observability and evaluation platform to trace, evaluate and improve LLM agents in production, with an open-source Phoenix core.
248K visits/mo
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metaflow.org
✓ verifiedFree
Open-source Python framework, born at Netflix, for building, scaling, and deploying real-world ML, AI, and data science workflows.
20K visits/mo
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Agenta
✓ verifiedFreemium
Open-source LLMOps platform uniting prompt management, evaluation and observability for teams shipping reliable LLM apps.
34K visits/mo
Pricing
Open Source: $0 (self-hosted, 100+ providers)
Free trial available
AX Free: $0/mo (25k spans/mo)
AX Pro: $50/mo (50k spans/mo)
No public pricing
No public pricing
Core features
- ✦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
- ✦Agent and LLM tracing
- ✦Large-scale evaluations
- ✦Open-source Phoenix observability
- ✦Alyx AI engineering agent
- ✦OpenTelemetry-based instrumentation
- ✦Experiments and prompt playgrounds
- ✦Plain-Python workflow orchestration
- ✦Automatic versioning and experiment tracking
- ✦Scale-out compute with GPUs and parallel instances
- ✦One-command deployment to production
- ✦Runs on AWS, Azure, GCP, or Kubernetes
- ✦Event-based triggering of workflows
- ✦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
Use cases
- →Giving developers governed access to many LLMs
- →Attributing and controlling LLM spend
- →Keeping apps running during provider outages
- →Debugging AI agents in production
- →Measuring LLM output quality
- →Catching regressions before deploy
- →Developing and debugging ML pipelines locally
- →Scaling model training to cloud GPUs
- →Deploying experiments to production unchanged
- →Building reactive, event-driven data systems
- →Version and manage prompts centrally
- →Benchmark and evaluate LLM outputs
- →Debug and trace production LLM issues
- →Collaborate across a team on LLM apps
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