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liteLLM logo
liteLLM
✓ verifiedFreemium

Open-source AI gateway giving dev teams unified access, fallbacks and spend tracking across 100+ LLMs.

703K visits/mo
Agenta logo
Agenta
✓ verifiedFreemium

Open-source LLMOps platform uniting prompt management, evaluation and observability for teams shipping reliable LLM apps.

34K visits/mo
Arize AI logo
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
Maxim AI logo
Maxim AI
✓ verifiedFreemium

End-to-end evaluation and observability platform for building, testing, and monitoring AI agents and LLM apps.

102K visits/mo
Pricing
Open Source: $0 (self-hosted, 100+ providers)

Free trial available

No public pricing

AX Free: $0/mo (25k spans/mo)
AX Pro: $50/mo (50k spans/mo)
Developer: $0 (3 seats, 10k logs/mo)
Professional: $29/seat/mo (100k logs/mo)
Business: $49/seat/mo (500k logs/mo)

Free trial available

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
  • 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
  • Agent and LLM tracing
  • Large-scale evaluations
  • Open-source Phoenix observability
  • Alyx AI engineering agent
  • OpenTelemetry-based instrumentation
  • Experiments and prompt playgrounds
  • Prompt IDE, versioning, and deployment
  • Agent simulation and evaluation
  • Production tracing and observability
  • Pre-built and custom evaluators
  • Human-in-the-loop evaluation
  • Bifrost LLM gateway
Use cases
  • 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
  • Debugging AI agents in production
  • Measuring LLM output quality
  • Catching regressions before deploy
  • Testing and comparing prompts and models
  • Evaluating and simulating AI agents
  • Monitoring agents in production
  • Running human evaluation pipelines
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