LLM Observability | AI-Native Observability Platform, WhaTap

Why LLM Observability matters

For LLM-powered services, knowing only whether the API returns a response isn't enough to judge operational health.
Monitoring response quality, token cost, and latency in real time—and managing multi-model workloads efficiently—has become a critical business challenge.

Unpredictable token cost

Depending on the model, prompt length, and response size, per-request cost can vary by up to 10×. Even failed requests still incur token cost.

Invisible quality risk

Hallucinations hidden behind a successful (HTTP 200) response erode brand trust and cause real business damage—an estimated $67.4B in losses in 2024.

Degradation masked
by averages

Average response time may look fine, but the p99 latency that only some users experience stays hidden. Once that context is gone, the issue can't even be reproduced.

Multi-model and infrastructure
complexity

LLM services often run across multiple models, multiple APIs, GPU infrastructure, and Kubernetes. Viewing each resource in isolation slows down root-cause analysis.

WhaTap LLM Observability

Visualize your LLM API call flow, token usage, and per-model performance on a single dashboard.
With trace-level drill-down, you can pinpoint where latency builds up and where cost leaks—at a glance.

What makes WhaTap LLM Observability different

WhaTap delivers unified insight across the entire path—from your LLM application to GPU infrastructure, Kubernetes pods, and the call trace itself.

The full call context, preserved as a single trace

Every input—system messages, prompts, and tool calls—is stored exactly as sent, so any incident can be reproduced instantly.

An operations view that pairs performance with cost

Compare response speed, token usage, error rate, and cost flow side by side per model—supporting the right call whether a service prioritizes performance or cost efficiency.

Unified tracing across LLM and infrastructure (GPU · K8s)

Tell apart whether latency comes from LLM processing or a GPU resource bottleneck, all on one screen. It's a uniquely WhaTap approach, built on our full-stack observability foundation.

Key features

From establishing operational visibility to optimizing cost, we've brought together the core capabilities you need to run AI services efficiently.

Unified LLM dashboard

Move from real-time status → requests & performance → tokens & cost → per-model comparison. Handle everything from status checks to root-cause analysis on a single page. Active transaction speed, LLM call types, and heatmaps surface anomalies the moment they appear.

LLM performance metrics &
per-model comparison

Track TTFT and TPOT by percentile to precisely locate the latency and bottlenecks that averages hide. From there, compare speed, cost, and error rate across models—so you can select and switch to the right serving engine or model for your use case, based on data.

Token usage & cost analysis

Track input/output token usage by time of day, average tokens per request, and cumulative daily cost. Compare token usage against cost to spot inefficient call paths, and use each model's cost contribution to prioritize budget optimization.

LLM API trace analysis +
GPU correlation

For each trace, review input/output messages, token counts, cost, latency, and host location—while analyzing GPU usage, VRAM, and temperature at the same moment. It's the only unified environment that lets you tell whether latency stems from LLM processing or a GPU resource bottleneck.

Response quality & prompt analysis

Automatic scoring of LLM responses, combined with performance and quality analysis, lets you monitor problem cases instantly—and manage and optimize prompts as systematically as code, all in one operations environment.

Unified log & event alerting

Threshold-based event rules and integrations with Slack, Telegram, Teams, Webhook, AlertNow, and more let you detect and respond to cost spikes, rising error rates, and latency in real time.

Experience the WhaTap right now.

Experience the
WhaTap right now.