LLM Observability
Token overruns, slow API calls, broken call chains — you need more than alerts. See the real cost and behavior behind every request, with token usage, cost trends, call traces, and root cause analysis unified in one place.
Pay-As-You-Go
$0.26
per 1K timeseries, per day*
With TrueWatch LLM Observability, every decision about your LLM usage is backed by data.
- Token Monitoring & Request Volume Tracking
Track cumulative Total Token consumption with a Prompt vs. Completion Token breakdown, split by model and application, and set thresholds to catch runaway requests before they hit your bill. - Cost Optimization & Spend Trend Analysis
See API call counts, average cost per call, and cumulative spend in one chart, with instant alerts when cost or volume crosses a threshold — before a spike becomes a budget overrun. - Tracing & Prompt Monitoring Across Call History
Look up complete metadata for every inference request by Trace ID — model version, temperature, prompt length, and output — then filter failed or timed-out calls and replay them from the input/output logs. - Flame Graph Root Cause Analysis
Automatically render each sub-span's latency as a flame graph to spot the slowest call stage, with P75/P90/P99 curves to tell sporadic slow requests from systemic bottlenecks.
*Billed daily. The cost is based on the amount of timeseries data collected by DataKit.
Multi-Year/Volume Discounts Available
Common Questions
- What counts as a timeseries in LLM Observability?
- A timeseries is a unique combination of metric and tag values reported for your LLM calls — for example, token counts or latency broken down by model, application, or request dimension. Billing is based on the number of unique timeseries collected per day.
- How does token monitoring help control costs?
- The dashboard breaks down Prompt Token and Completion Token usage by model and application, so you can evaluate the cost and complexity of each LLM call during development and set usage thresholds that trigger alerts before a single request runs over limit.
- Can I trace an individual LLM request?
- Yes, every request has a Trace ID you can look up for full metadata — start and end time, model version, temperature, prompt length, and output summary — and you can filter for failed or timed-out requests and replay them from the logs.
- How does root cause analysis for LLM latency work?
- Each trace's sub-span latency is automatically rendered as a flame graph, so you can see which call stage is consuming the most time, and P75/P90/P99 response time curves help distinguish sporadic slow requests from systemic performance bottlenecks.
