Most organizations are sitting on vast troves of unstructured data: think documents, images, audio, and videos. While these resources hold tremendous insights, extracting value from them at scale has long been held back by limits on speed, scalability, and cost.
Databricks’ new storage-optimized endpoints for Mosaic AI Vector Search are set to change the game, making large-scale, AI-powered applications more accessible and affordable than ever before.
Breakthrough Innovations in Vector Search
Unprecedented Scalability
With storage-optimized endpoints, businesses can now index and search across billions of vectors, enabling petabyte-scale operations. This removes the need for costly sampling or filtering, letting enterprises tap into their full data lakes for production-grade AI solutions.Major Cost Savings
By decoupling storage from compute and leveraging Spark-based parallelism, Databricks slashes infrastructure costs by up to 7x versus previous solutions. For example, handling 1.3 billion vectors can now cost just $7,000 per month, compared to $47,000 previously. This breakthrough makes large-scale generative AI (GenAI) applications financially viable.Accelerated Indexing Speeds
Building a billion-vector index now takes less than eight hours, with smaller sets ready in just minutes. Customers have seen indexing times drop from seven hours to one, letting teams innovate and iterate at unprecedented speed.Familiar SQL-Style Filtering
Metadata filtering is as simple as using SQL, so teams can refine search results without learning new query languages or writing custom logic. This streamlines workflows and lowers the barrier to entry.Easy Integration
These endpoints serve as drop-in replacements with unchanged APIs, so migration is seamless. Teams don’t need to rewrite code or retrain staff, making adoption quick and painless.Enterprise-Grade Governance
Unity Catalog ensures robust data governance, with access controls, audit trails, and lineage capabilities. This keeps compliance and security front and center from the outset.
Workflow Enhancements for Modern Teams
Rapid RAG Prototyping: Teams can swiftly test and deploy knowledge-based chat agents in the Agent Playground, using vector search indices as tools. This means moving from prototype to production without custom code or complex integrations.
Clear Cost Management: With endpoint budget policy tagging, platform owners and FinOps teams gain clear visibility into usage and spending. This helps allocate budgets and manage costs as workloads grow. Even more detailed tagging features are on the way.'
What’s Next: Future-Proofing Enterprise AI
- Scale-to-Zero: Automatic downscaling of compute resources when idle will further reduce costs.
- High QPS Support: Infrastructure upgrades are coming to support thousands of queries per second, enabling real-time, high-demand applications.
- Beyond Semantic Search: Efficient keyword-only retrieval will expand supported use cases, making the system even more versatile.
The Takeaway: A New Era for Enterprise AI
Databricks’ storage-optimized endpoints mark a significant leap forward for organizations aiming to harness massive unstructured datasets. By merging scale, speed, cost-efficiency, and seamless integration, Databricks empowers enterprises to confidently build advanced AI solutions, like retrieval-augmented generation, entity resolution, and semantic search, directly on their data lakes. With continued enhancements and deep integration into the Databricks Data Intelligence Platform, the future of scalable vector search is brighter than ever.
Databricks’ Storage-Optimized Endpoints Revolutionize Vector Search at Scale