Serverless vector database built for AWS
Cost-effective vector search purpose made for AWS: Pay only for what you use, optimize your cloud spend.
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")
))
SvectorDB has an official OpenAPI specification, allowing you to use any language you like.
Visit our supported clients section to get started.
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:
Snapshots
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
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.
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(...))
))
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.
Price Comparison Calculator
Queries per month
Writes per month
Vector Dimension
Vectors Stored
SvectorDB
$7.25/m
![](/img/pinecone.png)
Pinecone
$83.44/m
SvectorDB calculations
Feature | Amount | Units | Cost | Total |
---|---|---|---|---|
Queries | 1m | 1 Read Operation | $5 / million | $5.00 |
Writes | 100k | 1 Write Operation | $20 / million | $2.00 |
Storage | 500k vectors | ~1.018 GB | $0.25 / GB | $0.25 |
$7.25 |
Pinecone calculations
Feature | Amount | Units | Cost | Total |
---|---|---|---|---|
Queries* | 1m | 10 Read Units | $8.25 / million | $82.50 |
Writes | 100k | 3 Write Units | $2.00 / million | $0.60 |
Storage | 500k vectors | ~1.018 GB | $0.33 / GB | $0.34 |
$83.44 |
* Fetching 32 nearest neighbours and returning metadata
Ready to experience the difference?