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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
Agenta logo
Agenta
✓ verifiedFreemium

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

34K visits/mo
Openlayer logo
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
honeyhive.ai logo
honeyhive.ai
✓ verifiedFreemium

Observability and evaluation platform for production LLM agents, built on OpenTelemetry for tracing, monitoring and testing.

24K visits/mo
Pricing
Developer: $0 (3 seats, 10k logs/mo)
Professional: $29/seat/mo (100k logs/mo)
Business: $49/seat/mo (500k logs/mo)

Free trial available

No public pricing

Basic: Free (20k inferences/mo, 1 member, 5 projects)
Developer: $0 (10K events/month, up to 5 users, 30-day retention)
Core features
  • 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
  • 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
  • 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
  • OpenTelemetry-native distributed tracing across 100+ LLMs and frameworks
  • Online evaluation via LLM-as-a-judge or code
  • Offline experiments and regression detection
  • Annotation queues for expert review
  • Alerts and drift detection
  • Prompt management, CLI and docs MCP server
Use cases
  • Testing and comparing prompts and models
  • Evaluating and simulating AI agents
  • Monitoring agents in production
  • Running human evaluation pipelines
  • Version and manage prompts centrally
  • Benchmark and evaluate LLM outputs
  • Debug and trace production LLM issues
  • Collaborate across a team on LLM apps
  • Evaluate models before production
  • Monitor live AI systems for issues
  • Prevent unsafe or non-compliant outputs
  • Catch data drift and quality problems
  • Debugging multi-agent systems
  • Monitoring live agent quality at scale
  • Catching regressions before release
  • Human review of edge cases
  • Aligning automated evaluators with domain experts
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