AI teams · Telemetry

See every call. Trace every chain. Replay any request.

Apinizer's AI lane joins prompts, completions, MCP calls, and A2A conversations into one Elasticsearch index — and one audit ledger. Cost spikes, latency tail, and guardrail hits all live on the same dashboards.

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AI observability — For AI teams use case overview from Apinizer. — For AI teams · AI observability

The problem

AI observability is usually a notebook on someone's laptop.

The model team has a spreadsheet of prompt samples. The platform team has Prometheus. Compliance has nothing. When 'what did this cost us on Tuesday' becomes a question, three tools disagree. Apinizer puts every AI event — prompt, completion, tool call, agent conversation — in one place, alongside cost and audit. The Tuesday question becomes one query.

Capabilities

What Apinizer does here

Per-call telemetry

Provider, model, input / output / cached tokens, cost breakdown, time-to-first-token, time-per-output-token, guardrail and PII flags, failover origin, trace id — 28 AI fields captured per call.

Trace, timeline, replay

Turn on live trace for a proxy and watch calls as they happen. Chained calls form one trace tree; a timeline view shows where the time went, and a captured request can be replayed on the worker to reproduce it.

Cost + latency on one view

The two numbers every AI decision balances — one dashboard, joined by call, broken out eight ways: person, group, OU, application, model, project, deployment, and team.

Forensic chains

Reconstruct any agent chain end-to-end — including MCP invocations and A2A hops. The query joins prompts to tool calls to completions to user-facing responses.

Anomaly + severity-aware alarms

EMA + Bollinger bands on AI metrics. P1 to on-call when latency tail blows; P3 to digest when miss rate creeps.

Joins to the audit ledger

Every AI event sits alongside the audit ledger, and the same data exports natively over OTLP with OpenTelemetry GenAI semantics, a Prometheus metric family, and an importable Grafana dashboard. Regulator questions about AI decisions resolve as a saved query, not a forensic project.

Use cases

In production, this looks like…

Banking
Istanbul bank traces a contested chatbot answer to the exact prompt
Customer complaint cited a specific answer; audit query returned the prompt, model, route decision, and tool calls in seconds. Response: one paragraph, end of week.
Insurance
Munich insurer catches a quiet spend climb on a Tuesday morning
Token spend per virtual consumer broke the EMA upper band; the anomaly alarm fired before the daily budget tripped. Team found a retry storm in one client, capped it, returned to baseline.
Caught in <30 min
Public sector
Paris agency proves an AI decision did not depend on personal data
Audit query: 'what context entered the model for this citizen's case'. Result: no PII; the redaction firewall did its job. Complaint closed with evidence.
Telecom
Madrid carrier joins prompt cost to support-ticket NPS
Tickets resolved by AI tagged with their prompt cost; cost per NPS point computed. Finance sees the unit economics in their own dashboard.
Media
Milan publisher reproduces a bad answer instead of guessing at it
Editor flags a completion; the team opens the captured trace, reads the exact prompt and retrieved RAG context, then replays the request on the worker to reproduce it against a different model.
Retail
Amsterdam marketplace reconstructs an agent chain after a partner complaint
The chain hit MCP → A2A → MCP → API. The audit query returned each leg in order; root cause posted to the partner within a day.
Healthcare
Prague hospital alarms on latency tail in clinical chatbot
P99 latency drifted from 2.4s to 3.9s overnight. Anomaly alarm fired; root cause was a provider degradation; routing rolled to secondary in 90 seconds.
Energy
Baku utility separates ops-agent telemetry from analytics-agent telemetry
Operations have stricter alarms; analytics have looser ones. Same observability surface, different policies per agent class.