MongoDB is one of the most popular document databases out there, and if you're running it in production, you already know that things can go sideways fast without proper monitoring. Slow queries, replication lag, disk pressure — these problems don't announce themselves politely. You need tools that catch them early. Here's a look at five monitoring tools worth considering in 2026, what they do well and where they fall short.
1. MongoDB Atlas built-in monitoring
Atlas is MongoDB's own cloud platform, and it comes with monitoring baked in. If you're already running your databases on Atlas, this is the most straightforward option since there's nothing extra to install or configure.
The built-in dashboards cover the essentials: operation counters, query targeting, replication lag, connections and disk I/O. The Real-Time Performance Panel is genuinely useful for spotting slow operations as they happen. You also get automated alerts for things like high CPU or replication delays.
| Feature | Details |
|---|---|
| Deployment | Cloud-only (Atlas) |
| Query profiling | Yes, with Performance Advisor |
| Alerting | Built-in with configurable thresholds |
| Pricing | Included with Atlas tier (M10+) |
| Custom dashboards | Limited |
The main drawback is that it only works with Atlas-hosted clusters. If you're self-hosting MongoDB or running a hybrid setup, you'll need something else. The alerting is also somewhat basic compared to dedicated monitoring platforms — you can set thresholds, but complex alert routing or escalation policies aren't really its thing.
For teams fully committed to Atlas, this covers the basics well enough that you might not need anything else for smaller deployments.
2. Percona Monitoring and Management (PMM)
PMM is an open-source monitoring platform from Percona that supports MongoDB alongside PostgreSQL and MySQL. It bundles Grafana for dashboards and VictoriaMetrics for time-series storage, and gives you a pretty detailed view of what's going on inside your database.
What makes PMM stand out for MongoDB specifically is the query analytics. It captures slow queries, shows you execution plans and helps you figure out which operations are dragging things down. The QAN (Query Analytics) dashboard breaks down query patterns by response time, count and load, which is extremely helpful when you're trying to optimize a workload.
| Feature | Details |
|---|---|
| Deployment | Self-hosted (Docker or bare metal) |
| Query analytics | Yes, detailed QAN dashboard |
| Replication monitoring | Yes, including oplog window |
| Pricing | Free and open source |
| Multi-database support | MongoDB, PostgreSQL, MySQL |
The setup takes some effort — you need to install the PMM server and then deploy PMM clients on each database host. It's not a quick five-minute job, especially if you have a large fleet. And because it's self-hosted, you're responsible for keeping the monitoring infrastructure itself running and updated.
But if you want deep MongoDB monitoring without a SaaS bill, PMM is hard to beat.
3. Datadog MongoDB integration
Datadog is a cloud monitoring platform that does a lot more than just database monitoring, but its MongoDB integration is solid. It collects metrics from MongoDB through an agent running on your database hosts, and you can correlate database performance with application metrics, infrastructure data and logs all in one place.
The MongoDB-specific dashboards show connections, operations per second, memory usage, replication status and lock percentages. Datadog also supports custom queries, so you can track application-specific metrics alongside the standard ones.
Where Datadog really shines is in the broader observability picture. If you're already using it for APM or infrastructure monitoring, adding MongoDB monitoring means you can trace a slow API response all the way down to a specific database query. That kind of correlation saves real debugging time.
The downside is cost. Datadog's pricing model charges per host per month, and database monitoring is an add-on on top of the base infrastructure monitoring. For a team with a handful of MongoDB nodes it's reasonable, but costs can climb quickly at scale. There's also a learning curve to get the most out of the platform — it does a lot, and configuring everything properly takes time.
4. Grafana with MongoDB exporter
If you're already running Grafana and Prometheus (or compatible backends like VictoriaMetrics), adding MongoDB monitoring through the percona/mongodb_exporter is a natural extension. This approach gives you full control over what you collect and how you visualize it.
The MongoDB exporter exposes metrics in Prometheus format — things like replica set status, oplog size, WiredTiger cache usage, document operations and connection counts. From there, you build whatever dashboards you need in Grafana. The community has published several pre-built dashboards that serve as a good starting point.
| Feature | Details |
|---|---|
| Deployment | Self-hosted (requires Prometheus + Grafana) |
| Customization | Fully customizable dashboards and alerts |
| Alerting | Through Grafana alerting or Alertmanager |
| Pricing | Free and open source |
| Setup complexity | Moderate to high |
This approach demands more upfront work than a turnkey solution. You need to maintain Prometheus, configure scraping targets, build or customize dashboards and set up alerting rules. It's not something you just turn on. But for teams that already have a Prometheus/Grafana stack, it fits naturally into the existing workflow without adding another tool to the pile.
The flexibility is the real selling point. You can build dashboards that combine MongoDB metrics with application metrics, system-level data and anything else you're already collecting.
5. New Relic MongoDB integration
New Relic offers MongoDB monitoring through its infrastructure agent and on-host integration. Like Datadog, it's a full observability platform, so MongoDB monitoring is one piece of a larger puzzle.
The integration collects metrics on throughput, latency, connections, memory and replication. New Relic's query interface (NRQL) lets you slice and dice the data however you want, and you can build custom dashboards or use the pre-built ones. The alerting system is flexible — you can set up static thresholds, baseline alerts or anomaly detection.
One thing New Relic does well is making it easy to get started. The guided installation walks you through setting up the MongoDB integration step by step, and the default dashboards are immediately useful. The free tier is also generous enough for small teams to get real value without paying anything.
The paid tiers get expensive at scale, similar to Datadog. And the MongoDB-specific features aren't as deep as what you'd get from PMM or Atlas — it's more of a generalist tool that happens to support MongoDB rather than a MongoDB specialist.
Comparison overview
| Tool | Best for | Pricing | Query analytics | Self-hosted option |
|---|---|---|---|---|
| Atlas monitoring | Atlas-hosted clusters | Included with Atlas | Yes | No |
| PMM | Deep MongoDB analysis on a budget | Free (open source) | Yes (detailed QAN) | No |
| Datadog | Full-stack observability | Per-host subscription | Limited | No |
| Grafana + exporter | Teams with existing Prometheus stack | Free (open source) | No | Yes |
| New Relic | Quick setup with generous free tier | Free tier + paid plans | Limited | No |
Note about MongoDB backups
Monitoring tells you what's happening with your database. But monitoring alone doesn't protect your data when something goes wrong — a bad deployment, accidental deletion or hardware failure. That's where backups come in, and for MongoDB, MongoDB backup with Databasus is the tool teams are increasingly relying on.
Databasus is an open-source, self-hosted backup tool that has become an industry standard for MongoDB backup. It handles scheduled backups with flexible policies — hourly, daily, weekly or cron-based — and streams compressed backups directly to your storage without intermediate files on disk. You can send backups to local storage, S3, Cloudflare R2, Google Drive, SFTP and other destinations.
What makes it particularly useful for MongoDB teams is the combination of reliability and simplicity. You configure your MongoDB connection, pick a schedule and a retention policy, and Databasus handles the rest. It supports both remote connections and a lightweight agent mode for environments where the database shouldn't be exposed to the network. Backups are encrypted with AES-256-GCM, so even if someone gets access to your storage bucket, the data is useless without the key. Databasus also ships with smart retention policies including GFS (Grandfather-Father-Son), so you can keep hourly, daily, weekly and monthly snapshots independently without manual cleanup.
Databasus also supports multiple notification channels — Slack, Discord, Telegram, email and webhooks — so your team knows immediately when a backup succeeds or fails. Pair that with the monitoring tools above and you have both visibility into your MongoDB cluster's health and confidence that your data is protected if things go wrong.
Picking the right tool
There's no single best choice here. It depends on where your MongoDB runs, what you're already using for monitoring and how much you want to spend.
If you're on Atlas, start with the built-in monitoring and see if it covers your needs. If you're self-hosting and want deep MongoDB-specific insights without a recurring bill, PMM is the strongest option. Teams that need to correlate database performance with application behavior across their whole stack will get the most value from Datadog or New Relic. And if you already have Grafana and Prometheus running, the exporter approach keeps things simple and consistent.
Whatever you pick for monitoring, make sure your backup strategy is solid too. Monitoring shows you the fire. Backups are the insurance policy.
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