vLLM is a high-performance serving framework for large language models (LLMs). It optimizes token generation and resource management to deliver low-latency, scalable performance for AI-driven applications such as chatbots, virtual assistants, and recommendation systems. By efficiently managing concurrent requests and overlapping tasks, vLLM enables organizations to deploy LLMs in demanding environments with speed and efficiency.
Datadog’s vLLM integration provides comprehensive visibility into the performance and resource usage of your LLM workloads. By collecting real-time metrics, Datadog enables you to monitor key performance indicators such as response times, throughput, and resource consumption so you can quickly identify issues and optimize infrastructure usage for cost efficiency. Datadog’s vLLM integration comes with an out-of-the-box (OOTB) dashboard that is automatically populated with these key metrics, so you can seamlessly begin monitoring your LLM workloads in Datadog.
In this post, we’ll show you how you can:
- Monitor performance and ensure that your LLM applications deliver fast, reliable responses
- Optimize GPU and token usage to reduce LLM infrastructure costs
- Detect and address critical issues before they impact production
Monitor performance and ensure fast, reliable responses to prompts
Datadog’s vLLM integration provides an OOTB dashboard that visualizes critical performance metrics like end-to-end request latency, token generation throughput, and time to first token (TTFT) in a single pane.
These metrics provide deep insights into how efficiently your LLM models are processing requests. For instance, if your LLM-powered chatbot experiences a delay in response times, you can use the OOTB dashboard to pinpoint whether cache configuration, response generation throughput, or token generation issues are causing the problem. This visibility enables you to adjust resource allocation to meet demand and keep your LLMs performing at their best, even during peak traffic.
By correlating LLM performance data with the infrastructure metrics that you’re already collecting in Datadog—such as GPU or CPU utilization, and network latency—you can quickly identify and resolve any underlying infrastructure bottlenecks that might be affecting your LLM workloads. For instance, if you notice increased request latency in your LLM application, Datadog can help you trace the issue back to its root cause, such as GPU saturation. With this end-to-end visibility, you can easily address the resource constraints causing the delays and ensure your LLM application remains fast and reliable, even under heavy load.
Optimize resource usage and reduce cloud costs
LLMs require substantial high-compute resources to handle inference tasks. Without proper monitoring, over-provisioning GPU resources can result in runaway cloud costs. Datadog’s vLLM integration provides visibility into key resource metrics, such as GPU/CPU cache usage and request swapping between CPU and GPU. This enables you to optimize resource allocation and prevent unnecessary scaling. By tracking real-time resource consumption, organizations can reduce idle cloud spend while ensuring that their LLM workloads maintain high performance.
For example, by monitoring the GPU memory used by your LLM-powered virtual assistant, you can avoid exceeding resource limits that trigger automatic—and costly—scaling. With Datadog’s vLLM integration, you can rightsize your infrastructure and avoid waste, balancing performance with cost-efficiency.
Detect and address critical issues before they impact production
Datadog’s vLLM integration allows you to set up alerts for key LLM performance metrics, enabling proactive issue detection. You can monitor metrics like number of preemptions, requests waiting, and queue size to catch potential problems before they impact your application’s performance. For example, if your LLM application is handling a large volume of requests, Datadog can notify you of growing queues that may lead to delayed responses or service interruptions.
The Datadog vLLM integration comes with preconfigured Recommend Monitors to help you seamlessly set up alerts for any critical performance issues, such as high request latency, token generation throughput, or excessive resource usage. By proactively monitoring your LLM workloads with predefined thresholds and actionable notifications, Datadog enables you to respond quickly and ensure that your LLM applications continue to run smoothly.
Get started with the Datadog vLLM integration
Datadog’s vLLM integration delivers comprehensive visibility into the performance, resource utilization, and health of your LLM workloads. By using Datadog to monitor key performance metrics and set up proactive alerts, you can ensure that your LLM-powered applications meet user expectations while controlling cloud infrastructure costs.
To learn more about how Datadog enables customers to monitor every layer of their AI tech stack, read our blog post on AI integrations. And for more information about using our platform to monitor your LLM applications, read our blog post on Datadog LLM Observability.
Get started using our vLLM integration today. If you’re not already using Datadog, you can sign up for a 14-day free trial.