MongoDB Atlas & Datadog Integration Guide

Overview

MongoDB Atlas and Datadog work together to give you complete visibility into your database performance and health. Rather than toggling between two separate dashboards, you can monitor MongoDB metrics alongside your application and infrastructure data in a single Datadog workspace. This integration pulls operational metrics directly from your Atlas clusters and surfaces them in real time, making it easier to spot bottlenecks, troubleshoot issues, and optimize resource usage.

The integration is particularly valuable for teams running production workloads on Atlas who need to correlate database performance with application behavior. Instead of discovering a database slowdown after customers report problems, you’ll get alerts the moment query latency spikes or connection pools become exhausted.

How the Integration Works

• API-based connection: Datadog connects to MongoDB Atlas via the Atlas API, pulling metrics from your clusters without requiring agents or code changes to your application.
• Automated metric collection: Key performance indicators—including query latency, throughput, memory usage, and replication lag—are collected at regular intervals and sent to Datadog.
• Real-time dashboards: Metrics appear in Datadog dashboards within minutes of setup, allowing you to visualize trends and correlate database behavior with application events.
• Custom alerting: You can set thresholds on any collected metric and receive notifications via email, Slack, PagerDuty, or other channels when conditions are breached.
• Log integration: MongoDB Atlas logs can be forwarded to Datadog for centralized log analysis, making it simpler to investigate errors and performance issues across your stack.

Key Features & Capabilities

• Unified cluster monitoring: Track CPU, memory, disk I/O, and network metrics for all your Atlas clusters in one place, eliminating the need to check the Atlas UI separately.
• Query performance insights: Monitor slow query logs and query execution patterns directly in Datadog, helping you identify optimization opportunities without leaving your monitoring platform.
• Replication and sharding visibility: Get detailed metrics on replica set health, oplog usage, and shard distribution to ensure your cluster architecture is performing as expected.
• Connection pool management: Track active connections, connection pool saturation, and connection errors to prevent application timeouts and connection exhaustion issues.
• Automated alerting and escalation: Create multi-condition alerts that trigger when database metrics deviate from normal baselines, with automatic escalation to on-call teams via PagerDuty or other incident management tools.
• Historical trend analysis: Retain metrics for extended periods and use Datadog’s analytics tools to identify seasonal patterns, capacity planning needs, and long-term performance degradation.

Setup Difficulty

Easy (5–10 minutes, no code required)

Setting up the MongoDB Atlas and Datadog integration requires only API credentials and a few clicks in the Datadog UI. You’ll need an Atlas API key with read-only permissions, which you generate in the Atlas console. Then, in Datadog, you navigate to the MongoDB Atlas integration tile, paste your API credentials, and specify which clusters to monitor. Datadog begins collecting metrics immediately. No agents, SDKs, or application code changes are necessary.

Alternatives & Workarounds

If the native Datadog integration doesn’t fully meet your needs, consider these options:

• Zapier or Make (formerly Integromat): These no-code automation platforms can trigger workflows based on Datadog alerts or MongoDB events, though they’re better suited for notification routing than continuous metric collection.
• Custom API polling: Build a lightweight service that calls the MongoDB Atlas API on a schedule and pushes metrics to Datadog’s custom metrics endpoint. This gives you fine-grained control over what’s collected and how often.
• Prometheus + MongoDB exporter: If you’re already using Prometheus for monitoring, deploy a MongoDB exporter in your environment and scrape metrics directly from your Atlas clusters. You can then ingest those metrics into Datadog via the Prometheus integration.
• CloudWatch (AWS-only): If your Atlas clusters run on AWS, you can enable CloudWatch integration in Atlas to send metrics to CloudWatch, then use Datadog’s CloudWatch integration to pull them in. This adds a layer of indirection but may fit existing AWS workflows.

When to Use This Integration

The MongoDB Atlas–Datadog integration is a natural fit if you’re already paying for Datadog and want to consolidate your monitoring stack. It’s especially valuable for teams that:

• Run production MongoDB workloads and need proactive alerting on database health.
• Want to correlate database performance with application metrics and infrastructure events in a single pane of glass.
• Use Datadog for APM (application performance monitoring) and want database metrics in the same context.
• Operate multi-cluster Atlas deployments and need a scalable way to monitor all clusters simultaneously.
• Have compliance or audit requirements that benefit from centralized metric retention and alerting trails.

Potential Limitations

While the integration covers most common monitoring needs, be aware of a few constraints:

• Metric granularity: Some advanced MongoDB diagnostics (like detailed index usage or lock contention) may require direct access to the Atlas UI or MongoDB’s native profiling tools.
• Log forwarding setup: Although logs can be forwarded to Datadog, you’ll need to configure this separately in Atlas and may incur additional Datadog log ingestion costs.
• API rate limits: The Atlas API has rate limits, so if you’re monitoring dozens of clusters, ensure your polling frequency doesn’t exceed those limits.
• Latency: Metrics are collected at regular intervals (typically every 1–5 minutes), so real-time sub-minute alerting isn’t possible through this integration alone.

Cost Considerations

The MongoDB Atlas–Datadog integration itself is free to set up and use. However, keep in mind that Datadog charges based on the number of custom metrics ingested and the volume of logs retained. A typical MongoDB Atlas cluster generates 20–50 metrics per cluster, which falls within most Datadog pricing tiers. If you’re also forwarding logs, factor in Datadog’s log ingestion costs, which can vary based on volume and retention period.

Frequently Asked Questions

Do I need to install an agent on my MongoDB servers to use this integration?

No. The integration is API-based and pulls metrics directly from MongoDB Atlas without requiring any agents or modifications to your database infrastructure. Your application servers and other infrastructure may still run Datadog agents for APM and system monitoring, but MongoDB itself does not require one.

What happens if my Atlas API key expires or is revoked?

Datadog will stop collecting metrics and will alert you that the integration is disconnected. You’ll need to generate a new API key in the Atlas console and update it in Datadog. To avoid disruption, set a calendar reminder to rotate your API keys periodically (e.g., every 90 days) and keep backup credentials on hand.

Can I monitor Atlas clusters across multiple organizations?

Yes, but you’ll need to configure a separate API key for each Atlas organization and add each one to Datadog. If you have a large multi-tenant setup, consider whether a centralized monitoring approach (like a dedicated Datadog organization) makes sense for your team.

How quickly do alerts trigger after a metric threshold is breached?

Alerts typically trigger within 1–5 minutes of a metric breach, depending on your polling interval and Datadog’s evaluation frequency. For time-critical issues, you may want to supplement this with application-level monitoring or use MongoDB’s built-in alerting for immediate notification of cluster-level events.

Disclaimer

Integration features and capabilities are subject to change. Always verify the current state of the MongoDB Atlas–Datadog integration on the official Datadog integration page and MongoDB Atlas documentation before making deployment decisions. This article reflects the integration as of its publication date and may not account for recent updates or feature additions.