Know what your AI is doing. And what it costs.

When requests, model versions, and customers multiply, isolated logs stop answering useful questions. Build an analytical view of latency, errors, token usage, evaluations, and business outcomes.

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AI workloads

An analytical foundation for operating the product.

ClickHouse can support the event analysis around an AI application. The scope starts with the data and questions your team needs to investigate.

Model and agent observability

Capture request and trace identifiers, model and prompt versions, latency, errors, and relevant tool events. Make it possible to investigate a failed workflow.

A coherent event and trace model
Useful fields with controlled retention
Queries for debugging and incident review

Cost and usage analysis

Attribute token usage and spend to models, tenants, features, and environments. Make growth and unusually expensive paths visible.

Consistent usage and cost definitions
Customer and feature-level aggregation
Comparisons across releases and time

Evaluation analytics

Compare quality, latency, and cost on consistent evaluation sets. Join feedback and outcomes to the version that produced them.

Versioned evaluation records
Cohort and experiment comparisons
A record of regressions and tradeoffs

Features and analytical context

Explore aggregations of recent and historical behavior for recommendations, scoring, or retrieval context. Validate freshness and serving requirements for the particular application.

Repeatable feature definitions
Time-aware context and metadata filters
Explicit query and freshness targets

Data access and retention

Decide what needs to be collected before collecting everything. Minimize sensitive content and define who can access records and how long they remain.

Redaction or filtering before ingestion
Tenant and role boundaries
Deletion and retention design

Production capacity

Test the combined load from ingestion, investigations, dashboards, and evaluation queries. A fast demonstration needs evidence under actual operating conditions.

Realistic peak event volume
Concurrent queries and background work
A documented cost and capacity model

Published platform example

Respan: observability at 50 million events per day.

In its April 2026 user story, ClickHouse describes how Respan uses ClickHouse Cloud for LLM event ingestion and analytics, with materialized views supporting dashboard queries.

It is a useful example of the workload pattern. It is a vendor-published story, with its own architecture and operating conditions.

Read the Respan story

Questions to answer in your pilot

Can the event model explain failures? Traceability

Can tenants see only their own data? Isolation

Do peaks change query behavior? Capacity

Can you compare cost and quality? Evaluation

Set the boundary

Validate the serving path as well as the analytics.

A database choice alone does not establish retrieval quality, model quality, or application safety.

For vector retrieval

Benchmark recall, filtering, latency, memory, and update behavior on your corpus before selecting an index or replacing a specialized component.

For online decisions

Check consistency, feature freshness, tail latency, and failure handling against the application’s requirements.

For generated SQL

Use controlled permissions, query limits, and validated access patterns. Treat the application and access layer as part of the design.

Bring the AI question your dashboards cannot answer.

We can define the event model, workload, and first useful analytical view before you commit to a broader platform change.

Request a fit call