Products · Developer platform

Vectorize

Store, query, and manage high-dimensional vector databases

Pricing and limits

Pricing

as of Nov 3, 2026

As of Nov 3, 2026, from Cloudflare's docs, last updated there Oct 30, 2026. Licensed CC BY 4.0; navigation removed.

Vectorize is now Generally Available

To report bugs or give feedback, go to the #vectorize Discord channel ↗︎. If you are having issues with Wrangler, report issues in the Wrangler GitHub repository ↗︎.

Vectorize bills are based on:

  • Queried Vector Dimensions: The total number of vector dimensions queried. If you have 10,000 vectors with 384-dimensions in an index, and make 100 queries against that index, your total queried vector dimensions would sum to 3.878 million ( (10000 + 100) * 384).
  • Stored Vector Dimensions: The total number of vector dimensions stored. If you have 1,000 vectors with 1536-dimensions in an index, your stored vector dimensions would sum to 1.536 million ( 1000 * 1536).
  • Indexes: The Workers Paid plan includes 50,000 indexes per account. Each additional index costs $0.10 per month.

You are not billed for CPU, memory, or "active index hours". If you are not issuing queries against your indexes, you are not billed for queried vector dimensions.

Billing metrics

Workers Free Workers Paid
Total queried vector dimensions 30 million queried vector dimensions / month First 50 million queried vector dimensions / month included + $0.01 per million
Total stored vector dimensions 5 million stored vector dimensions First 10 million stored vector dimensions + $0.05 per 100 million
Indexes 100 Unlimited, with a default limit of 50,000. First 50,000 included + $0.10 per additional index per month.

Calculating vector dimensions

To calculate your potential usage, calculate the queried vector dimensions and the stored vector dimensions, and multiply by the unit price. The formula is defined as ((queried vectors + stored vectors) * dimensions * ($0.01 / 1,000,000)) + (stored vectors * dimensions * ($0.05 / 100,000,000))

  • For example, inserting 10,000 vectors of 768 dimensions each, and querying those 1,000 times per day (30,000 times per month) would be calculated as ((30,000 + 10,000) * 768) = 30,720,000 queried dimensions and (10,000 * 768) = 7,680,000 stored dimensions (within the included monthly allocation)
  • Separately, and excluding the included monthly allocation, this would be calculated as (30,000 + 10,000) * 768 * ($0.01 / 1,000,000) + (10,000 * 768 * ($0.05 / 100,000,000)) and sum to $0.31 per month.

Usage examples

The following table defines a number of example use-cases and the estimated monthly cost for querying a Vectorize index. These estimates do not include the Vectorize usage that is part of the Workers Free and Paid plans.

Workload Dimensions per vector Stored dimensions Queries per month Calculation Estimated total
Experiment 384 5,000 vectors 10,000 ((10000+5000)*384*(0.01/1000000)) + (5000*384*(0.05/100000000)) $0.06 / mo included
Scaling 768 25,000 vectors 50,000 ((50000+25000)*768*(0.01/1000000)) + (25000*768*(0.05/100000000)) $0.59 / mo most
Production 768 50,000 vectors 200,000 ((200000+50000)*768*(0.01/1000000)) + (50000*768*(0.05/100000000)) $1.94 / mo
Large 768 250,000 vectors 500,000 ((500000+250000)*768*(0.01/1000000)) + (250000*768*(0.05/100000000)) $5.86 / mo
XL 1536 500,000 vectors 1,000,000 ((1000000+500000)*1536*(0.01/1000000)) + (500000*1536*(0.05/100000000)) $23.42 / mo

included All of this usage would fall into the Vectorize usage included in the Workers Free or Paid plan.

most Most of this usage would fall into the Vectorize usage included within the Workers Paid plan.

Frequently Asked Questions

Frequently asked questions related to Vectorize pricing:

  • Will Vectorize always have a free tier?

Yes, the Workers free tier will always include the ability to prototype and experiment with Vectorize for free.

  • What happens if I exceed the monthly included reads, writes and/or storage on the paid tier?

You will be billed for the additional reads, writes and storage according to Vectorize's pricing.

  • Does Vectorize charge for data transfer / egress?

No.

  • Do queries I issue from the HTTP API or the Wrangler command-line count as billable usage?

Yes: any queries you issue against your index, including from the Workers API, HTTP API and CLI all count as usage.

  • Does an empty index, with no vectors, contribute to storage?

No. Empty indexes do not count as stored vector dimensions.

Empty indexes still count toward your account's index allocation. Each index above the Workers Paid allocation costs $0.10 per month.

Limits

as of Nov 3, 2026

As of Nov 3, 2026, from Cloudflare's docs, last updated there Oct 30, 2026. Licensed CC BY 4.0; navigation removed.

The following limits apply to accounts, indexes, and vectors:

Need a higher limit?

To request an adjustment to a limit, complete the Limit Increase Request Form ↗︎. If the limit can be increased, Cloudflare will contact you with next steps.

Feature Current Limit
Indexes per account Workers Paid: Unlimited, with a default limit of 50,000. Each index above 50,000 costs $0.10 per month. Free: 100.
Maximum dimensions per vector 1536 dimensions, 32 bits precision
Precision per vector dimension 32 bits (float32)
Maximum vector ID length 64 bytes
Metadata per vector 10KiB
Maximum returned results (topK) with values or metadata 50
Maximum returned results (topK) without values and metadata 100
Maximum upsert batch size (per batch) 1000 (Workers) / 5000 (HTTP API)
Maximum vectors in a list-vectors page 1000
Maximum index name length 64 bytes
Maximum vectors per index 20,000,000
Maximum namespaces per index 50,000 (Workers Paid) / 1000 (Free)
Maximum namespace name length 64 bytes
Maximum vectors upload size 100 MB
Maximum metadata indexes per Vectorize index 10
Maximum indexed data per metadata index per vector 64 bytes
<details> <summary>

Limits for V1 indexes (deprecated)

</summary>
Feature Limit
Indexes per account 100 indexes
Maximum dimensions per vector 1536 dimensions
Maximum vector ID length 64 bytes
Metadata per vector 10 KiB
Maximum returned results (topK) 20
Maximum upsert batch size (per batch) 1000 (Workers) / 5000 (HTTP API)
Maximum index name length 63 bytes
Maximum vectors per index 200,000
Maximum namespaces per index 1000 namespaces
Maximum namespace name length 63 bytes
</details>

Timeline

7 entries since February 2024.

August 2026

  • Vectorize indexes now support up to 20 million vectors

    You can now store up to 20 million vectors in a single Vectorize index, doubling the previous limit of 10 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…

July 2026

  • Reduced end-to-end latency for vector changes

    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 f…

June 2026

  • Billable usage and budget alerts now in product sidebars

    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 das…

March 2026

  • Return up to 50 query results with values or metadata

    You can now set topK up to 50 when a Vectorize query returns values or full metadata. This raises the previous limit of 20 for queries that use returnValues: true or returnMetadata: "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 topK limits.

January 2026

  • Vectorize indexes now support up to 10 million vectors

    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…

August 2025

  • List all vectors in a Vectorize index with the new list-vectors operation

    You can now list all vector identifiers in a Vectorize index using the new list-vectors operation. 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.

April 2025

  • Create fully-managed RAG pipelines for your AI applications with AutoRAG

    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 syst…