LangWatch

As LLM-powered applications grow in complexityspanning multi-step agent workflows, RAG pipelines, and tool-calling sequencestraditional monitoring tools fall short. LangWatch was purpose-built to fill this gap, providing the evaluation infrastructure needed to ship AI features with confidence.

The platform's core observability layer enables teams to search and inspect every LLM interaction in real time, across all environments. Engineers can debug failures, investigate incidents, trace individual requests through multi-step agent pipelines, and maintain complete audit trails for compliance purposes.

This deep visibility is particularly valuable for teams building autonomous agents where understanding exactly what the model decided and why is critical to diagnosing unexpected behavior.

Cost visibility is a first-class feature in LangWatch. The platform monitors input and output token usage across 800+ models and providers, attributing costs by tagwhether that means per team, per feature, per user, or per experiment.

Trend dashboards make it easy to spot cost anomalies before they become expensive, and the per-provider breakdown helps teams make informed decisions about model selection and routing strategies.

LangWatch provides a complete prompt and model management layer with version control, A/B testing, and feature-flag-style deployment controls. Teams can compare prompt variants, run controlled experiments, and roll back changes safelywith full audit trails logging every modification.

The platform also integrates DSPy optimization, enabling automatic prompt improvement through systematic experimentation rather than manual prompt engineering iterations.