We have greatly improved the throughput of the Vectorize write-ahead log (WAL) ↗. As a result, we have significantly reduced the end-to-end latency for a vector change to become queryable: median latency has dropped from 2 minutes to under 30 seconds, and p99 latency from 5 minutes to under 2 minutes.
This means inserts, upserts, and deletes are reflected in query results faster, improving the freshness of semantic search, recommendation, and retrieval-augmented generation (RAG) workloads. You do not need to change your code or configuration to benefit from this improvement.
For more information, refer to the Vectorize documentation.
Pay-as-you-go customers can now view billable usage and create budget alerts directly from the product overview pages for Workers & Pages, D1, R2, Workers KV, Queues, Vectorize, Durable Objects, and Containers. A new sidebar widget shows current-period spend and the billing cycle date range, alongside a button to create a budget alert.
The widget pulls from the same data as the Billable Usage dashboard and aligns to your billing cycle (or the current day on Free plans), so the numbers match your invoice. Enterprise contract accounts are not yet supported.
Selecting Create budget alert opens the budget alert flow inline so you can set a dollar threshold in the same place you are reviewing usage. Budget alerts apply to your total account-level spend across all products, not just the product page you create them from.
For more information, refer to the Usage-based billing documentation.
You can now set
topKup to50when a Vectorize query returns values or full metadata. This raises the previous limit of20for queries that usereturnValues: trueorreturnMetadata: "all".Use the higher limit when you need more matches in a single query response without dropping values or metadata. Refer to the Vectorize API reference for query options and current
topKlimits.
You can now store up to 10 million vectors in a single Vectorize index, doubling the previous limit of 5 million vectors. This enables larger-scale semantic search, recommendation systems, and retrieval-augmented generation (RAG) applications without splitting data across multiple indexes.
Vectorize continues to support indexes with up to 1,536 dimensions per vector at 32-bit precision. Refer to the Vectorize limits documentation for complete details.
You can now list all vector identifiers in a Vectorize index using the new
list-vectorsoperation. This enables bulk operations, auditing, and data migration workflows through paginated requests that maintain snapshot consistency.The operation is available via Wrangler CLI and REST API. Refer to the list-vectors best practices guide for detailed usage guidance.
AutoRAG is now in open beta, making it easy for you to build fully-managed retrieval-augmented generation (RAG) pipelines without managing infrastructure. Just upload your docs to R2, and AutoRAG handles the rest: embeddings, indexing, retrieval, and response generation via API.
With AutoRAG, you can:
- Customize your pipeline: Choose from Workers AI models, configure chunking strategies, edit system prompts, and more.
- Instant setup: AutoRAG provisions everything you need from Vectorize, AI gateway, to pipeline logic for you, so you can go from zero to a working RAG pipeline in seconds.
- Keep your index fresh: AutoRAG continuously syncs your index with your data source to ensure responses stay accurate and up to date.
- Ask questions: Query your data and receive grounded responses via a Workers binding or API.
Whether you're building internal tools, AI-powered search, or a support assistant, AutoRAG gets you from idea to deployment in minutes.
Get started in the Cloudflare dashboard ↗ or check out the guide for instructions on how to build your RAG pipeline today.