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Serverless vector database built for AWS

Cost-effective vector search purpose made for AWS: Pay only for what you use, optimize your cloud spend.

No credit card required

From prototype to production in a few lines of code

SvectorDB takes care of the heavy lifting so you can focus on scaling your product from 1 vector to 1 million vectors

Quick Start Tutorial
// Create or update an item
key: 'abc',
value: Buffer.from('Hello world!'),
vector: [0.1, 0.1, 0.1, 0.1]

// Query based on a vector
query: {
vector: [0.5, 0.5, 0.5, 0.5]

// Query based on key (nearest to existing vector)
query: {
key: 'abc'

Hybrid Search

Use Lucene / ElasticSearch style queries to filter results based on key-value pairs

Instant updates

Upserts and deletions are reflected instantly, no need to worry about eventual consistency

Natively Serverless

Pay per request based pricing, with no provisioning or scaling required

CloudFormation Support

Integrate SvectorDB into your existing CloudFormation templates

Built-in Vectorizers

Use our built-in vectorizers for text and images, or bring your own embeddings

Pay per request

Only pay for the requests you make, with no minimum fees or upfront costs

We're a micro start-up, and our philosophy centers around transparency. When you reach out to us, you're talking to the people who built the product, not navigating through layers of support agents. In the spirit of transparency, our weaknesses:


While we maintain our own internal backups to protect against data loss, we do not offer the ability to create snapshots of your databases.

Max record limits

To ensure consistent performance, we have a default limit of 1 million records per database. This limit can only be increased by contacting support.

Company Size

We're a micro start-up, while we consider it a benefit as we're hyper-responsive to customers needs, it may be a concern for some customers.

Wikipedia Embeddings

1 million





query latency (average)


recall @ 32

Use Cases

Recommendation Engines

Using vectors to represent items and users, recommendation engines leverage vector similarity to suggest relevant items to users based on their preferences.

Document / Image Search

Transforming documents and images into vectors enables deep meaningful search capabilities by leveraging semantic and visual similarities.

Retrieval Augmented Generation

Augmenting generative models with context enhances the quality of generated content, presenting a more refined and contextually relevant output.

Bring your own embeddings or use our vectorizers

Use our built-in vectorizers for text and images, or bring your own embeddings to leverage the full power of SvectorDB

Learn more
model: EmbeddingModel.ALL_MINILM_L6_V2,
input: {
text: 'The quick brown fox jumps over the lazy dog.',

model: EmbeddingModel.CLIP_VIT_BASE_PATH32,
input: {
image: Buffer.from(...)


$0.25 / GB / month

The total size of your database and indexes, including keys, value, and vectors.


$5 / million

A single query counts as 1 read operation, regardless of the number of results returned or data scanned.


$20 / million

A single put or delete call counts as 1 write operation, regardless of the size of the item.

Free Tier

5k records

Create up to 10 free tier indexes of up to 5k records, with no time limit.

SvectorDB has a free tier

Price Comparison Calculator

Queries per month

Writes per month

Vector Dimension

Vectors Stored





SvectorDB calculations
Queries1m1 Read Operation$5 / million$5.00
Writes100k1 Write Operation$20 / million$2.00
Storage500k vectors~1.018 GB$0.25 / GB$0.25
Pinecone calculations
Queries*1m10 Read Units$8.25 / million$82.50
Writes100k3 Write Units$2.00 / million$0.60
Storage500k vectors~1.018 GB$0.33 / GB$0.34

* Fetching 32 nearest neighbours and returning metadata

Ready to experience the difference?

No credit card required