Vector Search, Simplified

Leverage the vector capabilities of the most popular open source databases to enrich your AI queries with more accurate context.

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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.

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.

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.

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

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.

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