Most teams log AI traffic the way they'd log any API call: timestamp, status code, latency, error rate. Aggregate dashboards. The transient view treats LLM traffic as a request that succeeded or failed and then disappears.
That framing throws away the most valuable corpus your product produces. AI traffic, captured fully, is simultaneously a debugging trace, an evaluation set, a fine-tuning corpus, and a quality observability layer. The teams that capture it have these things. The teams that don't are still trying to figure out how to build them.
What full persistence gives your team
Replay
Something went sideways in production at 3 AM. Without persistence, you have a status code and a stack trace. With persistence, you have the exact prompt that was sent and the exact response that came back. You can replay it against any other model to see if it's a model issue or a prompt issue. Debugging time drops from hours to minutes.
Eval sets
Your real-world traffic is the evaluation set you wish you had. Pick a representative slice, label the outcomes, and you've got an evaluation suite that reflects actual usage rather than synthetic test cases. New prompts get scored against it before deploy. Model upgrades get scored against it before adoption. Quality regressions become visible.
Fine-tuning corpus
The day you decide to fine-tune a smaller model on a hot path to save cost or improve specialization, the data you need is the data you've been logging. Without it, you're starting from scratch with a vendor's UI and synthetic examples. With it, you have curated production traffic ready to filter, label, and feed.
Cost and quality observability
Cost per task. Quality drift after a model rev. Tail latency on a specific provider. All trivially answerable with SQL once the data is captured. None answerable without it.
What to capture
- →The complete prompt as sent — system message, user messages, tool definitions, all of it.
- →The complete response — content, tool calls, refusals, finish reason.
- →Model identifier and provider, including version.
- →Tokens in, tokens out, latency, cost.
- →Upstream cause: the user, the org, the task type, the agent or workflow that initiated the call.
Where to put it
Postgres. The same database the rest of the system uses. JSONB columns for the prompt, response, and assessment payloads. Indexes on user, org, task type, and date. Postgres can absorb millions of rows of model traffic on modest hardware, and SQL is the right query language for the questions you'll ask. When the corpus genuinely outgrows it — later than anyone expects — partition by date or move cold data to object storage. Don't pre-optimize for a size you don't have yet.
Privacy and retention
Capturing prompts and responses means capturing whatever PII flows through them. Treat the table accordingly: row-level security, encrypted at rest, retention policies that match your terms of service, and explicit redaction paths for sensitive fields. The persistence is non-negotiable; the privacy posture has to match.
What changes for your team
Once the data is there, the team's relationship with the AI layer changes. "Did the prompt change?" is a query, not a guess. "Did this regress?" is a query. "What does our top one percent of tasks look like?" is a query. The AI layer becomes a system you can engineer rather than a black box you appease.
Logging summaries is what teams do when they don't believe the corpus is valuable. Capturing the full picture is what teams do when they understand that today's traffic is tomorrow's everything. Capture it.