Performance
Traditional databases with dedicated vector extensions provide the same functionality and performance as dedicated vector solutions.
Enrich AI With
Vector Databases
Leverage the vector capabilities of the most popular open source databases to enrich your AI queries with more accurate context.
Benefits of leveraging traditional databases for vector search
Leverage built-in capabilities of your existing databases for efficient vector search and eliminate the complexities associated with migrating to a new database.
Simple Architecture
Reduce your infrastructure complexity by increasing the usage of existing technology. Avoid data duplication by moving the vector functionalities to where your data already resides.
Fast Onboarding
Onboard vector queries within the same tooling you already manage and use day-to-day. Reduce the Gen AI time to production.
Mature Technology
While dedicated vector databases might offer slightly newer functionalities, they often lack the support needed for enterprise-grade workloads.
Single Pane of Glass
Maintain strict control over your data assets, including their locations and security measures.
Portability
Rely on the best open source technology and move your data to where AI is in a secure, reliable, and scalable architecture.
Which database is right for you?
Navigating the world of vector databases doesn't have to be complex. Let's explore the unique strengths of each option for your vector search needs.
PostgreSQL®, the most popular database, offers vector capabilities with PGVector.
Benefits include:
- Performance at scale
- Storage of operational data: embrace vector search without duplicating the data
- Dedicated indexing and performance accelerating features
- Advanced features for hybrid queries
Learn more here:
ClickHouse®, the fastest analytical database, offers dedicated vector capabilities.
Benefits include:
- Parallelized query pipeline: performance at scale for analytical workloads
- Vector, hybrid, and aggregation search
- Horizontally scalable
- Columnar format (and therefore data compression)
- Tiered storage: can read data from cold storage for unlimited data retention
Learn more:
OpenSearch®, the leading search engine, provides native vector datatypes and search functionalities.
Benefits include:
- Easy to use
- Wide vector search algorithm selection
- Wide range of text search functionality, enabling performant hybrid search
- Horizontally scalable
Learn more here:
Dragonfly is an in-memory database for fast vector search.
Benefits include:
- Fast vector search for in-memory data
- Easy to integrate with existing data stores
Learn more:
Enhance your vector search accuracy with Hybrid Search
Standalone vector similarity search can be limited by its imprecise nature, especially when dealing with regulated or sensitive data. Hybrid search overcomes these challenges by allowing organizations to define filters that refine the search space, ensuring more accurate and secure results. Explore the differences below and why Aiven's hybrid search capabilities may be a good fit for your organization.
Advantages
- Single dimension to search: Compacted information representation enables you to use a single embedding calculation to create a simplified, compact version of the data.
- Advanced indexing capabilities: Most vector indexes have advanced features only if the query is not filtering out any components in the space. In this case, they offer high speed-up capabilities.
Drawbacks
- High cardinality computation: Each vector requires mathematical calculations over all of its elements. For example, with a dimensionality of 1000, every new line in the dataset requires 1000 additional calculations, leading to slower performance.
- Semantic similarity only: You cannot easily (or accurately) perform additional filtering on specific qualities of a document/object since semantic search will retrieve the closest vectors and may ignore the hard filters you want to define (e.g., closest vectors might exceed the budget).
- Indexing is a tradeoff between speed and accuracy: Better indexing increases the chance of not retrieving the correct set of similar documents/objects.
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Blog
Your database is more powerful than you think. Learn how built-in vector capabilities can power your GenAI applications and save you from the hassle of adopting a new database.
Vector search, simplified
Ready to harness the power of vector databases without the hassle of a migration? Leverage the familiar tools you already use on Aiven to accelerate your AI projects. Experiment with our platform today and discover the potential of your existing database.