Redis Cloud Metrics Now Flow Directly to Datadog

A new native integration sends Redis Cloud performance metrics directly to Datadog, simplifying monitoring without requiring any extra infrastructure.

Redis and Datadog have announced a new native integration that allows customers to monitor their Redis Cloud databases directly within the Datadog observability platform. According to a post on the official Redis blog, the feature is now available in a Private Preview. This partnership addresses a long-standing need for engineering teams by creating a direct pipeline for performance metrics, eliminating the complex workarounds previously required to get this critical data into a centralized monitoring system. For developers and operations teams who rely on both Redis for high-performance caching and Datadog for application monitoring, this move promises to unify their view of the entire technology stack. The integration is designed to be seamless, providing immediate value by placing Redis health and performance data alongside metrics from the rest of a company's infrastructure. This direct connection is a significant step forward from older methods that often involved manually configuring and maintaining separate data collectors, which could be brittle and add operational overhead.

The key technical innovation of this integration is its "native" design, which fundamentally changes how data is collected. Unlike traditional approaches that might require users to deploy a separate agent or a "sidecar" container to scrape metrics from Redis, this new method works through a direct, secure API connection between the Redis Cloud control plane and Datadog's ingestion endpoints. When a user enables the integration, Redis Cloud is authorized to push its metrics—such as memory usage, CPU utilization, and command counts—straight to the customer's Datadog account. This server-to-server communication model removes a potential point of failure, as there is no intermediate software to manage or troubleshoot. It also reduces infrastructure complexity and cost, as teams no longer need to provision compute resources to run a separate metrics exporter. Finally, it ensures that the data is timely and accurate, as it comes directly from the managed Redis Cloud service itself.

This collaboration is part of a much larger industry trend toward deeper, more seamless integrations between best-of-breed cloud services. As companies increasingly adopt a multi-cloud strategy using different specialized databases, the problem of fragmented observability has become a major challenge. In response, platforms like Datadog have shifted their strategy to become a central hub that integrates with the entire ecosystem. This "single pane of glass" approach is highly valued because it reduces cognitive load for on-call engineers and accelerates incident response. This specific integration mirrors similar native partnerships Datadog has forged with major cloud providers like AWS and Google Cloud. It signals that managed database providers now see deep observability as a core part of their product offering, not just an optional add-on. For the end-user, this trend means less time spent on plumbing and more time focused on leveraging data to build better software.

For founders, CTOs, and IT leaders, the practical takeaway is a direct path to higher operational efficiency and improved system reliability. Teams already using both Redis Cloud and Datadog can now consolidate their monitoring tools, simplifying workflows and allowing engineers to correlate Redis performance issues with application-level symptoms all within a single interface. This unified view dramatically shortens the time it takes to diagnose and resolve problems. Looking ahead, the immediate next step is the move from Private Preview to General Availability, which will make the integration accessible to all customers. We can also anticipate the scope of the integration to expand over time, potentially including logs, distributed tracing, and more sophisticated, pre-built dashboards and alerts. Companies invested in this ecosystem should monitor the progress of the preview and prepare to adopt it more broadly once it becomes generally available.

For engineering teams, this native integration eliminates the need to build and maintain fragile custom data pipelines for Redis monitoring. It centralizes observability, reducing tool sprawl and the operational overhead of correlating Redis performance with the rest of their application stack inside a single platform.

This partnership directly reduces operational costs by eliminating the need for engineers to build and manage custom monitoring infrastructure. It streamlines workflows, allowing teams to resolve performance issues faster, which improves application reliability and frees up valuable developer time for feature development.