8 Best LLM Monitoring & Observability Tools
A curated collection of the best observability and monitoring tools that provide visibility into LLM application behavior, performance metrics, and operational costs. These platforms offer tracing, session replay, cost tracking, and evaluation capabilities for debugging and optimizing AI agents in production.
Monitoring and observability tools provide visibility into LLM application behavior, performance, and operational costs. For AI agents, this matters because they're notoriously opaque—requests may fail silently, costs can spiral unexpectedly, and non-deterministic behavior is common. Unlike traditional application monitoring, agent observability must track token usage, model-specific performance, and the interactions between multiple LLM calls and tools, making specialized tooling essential for production deployments.
How to Choose
When selecting an observability tool for your AI agent, consider these factors:
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Integration model: Some tools (LangSmith, Langfuse) integrate deeply with specific frameworks or SDKs, while others (Helicone, Phoenix) offer lightweight one-line integrations that work framework-agnostic. Choose based on how much instrumentation control you want versus ease of setup.
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Deployment preference: If data residency or on-premise requirements are critical, Helicone, Langfuse, and Phoenix all offer open-source self-hosting options. Managed services like AgentOps and Arize handle infrastructure but require cloud access.
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Agent-specific features: AgentOps provides session replay and compliance audit trails designed specifically for multi-agent systems. General LLM observability platforms may require custom instrumentation for agent-level insights.
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Feature breadth: If you need evaluation workflows and prompt versioning alongside observability, Braintrust and LangSmith include these. If you need primarily tracing and cost tracking, Helicone and Phoenix are more focused.
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Pricing and free tier access: AgentOps publishes clear tier-based pricing. Others require visiting the website. For early-stage teams, all platforms offer free tiers, but limits vary significantly (AgentOps Basic: 5k events/month; others typically higher for development use).
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Scale and compliance: Enterprise teams requiring SLA guarantees, dedicated support, or audit trails should evaluate Arize, Braintrust, and AgentOps Enterprise plans.
Comparison
| Name | Best For | Pricing | Key Differentiator |
|---|---|---|---|
| AgentOps | Production agents, cost tracking, compliance | Free (5k events/mo), Pro ($40/mo), Enterprise | Session replay and compliance designed for agents |
| Arize AI | Enterprise teams, LLM pipelines, regression detection | See website | Enterprise-grade audit trails and systematic evaluation |
| Braintrust | Multi-model evaluation, regulated industries | See website | Integrated eval framework and prompt management |
| Helicone | Multi-provider setups, data privacy, cost tracking | Free trial, then paid | Open-source, one-line integration, strong privacy focus |
| Langfuse | Data residency, complex agent workflows | Free tier, self-hosted available | Open-source with evaluation and debugging tooling |
| LangSmith | LangChain/LangGraph users, prompt workflows | Free tier, then paid | Tight framework integration with LangChain ecosystem |
| Phoenix by Arize | RAG pipelines, open-source preference | Free cloud, self-hosted | Open-source, no feature gatekeeping, OpenTelemetry native |
| Weights & Biases | LLM training, experiment tracking | See website | Unified ML ops with LLM monitoring for research teams |
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