Pay only for what you use.
Our pricing is simple and transparent with a free 30 day trial with 10 GB storage and 50,000 requests per month tier (worth $255) to get started.
- 5 collections
- Storing Vectors and Metadata
- All encoders
- Facets & filters
- 5 collections
- Dimensionality reduction
- Global Multi-region Replication
- Service SLAs
- Isolated Environment
- Dedicated support
|Breakdown of features||Search||Analytics||Professional||Enterprise|
Encode arrays, dictionaries and fields
Vector Hub Encoding
Access our massive library of encoders and embedding models
Easy to set up filterable vector search
Facets & Filters
Vector search using facets and filters
Compare and evaluate the different performance of your vectors
Read Key Provisioning
Provision and deactivate keys for read only access
Advanced vector search for multivector weighted search
Hybrid Text Search
Combine vector search with traditional search to get the ultimate hybrid search
Perform aggregations to understand more about your data and vectors
Compress, understand and plot your vectors using dimensionality reduction
Clustering & Cluster Aggregation
Group and understand your vectors using clustering and aggregations
Vector Aggregation & Transforms
Aggregate vectors in your collection with groupbys to create new collections
Approximate nearest neighbours for searching through billions and millions of vectors
Customisable Replicas & Shards
Customise the amount of shards and replicas for each collection
Approximate Chunk Search
Approximate nearest neighbours on chunk vector search
Highly Customisable Search
Customise the scoring of search by creating and combining different distance metrics
Vectors designed for explainable A.I
Vectors designed for time series data
Questions & Answers
VecDB is a vector database for storing, searching, comparing and analysing vectors. Built for machine learning and for the cloud. With VecDB you can quickly prototype and productionise vector based applications like search, recommendations, personalisation, anomaly detection, similarity search, and many many more.
Vector embeddings are meaningful numerical representations of rich data in multi-dimensional space. Vectors can be used to represent any kind of data, such as image, text, audio, videos, users, etc. Once data is represented as vectors it is now possible to accurately search and analyse them using machine learning. It is challenging to develop the resources and infrastructure for generating, searching and analysing vectors. But there’s no escaping the fact: you need vector technology to deliver the best recommendation, discovery, search and translation systems. To learn more: https://getvectorai.com/blog/what-are-vectors-the-silent-disrupter/.
Since VecDB is available through an API it can be ran on any device with an active internet connection. VecDB is a managed database in the cloud so you don't have to maintain, update or scale any service.
For Python we have made a SDK to make the experience as seamless as possible. For example, you can insert your pandas dataframe directly into VecDB with our python SDK. However, if you prefer to interact with the API directly you can still do so.