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ATLAS

Scale your database with ease

Scale your MongoDB Atlas clusters with zero downtime. Leverage auto-scaling, sharding, and vertical scaling to easily handle any workload.

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Zero downtime vertical scaling

Add CPU power, RAM, and network throughput with a rolling upgrade that keeps your application online. Vertical scaling is ideal for steady increases in workload or complex queries that require more memory to keep "hot" data in RAM.

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Zero downtime vertical scaling illustration
Unlimited horizontal scaling illustration

Unlimited horizontal scaling

Automatically partition and distribute data across multiple clusters (shards). Sharding is the best path for applications with massive data or write volumes that a single server simply cannot handle.

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Set-and-forget auto-scaling

Monitor workloads in real time and automatically scale your clusters up or down based on application needs. Choose between reactive, predictive, or storage auto-scaling to maintain performance without over-provisioning.

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Set-and-forget auto-scaling illustration

Auto-scaling in action

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Coinbase logo
60%Reduction in scaling time
3.25xReduction in cluster deployment time
Financial Technology
“We’ve brought scaling times down from upward of 70 minutes to 25 minutes—less than half the time it used to take.”
Sean Hurley
Staff Software Engineer, Coinbase
Read Case Study
Coinbase logo
60%Reduction in scaling time
3.25xReduction in cluster deployment time
Financial Technology
“We’ve brought scaling times down from upward of 70 minutes to 25 minutes—less than half the time it used to take.”
Sean Hurley
Staff Software Engineer, Coinbase
Read Case Study

Auto-scaling resources

Master MongoDB auto-scaling. Find videos, tutorials, and case studies to understand horizontal, vertical, and zero-downtime scaling.

FAQ

Ready to Build Bigger?

MongoDB Atlas makes scaling as easy as setting the right configuration. Start building on a platform that grows with you.
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WHY SCALE ON ATLAS?
  • Multi-region availability
  • Zero-downtime upgrades
  • Automated sharding
  • Optimized storage scaling
  • Elastic resource limits