Observability

LLM observability captures prompts, responses, tokens, cost, and latency for every model call. This guide ranks five platforms for enterprises, including Bifrost, Langfuse, Arize, Datadog, and Fiddler, by how well they keep that telemetry under your control.

AI agent observability tools record the LLM calls, MCP tool calls, and sessions that agents generate in production. This guide compares Bifrost, Datadog LLM Observability, Grafana Cloud, Langfuse, and Arize Phoenix on where they capture traffic and what they can see.

Enterprise AI observability works best as a two-layer stack: a gateway that captures every model request and a backend your SREs already run. This guide compares Bifrost, Datadog, Dynatrace, Grafana Cloud, and Elastic on coverage, cost attribution, data control, and deployment.

This listicle compares Bifrost, Langfuse, Arize Phoenix, Datadog LLM Observability, and LangSmith on logs, latency breakdown, cost attribution, metrics, exports, content controls, and alerting.

TL;DR * An open source observability platform for LLM traffic is assembled from four building blocks: the OpenTelemetry Collector for pipelines, Prometheus for metrics, Grafana for dashboards, and Jaeger (or another OTLP backend) for traces. * A generic stack records HTTP status codes and latency but has no native concept of

TL;DR * An observability pipeline for LLM traffic moves telemetry through four stages: collect at the AI gateway, filter payloads and low-value spans, enrich with cost and routing context, and route each signal to the backend that can use it. * LLM telemetry breaks conventional pipelines on four axes: multi-