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');
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)
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;
Combined with search
Combing embedding generation and similarity search inline in SQL.
One database, many capabilities
Vector search is just the beginning—combine it with advanced analytics to uncover patterns, connect insights, and explore data from every angle in a single platform.
Spatial Analysis
An independently designed and performed Spatial and Time Series Benchmark to help organizations evaluate database technologies suitable for IoT and sensor data workloads.
Real-Time Analytics
An independently designed and performed Spatial and Time Series Benchmark to help organizations evaluate database technologies suitable for IoT and sensor data workloads.
Graph Analytics
An independently designed and performed Spatial and Time Series Benchmark to help organizations evaluate database technologies suitable for IoT and sensor data workloads.
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.
