MongoDB announced Atlas Infinite on September 29, 2026, as a new deployment option within MongoDB Atlas that separates compute from storage so each can scale independently. An Australian engineering team reportedly contributed to its development. The service is in public preview, with important limits on cloud provider, cluster types and features.
What is MongoDB Atlas Infinite?
Atlas Infinite is an Atlas deployment option, not a separate database product. MongoDB says it uses the same document model, drivers and APIs as Atlas Core, its renamed current Atlas offering, and that supported applications can adopt it without code changes. The architecture separates the compute nodes that run queries, transactions and aggregations from a storage layer managed separately by MongoDB. MongoDB describes the launch and architecture.
That separation is intended to let compute and storage respond to different needs: MongoDB says teams can add compute for a spike in demand without changing storage, or increase storage without scaling the cluster tier. It also describes encryption before data leaves a compute node, customer-managed keys through AWS KMS in preview, primary and standby compute nodes, and backups and point-in-time recovery at the storage layer. These are product descriptions, not a guarantee that every workload will scale or perform in the same way.
What did Australian engineers contribute?
An October 2, 2026 iTWire company-news article attributed to MongoDB says the company’s Australian engineering team has more than 70 engineers, was instrumental in Atlas Infinite’s development and is responsible for MongoDB’s global storage engine. It presents the team’s work as part of a product for customers and developers worldwide.
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The coverage does not identify individual engineers’ specific technical contributions or independently verify the team size. Mick Graham, MongoDB’s VP of Engineering, praised the Sydney team and described Atlas Infinite as an example of local data-layer innovation. His remarks represent the company’s view of its own engineering organisation.
What does public preview mean for prospective users?
Public preview means Atlas Infinite is available to try within the documented preview envelope, but it is not equivalent to a generally available service with the same feature coverage and commitments. MongoDB’s Atlas Infinite overview documentation, accessed October 3, 2026, says preview deployments are available on AWS only and have no uptime SLA. The available evidence does not establish whether the preview is supported in an Australian AWS region; check MongoDB’s current AWS region support information before planning a deployment.
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Documented preview limits
According to MongoDB’s documentation accessed October 3, 2026, the preview supports these cluster configurations:
- M10 through M60 general replica sets.
- M40 through M60 low-CPU replica sets.
- Up to 128 TB of logical storage per replica set.
The documentation lists these unsupported features and configurations:
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- Multi-region and multi-cloud clusters, global clusters, sharding, and M80+ tiers.
- Atlas Search and Vector Search, and Online Archive.
- Atlas Kubernetes Operator, cross-edition live migration and restore.
- Azure Key Vault and Google Cloud KMS.
- Charts, MongoDB for VS Code, MongoDB MCP Server and MongoDB Agent Skills.
- An uptime SLA.
Preview support can change. Check the linked MongoDB documentation for the current support matrix before selecting a cluster, planning migration or relying on a feature.
How does Atlas Infinite compare with Atlas Core?
MongoDB positions Atlas Core and Atlas Infinite as options for different workload patterns. Atlas Infinite’s separation of compute and storage is aimed at workloads where demand or data growth does not move in lockstep. Choosing between them requires checking whether the preview supports the features and cluster tier the application needs, and modelling cost against actual compute and storage use. MongoDB says Atlas Infinite pricing is tied to consumption, but the sources cited here do not establish current prices.
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| Decision point | Atlas Core | Atlas Infinite |
|---|---|---|
| Scaling model | MongoDB describes it as the current Atlas offering; the launch comparison contrasts it with Infinite’s independently scalable compute and storage. | Compute and storage are separated and can scale independently, according to MongoDB. |
| Preview constraints | Not stated in the launch comparison. | Public preview; AWS only, with documented tier, feature and SLA exclusions. |
| Cost basis | Not stated in the cited launch material. | MongoDB says pricing is tied to actual compute and storage consumption; current prices are not stated. |
How should performance claims be interpreted?
MongoDB’s product blog reports 189% more throughput per dollar in internal tests comparing Atlas Infinite with Atlas Core. MongoDB cautions that results can vary by workload, deployment, hardware and configuration; this is not a universal performance guarantee. The same blog reports more than 96% less time for read scaling compared with Atlas Core, also a company-reported comparison rather than a promised result for every application. See MongoDB’s product blog for the company’s qualifications and examples.
Customer examples are similarly specific. Mike Wallis, Head of DevOps at Icon Solutions, said the company processed up to 55% more transactions per second than its equivalent previous setup on MongoDB 8.0, without application changes. PicPay’s Database Reliability Engineer, Erick Schroder, said Atlas Infinite helped the platform handle four times its normal peak traffic for two hours with zero failures, and described faster backups and decoupled storage. These are customer-reported outcomes reproduced in MongoDB and iTWire coverage, not independently verified benchmark results. MongoDB’s September 29, 2026 release includes the PicPay example.
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What to check before adopting it
- Confirm regional support. AWS-only preview does not by itself confirm support in an Australian AWS region. Verify the region and configuration in MongoDB’s current documentation.
- Match your requirements to the preview matrix. Check tier, replica-set limits, storage ceiling and any needed search, sharding, multi-region or integration features.
- Plan for preview-level commitments. The documented preview has no uptime SLA, so assess whether that fits the application’s reliability requirements.
- Model the workload and cost. Compare compute demand and storage growth separately, and test the application’s own workload rather than assuming company or customer examples will repeat.
- Validate migration assumptions. MongoDB says supported cases need no application-code changes, but cross-edition live migration and restore are listed as unsupported in the preview documentation. Confirm the intended migration route before committing.
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