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Instant Search At Scale

Kinetica delivers lightning-fast vector search on real-time data by combining brute-force search for new, unindexed vectors with index-based search for stored data. Unlike traditional vector databases that require time-consuming indexing before queries can run, Kinetica lets you search instantly—so your latest data is always live, searchable, and actionable within seconds.

Real-Time Vector Similarity Search with Kinetica

Minimal data latency

Near zero data latency and outstanding query throughput.

GPU accelerated

NVIDIA’s RAPIDS cuVS for outstanding vector search performance.

Horizontal scaling

Linearly scale to handle billions of embeddings.

Pg Vector Compliant

Hybrid analytics. Fully integrated with SQL for powerful.

Enterprise Grade Security 

Role-based access control down to every cell of data.

Graph and Relational Query

Vector search using weighted graph and relational query.

Kinetica: Unmatched Data Latency and Exceptional Query Performance

5-14x Better Data Latency

Kinetica outperforms its competitors by achieving significantly faster load times, demonstrating its efficiency in handling large-scale datasets with millions of vectors. This makes Kinetica a strong choice for latency-sensitive applications.

Superior query throughput and accuracy

Kinetica ranks second in query latency, just behind Milvus, but outperforms Milvus with a higher recall accuracy. This combination of strong query performance and exceptional accuracy makes Kinetica a reliable choice for tasks requiring both speed and precision.

Ease of use with SQL

Set up your remote model, generate embeddings with it, and perform search on those embeddings—all using SQL.

CREATE or REPLACE MODEL openai_model
WITH OPTIONS (
remote_model_name = 'text-embedding-3-small',
remote_model_location='https://api.openai.com/v1/embeddings',
credential = 'openai_credential');
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Choose your remote model

Use Kinetica’s default embedding model, or configure your own (e.g. OpenAI, NVIDIA NIM)

GENERATE_EMBEDDINGS(
embedding_table => input_table(select top 100 from emb_demo.fine_food_reviews), 
embedding_input_columns => 'Summary, Text',
embedding_output_columns => 'Summary_emb, Text_emb',
dimensions => 1536)
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Generate embeddings

New table function to automatically generate embeddings in Kinetica

SELECT COSINE_DISTANCE(p.Text_emb, q.query_emb) AS dist, p.ProductId, p.UserId, p.Summary, p.Text
FROM  fine_food_reviews_emb AS p, TABLE (GENERATE_EMBEDDINGS(embedding_table => INPUT_TABLE(SELECT 'healthy food' AS query),
            embedding_input_columns => 'query',  
            embedding_output_columns => 'query_emb',
            dimensions => 1536)) AS q
ORDER BY 1;
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Combined with search

Combing embedding generation and similarity search inline in SQL.

Talk to Us!

The best way to appreciate the possibilities that Kinetica brings to high-performance real-time analytics is to see it in action.

Contact us, and we’ll give you a tour of Kinetica. We can also help you get started using it with your own data, your own schemas and your own queries.