LLM Observability

OpManager Nexus gives you a live view across every model running in production; latency, token usage, cost per request, and error rates, all in one dashboard. When something shifts, you see it before your users do.

LLM observability dashboard showing latency, token usage, and cost per model

Feature overview

How much do you actually know about what your LLMs are doing in production right now? Most teams know a model is deployed. Few know why costs spiked overnight, which requests are failing, or whether the model is responding the way it should. OpManager Nexus gives your team that visibility across every model, so problems get caught before users do.

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Total visibility

Monitor every model from a single dashboard

Your team shouldn't need three tools to understand how your AI models are performing. Everything that matters is in one place.

  • Stop finding out about cost spikes after the fact. Real-time charts show exactly where spend is moving before it becomes a problem.
  • Know which models are slowing down before users complain. Latency charts across all active models update continuously.
  • Avoid token limit surprises. Usage broken down by model tells you which workloads are consuming the most.
Single dashboard showing cost, latency, and token usage across every active LLM
Prompt analysis view showing input and output content, cost, and latency per LLM call
Pinpoint issues

Inspect every LLM call your application makes

When a model behaves unexpectedly, your team needs to see exactly what was sent and what came back, without manual log digging.

  • Stop guessing which requests are costing the most. Search across thousands of calls by model, service, or prompt content in seconds.
  • Catch unexpected model behavior before it affects users. Full input and output content per call lets you trace issues to the exact request.
  • Know which requests are worth optimizing. Cost and latency shown per prompt make the biggest gains obvious.
Investigate fast

Query your LLM logs the way you query anything else

When something goes wrong, your team needs answers fast. LLM logs are fully structured and queryable, so any request is retrievable in seconds.

  • Stop piecing together what happened from fragmented sources. Every call is logged with timestamp, model, token breakdown, and message content in one view.
  • Spot traffic surges before they become incidents. The log event chart gives your team an instant read on request patterns.
  • Reduce unnecessary token spend. Cache token visibility shows what's being reused versus regenerated so you can optimize with real data.
Queryable LLM log view with event chart, token breakdown, and message content

FAQ

What is LLM observability, and why do production teams need it?

LLM observability is the practice of monitoring how LLMs behave in production, tracking token usage, latency, cost, and error rates across every request. Without it, cost spikes go unnoticed and slow responses reach users before your team can fix them. OpManager Nexus brings all of this information into one platform alongside your existing infrastructure monitoring.

Does OpManager Nexus show cost and token usage per individual LLM request?

Yes. The Prompt Analysis view breaks down every call with token count (input and output), cost, latency, and the full prompt and response content. You can search and filter across thousands of requests by model, service, or prompt content to find exactly what you're looking for.

Which LLM providers does OpManager Nexus support for observability?

OpManager Nexus supports multiple providers, including OpenAI, Anthropic, and others via AWS Bedrock. You can monitor models from different providers simultaneously from the same dashboard, with per-model cost and performance breakdowns.

Can we query LLM logs the same way we query application logs?

Yes. The Logs tab supports query syntax so your team can filter by model, token count, timestamp, prompt content, or message fields. Results are returned instantly with a visual event chart showing request volume over time.

We use multiple Claude models in production. Can we track them separately?

Yes. Every chart and table in the dashboard breaks down by model, so you can compare response time, cost, token usage, and error rates across different models side by side. Cost by model and input or output token usage by model are both available as dedicated charts.

Do you know what AI is doing in your IT stack right now?

Get complete visibility into every LLM running in your stack today. Every hour without it is an hour your team is flying blind on cost, performance, and model behavior. Start your free trial and see everything in minutes.

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