--- title: "Enterprise AI Observability: Choosing an AI Observability Platform That AI, SRE and Security Teams Can All Use" published: false description: "A selection guide for enterprise AI observability platforms, built around what AI, SRE and security teams each need and how to test candidates against a real scenario." tags: observability, ai, devops, platformengineering --- An AI observability platform is the system that lets a team see how its AI applications behave in production, including model calls, retrieval, agent steps, cost and the services underneath them. In an enterprise, the hard part of choosing one is rarely the feature list. It is that several teams will depend on the answer, and each of them is asking a different question.
Imagine a selection review with three people in the room: an engineer from the AI team, a site reliability engineer, and someone from security. The discussion tends to go in circles unless the group agrees on what each person needs to be able to do afterwards.
Start with what each person must be able to do
The AI engineer needs to answer: which model call or tool step caused this response to be wrong, and did it get worse after the last change? That requires visibility into prompts, responses, retrieval and agent steps, plus a way to compare versions.
The SRE needs to answer: why is this slow or failing, and which team owns the fix? That requires the AI application to be connected to the services and infrastructure it calls, along with alerting and fault localization that work across both.
The security reviewer needs to answer: what sensitive data did we collect, how was it protected, and who touched it? That requires masking, access control and an audit trail of which identity called which tool, along with deployment options that match data requirements.
If a candidate platform lets only one of these three people complete their task, the decision is not finished.
Four screening questions
Before any demo, these four questions filter most candidates:
- Does it cover the kinds of AI applications we run, both LLM applications and agents?
- Can it follow one request from the application through model calls and tool calls?
- Does it connect AI applications with the underlying IT environment instead of treating them separately?
- Does it support analysis and response, such as anomaly analysis, fault localization and assistance with operations?
Test with a scenario, not a spec sheet
Pick a realistic scenario, such as an AI feature that slows down after a release, and run it past all three people on each candidate. The question is simple: can each of them reach their own answer from the same trace? A platform that satisfies the AI team but sends the SRE to a second tool has moved the problem rather than solved it.
Why the shared record matters
Different tools produce different partial truths. When AI, operations and security each use a different view, a disagreement about the cause of an incident is often really a disagreement about which data to trust. A shared trace turns that into a discussion about the same evidence. It is also what agentic AI operations depends on: agents that help with diagnosis can only be trusted if they can point to telemetry that people can verify.
Warning signs during evaluation
- AI telemetry lives apart from application and infrastructure telemetry
- Instrumentation cannot be exported or standardized, which makes later migration costly
- Prompts and responses are stored without configurable masking
- Sampling drops the failures you most need to investigate
- Data volume costs cannot be predicted as usage grows
Some vendors now package AI observability with application and infrastructure monitoring in a single platform. Bonree ONE is one example of that approach.
Quick answers
Can a general observability tool cover AI applications?
Partly. It covers services and infrastructure, but model calls, prompts, retrieval, agent steps and token cost need to be captured and tied back to the rest of the stack.
Who should make the decision?
A small group with AI, operations and security representation, so that the platform works for everyone who will depend on it.
