LLM Observability

LLM observability is the practice of monitoring, debugging, evaluating, and optimizing production AI applications. It provides visibility into prompts, responses, token usage, latency, traces, model performance, GPU utilization, and infrastructure so engineering teams can quickly identify failures, improve response quality, reduce AI costs, and maintain reliable AI applications.

Middleware detects both infrastructure and quality failures across your LLM applications. On the infrastructure side: latency spikes, token cost overruns, API errors, timeouts, and GPU resource saturation. On the quality side: hallucinations (via LLM-as-a-judge evaluation), response drift after model or prompt changes, evaluation score regressions, poor tool call behavior, and excessive token consumption per request. Unlike generic APM tools, Middleware captures these issues at the span and trace level so you can pinpoint which model call, prompt step, or tool invocation caused the failure.

Middleware integrates with any LLM provider or framework that supports OpenTelemetry, including OpenAI, Anthropic, Google Gemini, and open-source models like Llama and Mistral. Native integrations are available for OpenLIT and Traceloop, both of which follow OpenTelemetry GenAI semantic conventions. If your stack uses LangChain, LlamaIndex, or similar frameworks, you can instrument them with OTel-compatible SDKs and send traces directly to Middleware, no proprietary agent required.

Yes. The Middleware Playground lets you experiment with LLM behavior in a safe, isolated environment before deploying. You can switch between models, define output schemas to validate structured responses, add tool and function definitions to test calling behavior, and run sample prompts all without touching production infrastructure. It is designed for teams iterating on prompt engineering, model selection, or tool configuration who need fast feedback without deployment risk.

The Middleware LLM dashboard provides a real-time summary of your entire LLM application's performance across 50+ pre-built graphs. You can monitor token consumption, cost trends, error rate, LLM call volume, evaluation scores, latency percentiles, and tool usage all in one view without any manual configuration. The dashboard is available from day one and updates in real time, making it useful for both live incident triage and longer-term cost and quality trend analysis.

Middleware's LLM-as-a-judge feature lets you use a language model as the evaluator for your LLM application's outputs. You define the scope, choose the judge model, and set custom rules and acceptance criteria. Middleware then automatically scores each prompt against your configuration and flags responses that fall outside the defined criteria, giving you continuous quality monitoring without manual review.

Yes. Middleware monitors GPU hosts and devices alongside your LLM application metrics. You can track GPU utilization, memory usage, occupancy, temperature, power consumption, bandwidth, and running processes, all correlated with LLM trace data. This lets you understand the hardware cost and resource impact of each inference call without a separate GPU monitoring tool.

Yes. The Middleware Playground lets you test LLM behavior in a controlled environment before it reaches production. You can switch between different models, define output schemas to validate structured responses, add tool definitions to test function calling, and run sample prompts all without touching your production infrastructure. It's designed for teams that need to iterate on prompts and model configurations quickly.

Yes. Middleware provides observability for AI agents and agent-based workflows by capturing traces, prompt execution, tool calls, token usage, evaluations, logs, and GPU metrics. Teams can troubleshoot agent execution, optimize response quality, and monitor production AI applications from a unified platform.