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Transparent Pricing

Take the guesswork out of pricing with our simple, transparent pricing model

Pinecone

Dimensions	Records	Results	Cost / million queries
384	100,000	25	$57.50
384	100,000	50	$82.50
768	1,000,000	50	$123.75

vs

SvectorDB

Dimensions	Records	Results	Cost / million queries
384	100,000	25	$5.00
384	100,000	50	$5.00
768	1,000,000	50	$5.00

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

JavaScript
Python

client.embed({
    model: EmbeddingModel.ALL_MINILM_L6_V2,
    input: {
        text: 'The quick brown fox jumps over the lazy dog.',
    },
})

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

client.embed(EmbedInput(
    model=EmbeddingModel.ALL_MINILM_L6_V2,
    input=EmbeddingInputText("The quick brown fox jumps over the lazy dog.")
))

client.embed(EmbedInput(
  database_id=databaseId,
  model=EmbeddingModel.CLIP_VIT_BASE_PATH32,
  input=EmbeddingInputImage(bytes(...))
))

High availability

Databases are automatically replicated across multiple availability zones

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

Wikipedia Embeddings

1 million

vectors

768

dimensions

9ms

query latency (average)

97.4%

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.

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

JavaScript
Python
OpenAPI / Other

// Create or update an item
client.setItem({
    databaseId,
    key: 'abc',
    value: Buffer.from('Hello world!'),
    vector: [0.1, 0.1, 0.1, 0.1]
});

// Query based on a vector
client.query({
    databaseId,
    query: {
        vector: [0.5, 0.5, 0.5, 0.5]
    }
});

// Query based on key (nearest to existing vector)
client.query({
    databaseId,
    query: {
        key: 'abc'
    }
});

# Create or update an item
client.set_item(SetItemInput(
  database_id=databaseId,
  key="abc",
  value="Hello world!".encode(),
  vector=[0.1, 0.1, 0.1, 0.1]
))

# Query based on a vector
client.query(QueryInput(
  database_id=databaseId,
  query=QueryTypeVector([0.5, 0.5, 0.5, 0.5])
))

# Query based on key (nearest to existing vector)
client.query(QueryInput(
  database_id=databaseId,
  query=QueryTypeKey("abc")
))

Storage

$0.25 / GB / month

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

Queries

$5 / million

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

Writes

$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 to allow for experimentation and prototyping

Ready to experience the difference?

Get Started Read the Docs