{"slug": "how-alloydb-scann-scales-vector-search-to-10-billion-vectors", "title": "How AlloyDB ScaNN scales vector search to 10 billion vectors", "summary": "Google's AlloyDB ScaNN index now scales vector search to 10 billion vectors, a major architectural enhancement featuring a four-level tree (preview) that reduces compute intensity and improves memory efficiency. The new design achieves query complexity of O(N^1/4) and uses balanced tree shape and sampling optimization to overcome previous bottlenecks at two- and three-level configurations.", "body_md": "To satisfy the demands of enterprise-grade agentic AI applications, underlying vector databases often struggle to scale effectively as modern use cases can scale to billions of vectors.\n\nAs a fully managed PostgreSQL-compatible database service, [AlloyDB](https://docs.cloud.google.com/alloydb/docs/overview) is engineered to handle demanding enterprise workloads. Combining Google's infrastructure with the reliability of commercial databases, it delivers high availability, scalability, and includes a cutting-edge analytical engine, optimal for agentic AI use cases. A key part of this is its [ScaNN index](https://docs.cloud.google.com/alloydb/docs/ai/create-scann-index), which now operates efficiently at a scale of 10 billion vectors. This was achieved through a major architectural enhancement: an innovative [four-level tree (preview)](https://docs.cloud.google.com/alloydb/docs/ai/create-scann-index#four-level-tree-index) paired with efficient memory usage**.**\n\nScaling to a 10 billion vector workload presents significant memory and computational challenges. Previous AlloyDB ScaNN tree-based index was limited to [two](https://docs.cloud.google.com/alloydb/docs/ai/create-scann-index#two-level-tree-index)- or [three](https://docs.cloud.google.com/alloydb/docs/ai/create-scann-index#three-level-tree-index)-level tree configurations, and attempting to scale those structures led to several bottlenecks:\n\n**Increased compute intensity:** Larger tree structures demand significantly more operations for both index construction and query traversal.\n\n**Memory constraints:** The sampling processes required for 10 billion vectors can easily exceed the system's available memory capacity.\n\nThe introduction of a [four-level tree (preview)](https://docs.cloud.google.com/alloydb/docs/ai/create-scann-index#four-level-tree-index) is the primary innovation in the recent AlloyDB ScaNN release. This architecture, illustrated in Figure 1, employs a top-down strategy to optimize the balance between accuracy and build efficiency. To maintain high performance and mitigate recall loss, the system integrates key enhancements such as Top-K branch, [SOAR](https://research.google/blog/soar-new-algorithms-for-even-faster-vector-search-with-scann/#:~:text=ScaNN%20is%20open%2Dsourced%20on%20GitHub%20and%20can%20be%20easily%20installed%20via%20Pip.), [centroid adjustment](https://arxiv.org/abs/1908.10396) and balanced tree shape.\n\nThis design has two primary benefits:\n\n**1. Reduced compute intensity via hierarchical partitioning**\n\nThe four-level architecture drastically reduces compute intensity by using hierarchical partitioning to restrict the volume of vectors scanned during a query. Instead of traversing a flat or poorly segmented space, the multi-layered hierarchy narrows down the search path exponentially. Figure 2 illustrates the search spaces across different tree levels, demonstrating how structural layering optimizes traversal efficiency:\n\n**Two-level:** Utilizes coarse partitioning to guide queries, resulting in a basic search complexity of *O(N 1/2*\n\n**Three-level:** Introduces an intermediate layer to further subdivide clusters, narrowing exploration to *O(N 1/3*\n\n**Four-level:** Implements refined, highly granular partitions that optimize traversal efficiency down to *O(N 1/4*\n\nBy dynamically expanding hierarchical layers as the dataset expands, AlloyDB ScaNN maintains ultra-low query latency and avoids computational scale walls from impacting performance.\n\n**2. Efficient memory usage**\n\nAchieving a 10 billion vector scale requires high memory efficiency. AlloyDB ScaNN uses these strategies to maximize memory management performance:\n\n**Balanced tree shape construction:** The four-level tree utilizes a balanced configuration to circumvent memory limitations that restrict the size of training datasets. This balanced architecture effectively leverages reduced sampling sizes to construct high-fidelity tree partitions.\n\n**Sampling optimization:** When the system encounters memory limitations, it generates a condensed sampling set that considers performance and accuracy.\n\nBy leveraging the innovative four-level tree architecture in our internal tests, we are able to achieve the following performance results:\n\nAlloyDB can scale to over 10 billion vectors with its ScaNN index.\n\nAlloyDB can deliver <= 51 ms p95 latency and 95% recall at 10 billion vectors with its ScaNN index.\n\nExperience AlloyDB ScaNN's [four-level tree (preview)](https://docs.cloud.google.com/alloydb/docs/ai/create-scann-index#four-level-tree-index) architecture today. You can deploy ScaNN for AlloyDB by following our [quickstart guide](https://cloud.google.com/alloydb/docs/quickstart/create-and-connect) to set up an instance. For optimized, high-speed vector search, refer to the [official ScaNN documentation](https://docs.cloud.google.com/alloydb/docs/ai/create-scann-index). New users can also [explore AlloyDB](https://console.cloud.google.com/alloydb/create-trial-cluster?_gl=1*qsd2cd*_up*MQ..&gclid=CjwKCAjwooq3BhB3EiwAYqYoEh91xxGzv4xrmyMJJ_BPfF4X8cv-I3kINwvnMI2pADozFQPsrHnaOhoCbioQAvD_BwE&gclsrc=aw.ds) through our [30-day free trial](https://cloud.google.com/blog/products/databases/run-your-postgresql-database-in-an-alloydb-free-trial-cluster) program. We can’t wait to hear about what you build!", "url": "https://wpnews.pro/news/how-alloydb-scann-scales-vector-search-to-10-billion-vectors", "canonical_source": "https://cloud.google.com/blog/products/databases/alloydb-scann-index-four-level-tree-improves-vector-search/", "published_at": "2026-08-20 16:00:00+00:00", "updated_at": "2026-08-20 16:13:38.721293+00:00", "lang": "en", "topics": ["artificial-intelligence", "machine-learning", "ai-infrastructure", "ai-products"], "entities": ["Google", "AlloyDB", "ScaNN"], "alternates": {"html": "https://wpnews.pro/news/how-alloydb-scann-scales-vector-search-to-10-billion-vectors", "markdown": "https://wpnews.pro/news/how-alloydb-scann-scales-vector-search-to-10-billion-vectors.md", "text": "https://wpnews.pro/news/how-alloydb-scann-scales-vector-search-to-10-billion-vectors.txt", "jsonld": "https://wpnews.pro/news/how-alloydb-scann-scales-vector-search-to-10-billion-vectors.jsonld"}}