PromptLayer gives teams observability and control for LLM apps so prompts, models, and outputs stay reliable. It captures every request and response with metadata, then links them to code, releases, and users. Compare prompts across models, replay tricky cases, and track costs and latency by route. Add evals and tags to judge quality. With alerts, dashboards, and versioning, teams fix regressions quickly and prove behavior during reviews and audits confidently.
Log every prompt, response, and token count with headers, user IDs, and environment. Capture prompts after templating so you see what the model actually received. Metadata ties calls to releases and tickets. With concrete histories, engineers reproduce bugs, security reviews trace data paths, and product teams explain outcomes without guessing, turning support threads into actionable, verifiable fixes quickly.
Version prompts with diffs and notes. Replay historical calls against newer models to compare behavior and side effects. Pin versions for sensitive flows, then roll out experiments to a slice of traffic. This control introduces change safely, reveals drift, and documents intent. Teams make informed upgrades instead of blind switches and can demonstrate why the chosen combination meets accuracy, speed, and cost targets.
Run automatic and human evals to score factuality, tone, and safety. Tag edge cases and build curated test suites that represent your customers. Dashboards aggregate pass rates by route and model. With objective signals, leaders prioritize fixes that matter, reviewers align standards, and quality stops depending on anecdotal chats that are hard to repeat, measure, or defend when incidents occur.
Track spend per feature, team, and tenant with token-level detail. Latency histograms reveal slow prompts and overlong contexts. Budgets and alerts warn before overruns. With clear costs, engineering trims waste, finance forecasts reliably, and PMs make tradeoffs explicit instead of guessing, which avoids surprise invoices and keeps experiments sustainable across quarters and product lines under growth.
Set thresholds and route alerts to Slack, PagerDuty, or email when evals drop or error rates rise. Prebuilt dashboards show success, failure, and drift by release. SDKs and APIs integrate with data warehouses and issue trackers. These connections make changes auditable and keep stakeholders aligned, turning daily logs into operating metrics that guide fixes, rollbacks, and confident launches.
Best for product, platform, and ML teams operating LLM features in production. Helpful for startups moving from prototypes to governed services and enterprises needing audit trails. With logging, versioning, evals, and budgets, groups coordinate safely, resolve regressions faster, and communicate tradeoffs clearly to leadership, compliance, and customers across regions and workloads.
PromptLayer replaces screenshot debugging and ad hoc spreadsheets with trustworthy histories, versioned prompts, and measurable quality. Teams replay issues, compare models, and control costs, preventing regressions from silently reaching users. Alerts and dashboards surface drift early, and integrations connect fixes to tickets. The result is stable, explainable AI behavior that scales with fewer surprises and lower risk.
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