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✓ verifiedFreemium

Serverless AI cloud for running inference, training and sandboxes on GPUs with fast cold starts and pay-per-use billing.

988K 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
27K visits/mo
Latitude logo
Latitude
✓ verifiedFreemium

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

57K 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
Pricing
Starter: $0/mo + compute ($30 free credit)
Team: $250/mo + compute
AX Free: $0/mo (25k spans/mo)
AX Pro: $50/mo (50k spans/mo)

No public pricing

No public pricing

Free: $0 (real-time guardrails)
Developer: $0.002 per trace
Core features
  • Serverless GPU compute defined in Python
  • Sub-second container cold starts
  • Autoscale 0 to 1000+ GPUs
  • Inference, training and batch workloads
  • Secure sandboxes for untrusted code
  • Built-in logging and observability
  • Agent and LLM tracing
  • Large-scale evaluations
  • Open-source Phoenix observability
  • Alyx AI engineering agent
  • OpenTelemetry-based instrumentation
  • Experiments and prompt playgrounds
  • Open-Source AI Gateway
  • Multi-LLM Management & Cost Optimization
  • Efficient and Secure LLMs Invocation
  • Unified API Signature for LLMs
  • Load Balancer for seamless switching between LLMs
  • Fine-Grained Traffic Control for LLMs
  • LLM Quota Management
  • Real-time LLM Traffic Monitoring
  • Caching Strategies for AI in Production
  • Flexible Prompt Management
  • 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
  • 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
Use cases
  • Deploying and scaling model inference
  • Fine-tuning and training models
  • Running batch/parallel AI jobs
  • Executing untrusted code in sandboxes
  • Debugging AI agents in production
  • Measuring LLM output quality
  • Catching regressions before deploy
  • Building API portals for secure sharing of internal APIs with partners.
  • Tracking API usage and driving API monetization.
  • Managing and securing API access in compliance with enterprise policies.
  • Connecting to multiple AI large models simultaneously.
  • Optimizing LLM costs and improving efficiency.
  • Protecting against LLM attacks and data leaks.
  • Monitoring AI agents in production
  • Debugging and triaging agent failures
  • Building regression evals from real traffic
  • Getting alerted on new or escalating issues
  • Monitoring production AI agents
  • Enforcing safety guardrails on LLM apps
  • Evaluating and debugging model behavior
  • Governance and compliance for enterprise AI
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