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MongoDB’s Atlas Infinite, announced on September 29, 2026, is a new deployment option in MongoDB Atlas that separates compute from storage so each can scale independently. An October 2 iTWire company-news article attributed to MongoDB says its Australian engineering team—more than 70 people—contributed to the launch and is responsible for the company’s global storage engine. Atlas Infinite is in public preview; MongoDB’s documentation currently limits preview deployments to AWS and excludes several features and configurations that may matter to production workloads.
What is MongoDB Atlas Infinite?
Atlas Infinite is a deployment option within MongoDB Atlas, not a separate database product. MongoDB says it uses the same document model, drivers and APIs as Atlas Core, the company’s renamed current Atlas offering. For supported workloads, MongoDB says adopting Infinite does not require application-code changes. The two options are intended for different workload needs, so compatibility and preview restrictions should be checked before choosing Infinite.
The architectural difference is that compute and storage are decoupled. Compute nodes handle application queries, transactions and aggregations; MongoDB manages storage as a separate layer. MongoDB says this lets teams add compute to respond to a workload spike without changing storage, or increase storage without scaling the compute cluster tier. Its product materials also describe encryption before data leaves a compute node, customer-managed keys through AWS KMS in preview, a primary and standby compute node, and backup and point-in-time recovery at the storage layer. See MongoDB’s product announcement and the Atlas Infinite overview documentation for the design details.
What did Australian engineers contribute?
iTWire’s October 2, 2026 article, labeled company news and attributed to MongoDB, reports that the Australian engineering team has grown to more than 70 engineers, contributed to Atlas Infinite’s development and is responsible for MongoDB’s global storage engine. The account describes work with worldwide impact, but does not name individual engineers or specify which components they built. The team-size and responsibility claims should therefore be understood as company-reported information, not independently verified findings.
Mick Graham, MongoDB’s VP of Engineering, told iTWire: “Australian organisations can only work with AI safely and reliably if their underlying data infrastructure is built for continuous, real-time adaptation at a massive scale. That is a monumental technical hurdle, and overcoming it demands elite, hyper-specialised engineering talent. We’re so proud of the uniquely skilled team we have in Sydney. Atlas Infinite is the latest example of the consistently cutting-edge data-layer innovation our local team delivers.”
Is Atlas Infinite available in Australia?
MongoDB’s documentation describes the product as a public preview available on AWS only. That establishes a cloud-provider restriction, not support in any particular Australian AWS region. The cited sources do not confirm Australian-region availability; check MongoDB’s current Atlas Infinite documentation and AWS region support before planning a deployment.
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What are the public-preview limits?
The documented preview covers general replica sets on M10 through M60, low-CPU replica sets on M40 through M60, and up to 128 TB of logical storage per replica set. MongoDB’s documentation lists these material exclusions and constraints:
- No multi-region or multi-cloud clusters, global clusters, or sharded clusters.
- No M80-or-higher tiers.
- No Atlas Search or Vector Search, or Online Archive.
- No Atlas Kubernetes Operator; no cross-edition live migration or restore.
- No Azure Key Vault or Google Cloud KMS for customer-managed encryption keys in the preview.
- No Charts, MongoDB for VS Code, MongoDB MCP Server, or MongoDB Agent Skills.
- No uptime SLA.
These restrictions can rule out Infinite even when its scaling model fits a workload. The preview documentation is the authority for current limits; verify it before committing to a migration or production design.
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1Scan for outdated or missing drivers - takes under a minute2Clear out junk files and repair common Windows errors3Fix the driver behind crashes, sound loss and screen glitchesHow do the performance claims compare?
MongoDB’s numbers are useful as vendor-reported examples, not universal performance promises. Its product blog reports 189% more throughput per dollar in internal tests comparing Atlas Infinite with Atlas Core, and says results may vary with workload, deployment, hardware and configuration. MongoDB also reports more than 96% less time for read scaling compared with Atlas Core. Neither figure establishes the outcome an individual deployment will achieve.
Customer examples are similarly specific. MongoDB quotes Icon Solutions’ Head of DevOps, Mike Wallis, saying the company processed up to 55% more transactions per second than its equivalent previous MongoDB 8.0 setup, with no application changes. PicPay’s Database Reliability Engineer, Erick Schroder, describes Infinite as enabling growth with simpler operations and order-of-magnitude faster backups; MongoDB separately reports that PicPay handled four times its normal peak traffic for two hours with zero failures. These are customer and company-reported outcomes, not independent benchmark results or guarantees for other users. Read the figures in MongoDB’s product blog and its September 29, 2026 announcement.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.When should a team consider Atlas Infinite?
Infinite is worth evaluating when compute demand and data growth vary independently—for example, when traffic surges but stored data does not, or when a dataset grows without a matching increase in query load. It is not an automatic upgrade: confirm the necessary tier, region, feature set, migration path and operational requirements against the preview restrictions. MongoDB says pricing is tied to actual compute and storage consumption, but the cited materials do not establish current prices. Compare total cost using the workload’s real usage pattern rather than assuming decoupling will always reduce spend.
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