Complete AI Observability
Monitor and Trace your LLMs

Observe requests, spans, cost and latency in real-time to monitor your LLMs. Understand how your LLMs are performing and where they can be improved.

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Observability that Illuminates

Universal Model Tracking

Track usage from any model with elegant data visualization.

Complete Metadata and Analytics

Monitor and Analyze latency, scores, tokens, and custom key-value pairs.

Prompt-Specific Insights

See which prompts and input variables were used for a request.

Full Span Support

Utilize OpenTelemetry for end-to-end function tracking around LLM calls.

Fine-Tune Models

Use historical data to fine-tune models and improve performance.

High-Performance Solution

Non-proxy design supporting millions of requests daily.

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Unravel the mystery of your LLMs

Understand how your users are interacting with your LLMs. Monitor requests, spans, cost, and latency in real-time. Use advanced analytics to analyze metrics.

Real-Time Auditing

Keep an eye on your LLMs with real-time monitoring of requests, spans, cost, and latency.

Track Analytics

Track metrics based on metadata, prompts, time, and model types.

Locate Bottlenecks

Understand where your Prompts are not performing as expected and why.

Optimize Costs

Understand the cost of your LLMs and how to optimize them.

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Frequently asked questions

If you still have questions feel free to contact us at sales@promptlayer.com

How do enterprises audit LLM behavior?

To audit LLM behavior, enterprises should implement LLM observability that logs every prompt input, model response, and execution detail across production workflows. This creates a searchable, replayable record of how outputs were generated. Teams rely on this audit trail to reproduce past behavior, debug incidents, and demonstrate compliance when regulators or internal stakeholders need clear, defensible explanations.

How do you trace multi-step LLM workflows?

Tracing multi-step LLM workflows requires visibility into how each step executes and passes data forward. Teams rely on end-to-end tracing that captures user inputs, intermediate prompt steps, outputs, models, and latency across every step. This is often implemented using OTEL-compatible LLM tracing, enabling debugging, cost attribution, and systematic optimization of complex workflows.

How do we identify which step in a chain causes hallucinations?

Identifying hallucinations in a multi-step chain requires step-by-step inspection of production traces. By reviewing the inputs and outputs of each sequential prompt in the chain, teams can trace the moment a factual error or drift was introduced, often by running the production trace in a dedicated playground for debugging.

How to collect bad LLM outputs automatically?

Teams collect bad LLM outputs by instrumenting production workflows to flag failures automatically. In practice, an observability platform monitors production runs, identifying outputs with low quality scores, format violations, error patterns, or negative user feedback and log them for review. This creates a reliable dataset teams can use for debugging, evaluation, and improving prompt behavior over time.

How do teams monitor LLM quality over time?

Monitoring LLM quality over time requires continuous, recurring evaluation workflows integrated into the deployment pipeline. Teams should track average and percentile scores from a defined evaluation suite, coupled with production metrics like user feedback or success rates (e.g., successful support ticket resolution). These checks are integrated into deployment workflows so drops in quality are flagged quickly, before prompt decay impacts users.

Complete AI ObservabilityMonitor and Trace your LLMs