Observability

Compare the top LLM observability tools in 2026 by capture layer: gateway capture, SDK tracing, and OpenTelemetry libraries, with open-source options.

An AI observability platform captures traces, metrics, and cost for every LLM request and delivers them to the tools teams already run. This guide compares Bifrost, Datadog LLM Observability, New Relic AI Monitoring, Dynatrace, and Grafana Cloud on fit with an existing telemetry stack.

An AI outage is any period when an LLM provider API fails or slows enough to break your application. This guide compares five tools for monitoring LLM provider uptime, including Bifrost, Datadog, StatusGator, and Better Stack, by signal source, alerting, and automated response.

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.