OpenAI Scales Habitat with Multi‑Tenant Azure Cosmos DB

Introduction

OpenAI’s online storage platform, Habitat, is the backbone that delivers sub‑millisecond data access to more than one billion ChatGPT users in 40 geographic regions. As the user base exploded, Habitat faced the classic scaling dilemma: maintain reliability while delivering the performance needed for a global AI service. The engineering team turned to Azure Cosmos DB as the core storage engine, but achieving the required throughput and multi‑tenant reliability demanded a systematic re‑evaluation of design choices, workload isolation, and operational practices.

This article, Part II of a two‑part series, outlines the three‑phase journey Habitat undertook with Azure Cosmos DB—from relying on basic primitives, through experiments with single‑tenant isolation, to adopting a co‑managed multi‑tenant container model. The narrative highlights the trade‑offs, the engineering rigor applied, and the concrete patterns that enabled a 12× improvement in read latency while supporting over 500 petabytes of data.

Phase‑wise Evolution of Habitat’s Storage Layer

The evolution can be grouped into three distinct phases, each addressing a specific set of constraints and objectives.

• Phase I – Leveraging Azure Cosmos DB primitives. Early on, Habitat operated with a single default container per Azure Cosmos DB account. The team relied on logical partition keys exposed directly to clients, assuming the built‑in 10 K RU/s limit per logical partition would provide back‑pressure. This approach worked for modest traffic but quickly revealed weaknesses: frequent collisions on popular keys such as user_id and workspace_id, noisy‑neighbor effects, and uneven partition distribution that led to weak partitions.
• Phase II – Exploring workload isolation and single‑tenancy. To mitigate the reliability issues of a shared container, Habitat experimented with provisioning separate, single‑tenant backing containers for each product surface. While this reduced blast radius and simplified ownership, it introduced new challenges. Capacity planning became fragmented, leading to over‑provisioning and idle resources. The lack of stable aggregate growth made it difficult to predict scaling needs, prompting the team to look for a more balanced solution.
• Phase III – Returning to multi‑tenancy by default with advanced controls. Collaborating closely with the Azure team, Habitat adopted a co‑managed container model that retains the benefits of multi‑tenancy while adding granular controls. Key techniques include hot‑key throttling, Quality of Service (QoS) policies, serverless mode for bursty workloads, and data movement capabilities to rebalance load across tenants. The result is a highly reliable, globally distributed storage layer that can sustain massive read throughput.

Throughout this journey, Habitat continuously evaluated trade‑offs: reliability first, then speed, and finally massive‑scale operability. The pattern guided decisions ranging from partition key selection to caching strategies and proxy optimization.

Key Engineering Practices for Reliable Multi‑Tenancy

Several engineering practices emerged as critical for maintaining stability and performance at scale.

• Logical partition key design. By exposing partition key selection to clients early, Habitat experienced collisions that degraded throughput. The team learned to centralize partition key selection, using deterministic patterns that spread load evenly across logical partitions.
• Noisy‑neighbor mitigation. Multi‑tenant environments inevitably host workloads that can dominate resources. Habitat implemented hot‑key throttling and QoS policies to cap per‑tenant consumption, ensuring fair share for all tenants.
• Co‑managed containers. Instead of each product surface owning its own backing container, Habitat now uses a shared container with co‑management responsibilities. This reduces operational overhead while preserving isolation through provisioned throughput and access controls.
• Global caching and data placement. To achieve 12× faster reads, Habitat stores data close to users wherever capacity permits. Only the smallest, latency‑sensitive datasets are broadly replicated; all other data relies on Azure Cosmos DB’s integrated caching layer.
• Rust‑based proxy optimization. Reducing proxy overhead was a focal point. By rewriting the proxy layer in Rust, Habitat decreased latency and improved throughput, allowing more requests to be processed within the same RU budget.
• Change Data Capture (CDC) and streaming. Habitat leverages CDC pipelines feeding into services such as Databricks, Rockset, and Kafka. This enables near‑real‑time analytics and downstream processing without impacting the primary storage performance.

Collectively, these practices illustrate a pragmatic approach to multi‑tenant storage: start simple, iterate based on observed failures, and gradually introduce sophisticated controls as scale demands.

What Defenders and Engineers Should Do Now

For organizations facing similar growth trajectories, the Habitat experience offers actionable guidance:

• Start with primitives, then iterate. Begin by using the native sharding and throughput controls of your database service. Document collision patterns and monitor partition distribution to identify early signs of imbalance.
• Plan for multi‑tenant isolation early. Even if you begin with single‑tenant deployments, anticipate the operational cost of managing many isolated instances. Design a governance model that defines who owns capacity planning and how shared resources will be allocated.
• Implement QoS and throttling. Define per‑tenant throughput limits and automated scaling triggers. This prevents a single workload from degrading service for others.
• Optimize data placement and caching. Identify latency‑critical datasets and replicate them strategically. Rely on the database’s built‑in caching where possible, and consider edge caching for read‑heavy workloads.
• Use efficient proxy layers. If your current proxy implementation is a bottleneck, evaluate language and runtime choices. Rust, Go, or other high‑performance languages can reduce overhead and improve request handling.
• Integrate CDC for observability. Streaming changes to analytics platforms provides visibility into usage patterns, helping capacity planners anticipate future spikes.

By adopting these practices, engineering teams can build storage platforms that scale gracefully, remain resilient against noisy neighbors, and continue to deliver the low‑latency experience required by modern AI services.

Conclusion

OpenAI’s Habitat platform demonstrates that scaling storage for a global AI service is achievable through disciplined engineering, close collaboration with cloud providers, and iterative refinement of multi‑tenant architectures. The journey from simple containers to sophisticated, co‑managed environments provides a roadmap for any organization preparing to serve massive user bases.

When planning your own storage evolution, remember that reliability, speed, and scale must be addressed in sequence, and that built‑in database primitives can often eliminate the need for over‑engineered custom solutions.

Should you need assistance designing resilient, multi‑tenant storage or integrating advanced DevSecOps practices into your cloud infrastructure, Grivyonx’s AI automation and cloud security services can help you implement patterns similar to those pioneered by Habitat, ensuring your platform remains both performant and secure as it grows.