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.

OpenRouter logo
OpenRouter
✓ verifiedFreemium

Unified API gateway that routes requests to 400+ LLMs across 70+ providers with failover and no subscription.

17M visits/mo
Abacus.AI logo
Abacus.AI
✓ verifiedPaid

AI super-assistant plus enterprise ML platform: ChatLLM for teams and end-to-end model building for enterprises; broad, pricing not shown.

4.3M 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
Helicone logo
Helicone
✓ verifiedFreemium

LLM observability platform and AI gateway that lets teams route, log, debug and analyze their model requests.

100K visits/mo
Pricing
Free: $0
Pay-as-you-go: Per-token, no subscription
Enterprise: Talk to sales

No public pricing

AX Free: $0/mo (25k spans/mo)
AX Pro: $50/mo (50k spans/mo)
Hobby: Free (10,000 requests/mo)
Pro: $79/mo (unlimited seats)
Team: $799/mo (SOC-2 & HIPAA)

Free trial available

Core features
  • One unified, OpenAI-compatible API for 400+ models
  • Automatic provider failover for higher uptime
  • Edge routing for low latency
  • Custom data and provider policies
  • Pay-as-you-go credits usable across any model
  • ChatLLM access to multiple top AI models
  • AI agents and automation
  • No-code full-stack app creation
  • Enterprise generative AI platform
  • Structured ML model building
  • Optimization and forecasting
  • Agent and LLM tracing
  • Large-scale evaluations
  • Open-source Phoenix observability
  • Alyx AI engineering agent
  • OpenTelemetry-based instrumentation
  • Experiments and prompt playgrounds
  • Request logging and LLM observability
  • AI gateway with routing and automatic fallbacks
  • Caching and rate limiting
  • Session, user and custom-property analytics
  • Prompts, playground and datasets for testing
  • Integrations with OpenAI, Anthropic, Azure and more
Use cases
  • Accessing many LLMs through one integration
  • Adding provider redundancy to AI apps
  • Comparing model price and performance
  • Powering agents and AI-native products
  • Chat with many AI models in one place
  • Build and deploy ML models
  • Automate tasks with AI agents
  • Debugging AI agents in production
  • Measuring LLM output quality
  • Catching regressions before deploy
  • Monitoring and debugging LLM apps
  • Analyzing model usage and cost
  • Caching responses to cut spend
  • Managing prompts and testing datasets
Visit
More in LLM Ops Observability