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

liteLLM logo
liteLLM
✓ verifiedFreemium

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

703K 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
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
portkey.ai logo
portkey.ai
✓ verified

AI gateway and observability suite for governing and optimizing LLM apps; strong dev-tool traffic.

266K visits/mo
Pricing
Open Source: $0 (self-hosted, 100+ providers)

Free trial available

Developer: $0 (10K events/month, up to 5 users, 30-day retention)
Basic: Free (20k inferences/mo, 1 member, 5 projects)

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
  • 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
  • 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
  • AI Gateway for reliable LLM routing
  • Prompt Engineering for collaborative prompt management
  • Guardrails for enforcing reliable LLM behavior
  • Observability Suite for monitoring costs, quality, and latency
  • MCP Client for building AI agents with real-world tool access
Use cases
  • Giving developers governed access to many LLMs
  • Attributing and controlling LLM spend
  • Keeping apps running during provider outages
  • 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
  • Evaluate models before production
  • Monitor live AI systems for issues
  • Prevent unsafe or non-compliant outputs
  • Catch data drift and quality problems
  • Monitor costs, quality, and latency of AI applications.
  • Route to 250+ LLMs reliably with a single endpoint.
  • Streamline and scale prompt engineering.
  • Enforce reliable LLM behavior with guardrails.
  • Build agents with access to real-world tools.
Visit
More in LLM Ops Observability