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Latitude logo
Latitude
✓ verifiedFreemium

Open-source AI-agent observability platform for tracing sessions, clustering failures and running evals on live traffic.

57K 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
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
DeepSeek logo
DeepSeek
✓ verifiedFreemium

Chinese AI lab DeepSeek offering free chat apps and low-cost API access to its frontier V-series and R-series reasoning models.

430M visits/mo
Pricing

No public pricing

AX Free: $0/mo (25k spans/mo)
AX Pro: $50/mo (50k spans/mo)
Developer: $0 (10K events/month, up to 5 users, 30-day retention)

No public pricing

Core features
  • 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
  • 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
  • Free DeepSeek chat (web and app)
  • Open API platform
  • V-series and R-series reasoning models
  • DeepSeek-V4 with long context and stronger agent ability
  • OpenAI/Anthropic-compatible API
  • Extensive published model lineup
Use cases
  • 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
  • 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
  • Free AI chat and assistance
  • Building apps via API
  • Reasoning and coding tasks
  • Low-cost LLM inference
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