toolspool

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

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
Fiddler AI logo
Fiddler AI
✓ verifiedFreemium

Enterprise AI observability and security platform to monitor, evaluate, and govern agentic and ML systems with guardrails.

51K visits/mo
LangWatch logo
LangWatch
✓ verifiedFreemium

Platform to test, evaluate and observe LLM and voice AI agents, with prompt management and red-teaming for production.

23K visits/mo6.4K saves
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
AX Free: $0/mo (25k spans/mo)
AX Pro: $50/mo (50k spans/mo)
Free: $0 (real-time guardrails)
Developer: $0.002 per trace
Developer: €0 (50k events/mo)
Growth: €29/core-seat/mo (+ €5 per 100k events)
Developer: $0 (10K events/month, up to 5 users, 30-day retention)
Core features
  • Agent and LLM tracing
  • Large-scale evaluations
  • Open-source Phoenix observability
  • Alyx AI engineering agent
  • OpenTelemetry-based instrumentation
  • Experiments and prompt playgrounds
  • End-to-end agentic and ML observability
  • Real-time guardrails (hallucination, PII, jailbreak)
  • Continuous evaluations and custom judges
  • Root-cause analysis and decision lineage
  • AI governance, risk, and compliance controls
  • Flexible SaaS, VPC, or on-prem deployment
  • Scenario-based agent testing
  • LLM evaluation and quality scoring
  • Observability for cost and latency
  • Prompt management with GitHub sync
  • Voice AI simulation
  • LLM red-teaming and governance
  • 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
  • Debugging AI agents in production
  • Measuring LLM output quality
  • Catching regressions before deploy
  • Monitoring production AI agents
  • Enforcing safety guardrails on LLM apps
  • Evaluating and debugging model behavior
  • Governance and compliance for enterprise AI
  • Catch agent issues before production
  • Evaluate and monitor LLM quality
  • Test voice AI agents at scale
  • 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
Visit
More in LLM Ops Observability