Simple, Cost-Effective 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 at no additional cost.
Vector Search, Simplified
Leverage the vector capabilities of the most popular open source databases to enrich your AI queries with more accurate context.
Benefits of leveraging multi-model 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.
Fast Onboarding
Onboard vector queries within the same tooling you already manage and use day-to-day. Reduce the Gen AI time to production.
Performance
Multi-model databases offer the best of both worlds: standard optimization features plus dedicated vector functionality, delivering the same power and performance as dedicated vector solutions.
Trusted 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 vector-enabled 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:
AlloyDB Omni is a high-performance, 100% PostgreSQL-compatible database that excels in handling diverse workloads, including transactional, analytical, and AI-powered vector similarity search.
Benefits include:
- Dedicated ScaNN indexing to boost performance
- Easy integrations with AI models via SQL for embedding calculations and inference
- Plus all of the benefits of PostgreSQL®!
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:
Aiven for Vector Databases: Supercharging Product Search
- Diverse Vector Database Options: Aiven supports a variety of popular open-source vector databases like PostgreSQL® with pgvector, OpenSearch®, ClickHouse®, and Dragonfly, allowing you to choose the best tool for your specific needs and performance requirements.
- Enhanced AI/ML Workflows: Combine the power of vector search with traditional data sources to create comprehensive AI/ML workflows. For example, use vector embeddings generated by your AI model to enhance product listings and enable semantic product search.
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. Utilize hybrid search, which searches on both vector and non-vector data, on multi-model databases to address these challenges. By applying filters that narrow down the search scope, you can ensure more precise and secure results.
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.
Vector search in action
Vector search is revolutionizing how we interact with data. By leveraging the power of embeddings and hybrid search, unlock new possibilities for applications across various industries and solve complex challenges.
Retrieval-Augmented Generation (RAG)
Image Similarity Search
Recommendation Engine Optimization
Tailored Customer Experiences
Smart Search Engines
Anomaly detection
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Enrich AI with vector databases
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 databases.