LLM Inference Observability
Diagnose slow requests, KV cache misses, scheduler bottlenecks, batching inefficiencies, and GPU waste across vLLM, SGLang, TensorRT-LLM, Ray Serve, and KubeRay.
Your current monitoring tells you that inference is slow. We show you why — from request queue to GPU execution.
The Gap
Application observability shows what your app did. Inference observability shows what your infrastructure did.
Product
Understand every millisecond of an inference request — queue, prefill, decode, streaming, and GPU.
See cache hits, misses, evictions, reuse, and cost savings at the request, model, and tenant level.
Understand queueing, batching, starvation, and throughput drops inside the engine scheduler.
Correlate inference behavior with GPU utilization, memory pressure, and idle time.
Trace bottlenecks across tensor parallelism, pipeline parallelism, and NCCL communication.
Expose engine-aware metrics for smarter routing, replica selection, and capacity decisions.
Normalize metrics across vLLM, SGLang, TensorRT-LLM, Ray Serve, and more into one canonical model.
Request Explainability
Instead of seeing only 'Request latency: 7.2s', see exactly where every millisecond was spent — from queue admission through decode completion.
Features
Buyer Value
KV Cache Intelligence
KV cache behavior directly impacts TTFT, throughput, and GPU cost. Most teams only see basic utilization — not why cache performance changes.
Features
Buyer Value
Scheduler Visibility
The scheduler controls queueing, batching, throughput, and TTFT. Most teams treat it as a black box — leaving them unable to explain why requests are slow.
Features
Buyer Value
GPU & Memory Observability
GPU utilization alone is not enough. You need to know whether the GPU is busy doing useful inference work or waiting on memory, communication, queueing, or scheduling.
Features
Buyer Value
Distributed Inference Debugging
As models scale across GPUs and nodes, inference problems become distributed-systems problems. Tensor-parallel imbalance, pipeline bubbles, and NCCL overhead are invisible without the right instrumentation.
Features
Buyer Value
Routing & Autoscaling Signals
CPU and GPU utilization are not enough for LLM routing and autoscaling. Inference routing needs engine-level signals — queue depth, KV cache pressure, TTFT trends, and batch utilization.
Features
Buyer Value
Multi-Engine Telemetry
Every engine exposes different metrics with different names and semantics. This product normalizes them into one consistent, canonical inference schema.
Features
Buyer Value
Integrations
This is not a Datadog or Grafana replacement. It is the missing inference-engine telemetry layer — sending better data into the observability tools your team already uses.
Inference Engines
Orchestration
Telemetry
Infrastructure
Send better inference telemetry into the observability tools your team already uses.
Incident Response
Cost & Capacity Planning
Inference cost is shaped by queueing, batching, cache reuse, GPU utilization, and distributed overhead — not just token volume. See exactly where GPU time goes and where money is being wasted.
Features
Buyer Value
Architecture
Collect metrics, traces, logs, and runtime state from inference engines.
Converts engine-specific signals into a canonical inference schema.
Connects gateway request IDs, app traces, engine traces, and GPU metrics.
Detects bottlenecks, regressions, cache failures, and scheduler issues.
Suggests routing, scaling, cache, and concurrency changes.
Sends data to Prometheus, OpenTelemetry, Grafana, Datadog, New Relic, or cloud observability tools.
Differentiation
| Capability | Generic APM / LLM Observability | This Product |
|---|---|---|
| App traces | Strong | Supported |
| Token usage | Strong | Supported |
| Prompt logging | Strong | Supported |
| Request latency | Strong | Deep breakdown |
| Queue / prefill / decode visibility | Limited | Native |
| KV cache intelligence | Limited | Native |
| Scheduler visibility | Limited | Native |
| Batch efficiency | Limited | Native |
| Tensor-parallel visibility | Limited | Native |
| NCCL bottleneck detection | Limited | Native |
| Engine-normalized telemetry | Limited | Native |
| Routing / autoscaling signals | Limited | Native |
We do not replace your observability stack. We make it inference-aware.
Use Cases
Find whether latency comes from queueing, prefill, decode, cache misses, or GPU communication.
Identify queue pressure, cache misses, and prefill bottlenecks that delay time to first token.
See whether GPUs are doing useful inference work or waiting on scheduler, memory, or network.
Find cache misses, fragmentation, evictions, and tenant-level cache inefficiency.
Understand batch size, batch utilization, fragmentation, and scheduler behavior.
Forecast GPU needs based on request volume, context length, cache reuse, and decode throughput.
Route requests based on queue depth, cache locality, replica health, and engine pressure.
Find tensor-parallel imbalance, pipeline bubbles, and NCCL communication bottlenecks.
Who Uses It
Operate vLLM, SGLang, TensorRT-LLM, Ray Serve, and Kubernetes-based inference at scale.
Debug GPU utilization, memory pressure, networking, and distributed serving behavior.
Reduce incident resolution time, improve alert quality, and build operational confidence.
Understand how model behavior — context length, output length, batch size — affects infrastructure performance.
Attribute GPU cost by model, tenant, cache behavior, and request type. Find and fix wasted capacity.