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Inside OpenAI Habitat: scaling online storage past 70M requests per second and migrating from Python to Rust

OpenAI centralized its storage control plane before shifting production traffic to a Rust rewrite, with measured efficiency and latency gains.

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Inside OpenAI Habitat: scaling online storage past 70M requests per second and migrating from Python to Rust

Key takeaways

OpenAI published an engineering account of Habitat on September 11. The company says its online storage platform now handles more than 70 million requests per second, supports products used by over one billion people each week across almost 40 regions, and serves more than 500 petabytes of data. Habitat began as a Python client library for GPTs in 2023, then became a standalone service so deployments, observability, access controls, audit logging, and platform improvements could be managed centrally. In the second quarter of 2026, two engineers used Codex and GPT-5.5 to rewrite the service in Rust. OpenAI reports that the Rust service now handles 95% of production requests and is six times more CPU-efficient and fifteen times more memory-efficient than the Python version. These are internal production measurements, so other teams should adopt the migration discipline rather than assume identical gains.

OpenAI says Habitat processes more than 70 million requests per second, supports products used by over one billion people each week across almost 40 regions, and serves more than 500 petabytes of data.
Habitat launched for GPTs at DevDay 2023 as a Python client library over Azure Cosmos DB, then became a standalone service to centralize deployment, observability, security controls, and platform improvements.
OpenAI reports that two engineers used Codex and GPT-5.5 to complete a Rust rewrite in Q2 2026; it now serves 95% of production requests with measured gains of 6x CPU efficiency and 15x memory efficiency.

Direct answer

OpenAI published an engineering account of Habitat on September 11. The company says its online storage platform now handles more than 70 million requests per second, supports products used by over one billion people each week across almost 40 regions, and serves more than 500 petabytes of data. Habitat began as a Python client library for GPTs in 2023, then became a standalone service so deployments, observability, access controls, audit logging, and platform improvements could be managed centrally. In the second quarter of 2026, two engineers used Codex and GPT-5.5 to rewrite the service in Rust. OpenAI reports that the Rust service now handles 95% of production requests and is six times more CPU-efficient and fifteen times more memory-efficient than the Python version. These are internal production measurements, so other teams should adopt the migration discipline rather than assume identical gains.

Verified facts

OpenAI says Habitat processes more than 70 million requests per second, supports products used by over one billion people each week across almost 40 regions, and serves more than 500 petabytes of data.

Habitat launched for GPTs at DevDay 2023 as a Python client library over Azure Cosmos DB, then became a standalone service to centralize deployment, observability, security controls, and platform improvements.

OpenAI reports that two engineers used Codex and GPT-5.5 to complete a Rust rewrite in Q2 2026; it now serves 95% of production requests with measured gains of 6x CPU efficiency and 15x memory efficiency.

Inside OpenAI Habitat: scaling online storage past 70M requests per second and migrating from Python to Rust cover infographic
ENHE AI original composite: a topic-specific real-work scene with fact-checked editorial copy.

What changed

  • Storage logic moved from distributed clients into a centralized service control plane
  • Tail latency, connection reuse, and downstream protection became explicit operating concerns
  • A constrained NoSQL API preserves predictable work as a scaling boundary
  • The Rust rewrite was validated by progressively serving production traffic
Inside OpenAI Habitat: scaling online storage past 70M requests per second and migrating from Python to Rust team operating flow
A four-step path from announcement to testable, reversible, auditable operations.

Impact for AI users

The account shows why user-perceived speed follows the slowest parts of a request chain rather than average database latency alone. For teams building high-volume AI products, the transferable pattern is to stabilize the protocol, permissions, audit trail, and observability before replacing the runtime. Shadow traffic, progressive percentages, and an explicit rollback point can then test the new implementation. OpenAI's scale, internal tooling, and reported efficiency multiples are specific to Habitat and should not be treated as a forecast for another system.

Operating checklist

  1. Capture a pre-migration baseline for throughput, p95 and p99 latency, event-loop delay, connection counts, and downstream errors.
  2. Stabilize routing, authorization, audit logging, and rate limits behind one protocol, then test compatibility with replayed traffic.
  3. Compare Python and Rust with shadow requests and a small production percentage, expanding only while automatic stop thresholds remain clear.
  4. Preserve the version, metrics, failure samples, and rollback command for each stage, and verify the old path before proceeding.

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FAQ

Is Habitat a database product offered by OpenAI?

No. The article describes Habitat as an internal online storage platform for OpenAI products; this release is an engineering account.

Has OpenAI completely retired the Python service?

Not yet. OpenAI says Rust handles 95% of production requests and that Python will be deprecated over the following weeks.

Can another system assume the same 6x and 15x efficiency gains?

No. Those are internal Habitat measurements, and hardware, workload, protocol, and implementation differences can materially change the result.

Summary

Habitat's central lesson is not merely the choice of Rust. OpenAI first established a common control plane, then let the replacement prove itself progressively with production metrics. That migration order is more reusable than any single efficiency number.

This AI-assisted article is checked by ENHE AI automation for official sources, bilingual fields, media rights, page safety, and historical duplication before publication.

What this means for everyday users

The account shows why user-perceived speed follows the slowest parts of a request chain rather than average database latency alone. For teams building high-volume AI products, the transferable pattern is to stabilize the protocol, permissions, audit trail, and observability before replacing the runtime. Shadow traffic, progressive percentages, and an explicit rollback point can then test the new implementation. OpenAI's scale, internal tooling, and reported efficiency multiples are specific to Habitat and should not be treated as a forecast for another system.

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