Datadog

New York City, NY, USA
2010
  |  By Mahashree Rajendran
Organizations today rely on cloud object storage to power diverse workloads, from data analytics and machine learning pipelines to content delivery platforms. But as data volumes explode and storage patterns become more complex, teams often struggle to understand and proactively optimize their storage utilization. When issues arise—such as unexpected costs or performance bottlenecks—these teams frequently lack the visibility needed to quickly identify and resolve root causes.
  |  By Danny Driscoll
Amazon Elastic Container Service (ECS) is a container orchestration service that enables you to efficiently deploy new applications or modernize existing ones by migrating them to a containerized environment. Building on ECS gives you the flexibility, scalability, and security that containers offer, but also presents challenges in monitoring and troubleshooting your applications and infrastructure.
  |  By Jordan Obey
In 2021, we announced the release of the Datadog Lambda extension, a simplified, cost-effective way for customers to collect monitoring data from their AWS Lambda functions. This extension was a specialized build of our main Datadog Agent designed to monitor Lambda executions.
  |  By Anjali Thatte
AWS Inferentia and AWS Trainium are purpose-built AI chips that—with the AWS Neuron SDK—are used to build and deploy generative AI models. As models increasingly require a larger number of accelerated compute instances, observability plays a critical role in ML operations, empowering users to improve performance, diagnose and fix failures, and optimize resource utilization.
  |  By Matthieu Jaillais
Over the past few years, Arm has surged to the forefront of computing. For decades, Arm processors were mainly associated with a handful of specific use cases, such as smartphones, IoT devices, and the Raspberry Pi. But the introduction of AWS Graviton2 in 2019 and the adoption of Arm-based hardware platforms by Apple and others helped bring about a dramatic shift, and Arm is now the most widely used processor architecture in the world.
  |  By Evan Pandya
Datadog APM and distributed tracing provide teams with an end-to-end view of requests across services, uncovering dependencies and performance bottlenecks to enable real-time troubleshooting and optimization. However, traditional manual instrumentation, while customizable, is often time consuming, error prone, and resource intensive, requiring developers to configure each service individually and closely collaborate with SRE teams.
  |  By Thomas Sobolik
Managing LLM provider costs has become a chief concern for organizations building and deploying custom applications that consume services like OpenAI. These applications often rely on multiple backend LLM calls to handle a single initial prompt, leading to rapid token consumption—and consequently, rising costs. But shortening prompts or chunking documents to reduce token consumption can be difficult and introduce performance trade-offs, including an increased risk of hallucinations.
  |  By Cliff Kim
Infrastructure-as-code (IaC) tools like Terraform and CloudFormation allow teams to define, manage, and provision their cloud infrastructure using code, as opposed to clicking through consoles or executing commands via a CLI. IaC adoption is now widespread and helps teams increase productivity and efficiency, but it also introduces new surface area for mistakes, defects, and other risks.
  |  By Amber Tunnell
Datadog is a central hub of information—enabling you to see logs, traces, and metrics from across your stack and providing a centralized source of notifications about potential issues. However, when Datadog notifies you of an issue, you often need to log in to other applications to fully assess and resolve it, which slows down mitigation.
  |  By Micah Kim
Today, CISOs and security teams face a rapidly growing volume of logs from a variety of sources, all arriving in different formats. They write and maintain detection rules, build pipelines, and investigate threats across multiple environments and applications. Efficiently maintaining their security posture across multiple products and data formats has become increasingly challenging.
  |  By Datadog
Cloud spending continues to grow, but managing costs effectively remains a challenge for many organizations. In this video, Datadog Senior Product Manager Kayla Taylor dives into our recent State of Cloud Costs report—which analyzed AWS cloud cost data from hundreds of organizations—to understand the key factors driving cloud expenses. We explore the impact of adopting emerging compute technologies like Arm-based processors, GPUs, and AI capabilities, how usage patterns and previous-generation technologies affect cloud costs, and the role of AWS discount programs in cost management.
  |  By Datadog
In this video we’ll continue looking at how Kubernetes handles authentication with a look at bootstrap and static token authentication.
  |  By Datadog
Datadog operates dozens of Kubernetes clusters, tens of thousands of hosts, and millions of containers across a multi-cloud environment, spanning AWS, Azure, and Google Cloud. With over 2,000 engineers, we needed to ensure that every developer and application could securely and efficiently access resources across these various cloud providers.
  |  By Datadog
This video aims to showcase how developers can self-serve from an application to simplify the management of their AWS cloud resources. Rather than switching between tools or reaching out to another team for help, developers can take action directly from their observability tool, enabling faster resolution of application issues. We will demonstrate how to build a simple app that allows them to minimize disruptions by quickly taking action on their SQS queues in AWS, using insights provided by Datadog.
  |  By Datadog
Temporal is an open source platform to build resilient and reliable distributed systems. Datadog started using Temporal in 2020 as the foundation for our internal software delivery platform. Since then, its usage has been widely adopted as a platform that any engineering team can use to build their systems. In this Datadog on episode, Ara Pulido chats with Loïc Minaudier, Senior Software Engineer in the Atlas team, responsible for providing a developer platform on top of Temporal, and Allen George, Engineering Manager in the Datadog Workflows team.
  |  By Datadog
On This Month in Datadog, we’re spotlighting LLM Observability’s native integration with Google Gemini, which automatically captures the LLM requests your application makes to Gemini models.
  |  By Datadog
Datadog Service Catalog automatically consolidates real-time observability data and internal engineering knowledge about all of your services into a unified view.
  |  By Datadog
Datadog is constantly elevating the approach to cloud monitoring and security. This Month in Datadog updates you on our newest product features, announcements, resources, and events. To learn more about Datadog and start a free 14-day trial, visit Cloud Monitoring as a Service | Datadog. This month, we put the Spotlight on Datadog LLM Observability’s native integration with Google Gemini.
  |  By Datadog
Learn how Appfolio is delivering positive customer experiences in real estate with generative AI — supported and safeguarded by Datadog’s LLM Observability. See how you can use Datadog LLM Observability to monitor, troubleshoot, improve, and secure your LLM applications.
  |  By Datadog
With over 426 million active users, comprised of consumers and merchants, Paypal processes approximately 25 billion transactions valued at around $1.53 trillion USD. Paypal is shaping the future of commerce for millions of customers globally, and to do that, they use Datadog to provide timely insights into their entire stack.
  |  By Datadog
As Docker adoption continues to rise, many organizations have turned to orchestration platforms like ECS and Kubernetes to manage large numbers of ephemeral containers. Thousands of companies use Datadog to monitor millions of containers, which enables us to identify trends in real-world orchestration usage. We're excited to share 8 key findings of our research.
  |  By Datadog
The elasticity and nearly infinite scalability of the cloud have transformed IT infrastructure. Modern infrastructure is now made up of constantly changing, often short-lived VMs or containers. This has elevated the need for new methods and new tools for monitoring. In this eBook, we outline an effective framework for monitoring modern infrastructure and applications, however large or dynamic they may be.
  |  By Datadog
Where does Docker adoption currently stand and how has it changed? With thousands of companies using Datadog to track their infrastructure, we can see software trends emerging in real time. We're excited to share what we can see about true Docker adoption.
  |  By Datadog
Build an effective framework for monitoring AWS infrastructure and applications, however large or dynamic they may be. The elasticity and nearly infinite scalability of the AWS cloud have transformed IT infrastructure. Modern infrastructure is now made up of constantly changing, often short-lived components. This has elevated the need for new methods and new tools for monitoring.
  |  By Datadog
Like a car, Elasticsearch was designed to allow you to get up and running quickly, without having to understand all of its inner workings. However, it's only a matter of time before you run into engine trouble here or there. This guide explains how to address five common Elasticsearch challenges.
  |  By Datadog
Monitoring Kubernetes requires you to rethink your monitoring strategies, especially if you are used to monitoring traditional hosts such as VMs or physical machines. This guide prepares you to effectively approach Kubernetes monitoring in light of its significant operational differences.

Datadog is the essential monitoring platform for cloud applications. We bring together data from servers, containers, databases, and third-party services to make your stack entirely observable. These capabilities help DevOps teams avoid downtime, resolve performance issues, and ensure customers are getting the best user experience.

See it all in one place:

  • See across systems, apps, and services: With turn-key integrations, Datadog seamlessly aggregates metrics and events across the full devops stack.
  • Get full visibility into modern applications: Monitor, troubleshoot, and optimize application performance.
  • Analyze and explore log data in context: Quickly search, filter, and analyze your logs for troubleshooting and open-ended exploration of your data.
  • Build real-time interactive dashboards: More than summary dashboards, Datadog offers all high-resolution metrics and events for manipulation and graphing.
  • Get alerted on critical issues: Datadog notifies you of performance problems, whether they affect a single host or a massive cluster.

Modern monitoring & analytics. See inside any stack, any app, at any scale, anywhere.