10 Best AI Observability Tools for Monitoring LLM Applications

Category 109: AI Observability

1. LangSmith — https://www.langchain.com/langsmith

LangSmith provides tracing, evaluation, monitoring, and debugging for LLM applications and agents.

2. Arize AI — https://arize.com/

Arize provides observability for machine-learning and generative AI systems.

3. Helicone — https://www.helicone.ai/

Helicone provides LLM observability, analytics, logging, monitoring, and cost tracking.

4. Langfuse — https://langfuse.com/

Langfuse provides open-source LLM observability, tracing, prompt management, and evaluation.

5. Portkey — https://portkey.ai/

Portkey provides an AI gateway with observability, routing, caching, reliability, and prompt-management features.

6. Weights & Biases — https://wandb.ai/

Weights & Biases provides model and application observability across machine-learning development workflows.

7. Datadog — https://www.datadoghq.com/

Datadog provides application and infrastructure observability with capabilities for monitoring AI systems.

8. New Relic — https://newrelic.com/

New Relic provides application monitoring and observability across software and AI-powered services.

9. WhyLabs — https://whylabs.ai/

WhyLabs provides AI observability and monitoring for machine-learning and generative AI systems.

10. Fiddler AI — https://www.fiddler.ai/

Fiddler provides AI observability, monitoring, explainability, and governance capabilities.

Conclusion: Production AI systems should be monitored for more than uptime; output quality, latency, cost, failures, model behavior, and data changes can all matter.

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