VLLM Observability

Optimize LLM Application Performance with Datadog and vLLM

Gain comprehensive visibility into the performance and resource usage of your LLM workloads.

Monitor and Optimize vLLM Inference Performance in Real Time

Gain complete visibility into inference latency, token generation throughput, and time to first token (TTFT) with out-of-the-box dashboards for vLLM workloads
Quickly identify bottlenecks across GPUs, memory, and request queues to keep LLM applications fast under production load
Correlate serving metrics with end-to-end traces to understand how infrastructure performance impacts user experience and downstream workflows

Track GPU, CPU, memory, and cache utilization in real time to prevent over-provisioning and reduce unnecessary cloud spend
Rightsize infrastructure based on live usage patterns and token demand to balance performance and efficiency
Continuously uncover opportunities to improve cost-to-performance ratios across vLLM deployments without sacrificing reliability

Proactively monitor queue depth, preemptions, request backlogs, and other critical serving metrics with recommended preconfigured monitors
Automatically surface anomalies in latency, throughput, and resource consumption before they degrade response quality
Resolve performance disruptions early with actionable alerts and full-stack visibility into your inference pipeline

Get full visibility into every experiment run with automatic tracing that captures evaluation scores, latency, errors, and token usage
Resolve regressions faster by isolating low-scoring test cases and inspecting tool calls, retrieval steps, and intermediate outputs in the execution trace
Keep testing repeatable across teams with versioned datasets, experiment runs, and shared performance analysis in one place
Compare experiment outcomes alongside production telemetry and evaluation signals from the same platform

Datadogを始める5つのステップ

ステップ1

トライアル登録フォームに入力わずか30秒で無料でアカウントを作成。クレジットカードは不要

ステップ2

技術スタックに関する基本的な質問に回答約1分で完了

ステップ3

Datadog エージェントをインストールシステムレベルのメトリクスをDatadogプラットフォームに送信

ステップ4

API経由で追加のメトリクスを取得するための認証情報を提供 AWS、Azure、GCPなどのクラウド環境を完全に可視化

ステップ5

すぐに使えるダッシュボードでパフォーマンスを視覚化環境全体のパフォーマンスをリアルタイムで確認可能