LLM Observability: Get the full picture of AI usage
Gain complete visibility into how AI is used across your organization – from shadow tools and prompts to Gateway-level traffic, performance & spend.
Observer
See how your organization uses AI
From ChatGPT in the browser to Cursor and Claude Code on the desktop, all tracked.
Explore the platform
Organisation
Your workspace, the devices it licenses, and who can reach the console.
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🔥 Early access
Observe and manage LLM requests
See usage by team, project, and user
Track requests, tokens, models, and API keys per team, user, or project.Track performance and routing efficiency
Monitor latency, errors, and throughput across models and providers.Monitor token usage and costs
Break down spend by model, application, project, team, user, API key, and individual request.Investigate any request
Use logs and traces to see tokens, latency, routing decisions, cache behavior, costs, and errors behind any request.Business benefits of
AI observability
AI observability isn’t just for technical teams. It’s a business-critical layer that gives security, finance, and visibility into how AI is used, what it costs, and where issues or risks emerge.
Reduce risk exposure
Spot policy violations, unusual usage, and potential risks with visibility into AI activity across your organization.
Control costs across your stack
See exactly what AI is costing you, by team, tool, and project, and cut unnecessary spend wherever it happens.
Prove compliance, build trust
Show stakeholders and regulators how AI is being used, and what protections are in place. Logs and reports make your policies verifiable, not just theoretical.
Prove AI adoption and ROI
See which teams are actually using AI day to day, not just who has a license, so you can prove your rollout is working.
What happens without full AI observability
When you can’t see how AI is being used, you can’t control the risks it creates. Blind spots in your AI stack and infrastructure create serious
FAQ
Full stack visibility: From user prompts to model responses, every interaction should be logged and traceable.
Chain and agent-level tracing: Understand how multi-agent workflows and chained prompts perform.
Data protection and guardrails: Protect sensitive inputs and outputs in real time.
Scalability and integration: Ensure the solution works across all your apps, models, and teams.
User and admin access: Provide the right level of visibility to both technical teams and business users.
LLM monitoring focuses on surface-level metrics like uptime, error rates, and latency. It alerts you when something goes wrong, but it rarely provides context about why it happened or how to fix it. Monitoring helps with incident detection, but not deep diagnostics.
LLM observability, on the other hand, gives you full visibility into the entire lifecycle of every model interaction — from the user’s input to the model’s response, and all the steps in between. It provides traces, logs, token-level insights, and usage patterns. Observability helps you understand behavior and ensure compliance across teams.