LLM Observability solution for your business
Gain complete visibility into AI usage across your organization with nexos.ai—track every token and output, whether in Workspace chats or via API integrations.
- Achieve complete insight into how LLMs are used
- Capture records of every LLM query and response for in-depth analysis
- Maintain control with dashboards that track usage patterns and costs
What is LLM observability?
Track every prompt, response, and model interaction
Monitor usage and costs in real time
Enforce accountability with full audit trails
How nexos.ai provides your business with full LLM observability
nexos.ai makes observability part of your AI architecture, not an afterthought. You get insights at every layer, from API traffic to user-level behavior.
Business benefits of LLM observability
finance, and operations the clarity they need to move forward with confidence.
Control costs across your stack
Track real-time usage and apply team-level budgets. Avoid runaway spending, even when using multiple models or providers.
Reduce risk exposure
Catch policy violations and prevent data leaks before they escalate. Full visibility makes it easier to spot weak points and take action.
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.
AI risks with LLM tools you business can’t afford to ignore
When you can’t see how AI is being used, you can’t control the risks it creates. Blind spots in your AI stack create serious LLM challenges — from data leaks to runaway costs:
Without AI security, businesses expose themselves to more than just technical debt. They put their customers, reputation, and operations on the line.
See how nexos.ai makes AI observability simple
Be one of the first to experience the all-in-one AI platform that puts LLM visibility, security, and control at the center of your AI strategy.
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, fallback events, and usage patterns. Observability helps you understand behavior and ensure compliance across teams.