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Enterprise AI observability and security platform to monitor, evaluate, and govern agentic and ML systems with guardrails.
Free comparison tool for LLM API prices across providers, with a calculator to estimate token costs.
AI observability and evaluation platform to trace, evaluate and improve LLM agents in production, with an open-source Phoenix core.
Open-source Python framework, born at Netflix, for building, scaling, and deploying real-world ML, AI, and data science workflows.
Platform to test, evaluate and observe LLM and voice AI agents, with prompt management and red-teaming for production.
No public pricing
No public pricing
- ✦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
- ✦Compare LLM API prices across providers
- ✦Per-million-token input/output rates
- ✦Quality and context-window data
- ✦Token cost calculator
- ✦Sortable, searchable model table
- ✦Agent and LLM tracing
- ✦Large-scale evaluations
- ✦Open-source Phoenix observability
- ✦Alyx AI engineering agent
- ✦OpenTelemetry-based instrumentation
- ✦Experiments and prompt playgrounds
- ✦Plain-Python workflow orchestration
- ✦Automatic versioning and experiment tracking
- ✦Scale-out compute with GPUs and parallel instances
- ✦One-command deployment to production
- ✦Runs on AWS, Azure, GCP, or Kubernetes
- ✦Event-based triggering of workflows
- ✦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
- →Monitoring production AI agents
- →Enforcing safety guardrails on LLM apps
- →Evaluating and debugging model behavior
- →Governance and compliance for enterprise AI
- →Compare LLM API costs
- →Estimate token spending for a project
- →Pick a cost-effective model
- →Debugging AI agents in production
- →Measuring LLM output quality
- →Catching regressions before deploy
- →Developing and debugging ML pipelines locally
- →Scaling model training to cloud GPUs
- →Deploying experiments to production unchanged
- →Building reactive, event-driven data systems
- →Catch agent issues before production
- →Evaluate and monitor LLM quality
- →Test voice AI agents at scale