Vector Store
Chunk, embed, stored and retrieved documents, posts, and images in a fast, scalable, performant and cost-efficient manner. Build Retrieval Augmented Generation (RAG) applications by combining Vector Store with LLM APIs. Add new documents to the vector store instantly or at scheduled intervals, ensuring it remains continually updated.
Easy and intuitive to set and use
Enterprise-grade security
Highly performant, scalable, fast and cost-efficient
The Abacus.AI solution can support any nearest neighbor algorithm, incorporates state-of-the-art efficiency techniques and can deliver 100% recall. It is the ONLY solution of its kind in the industry. Leverage vector store for ad-targeting, personalization, image classification or LLM based applications.

 
Pricing
VECTOR STORE
POD TYPE PRICE/POD-HOUR CAPACITY OPTIMIZED FOR
Storage optimized Pod $0.10 Upto 5M Vectors Infrequent retrieval of large data sets
Performance optimized Pod $0.10 Upto 1M Vectors Frequent retrieval of smaller data sets
The capacity can be scaled horizontally by adding more pods
  • PERFORM EFFICIENT SEARCHES ON VECTOR EMBEDDINGS 
  • BUILD SOPHISTICATED DEEP LEARNING MODELS 
Benefits
Enterprise Ready
Abacus.AI vector store provides enterprise grade security, governance, compliance and high availability.
Large Scale With SLAs For Throughput and Latency
Abacus.AI vector store can work on datasets that have more than 1Bn+ vectors and deliver predictions under 150ms.
Highly Accurate
Vector Store is best in class and generates predictions with extremely high accuracy.
Featured on

e-week badge gartner forbes badge cbinsight badge gartner badge
Copyright © 2024 Abacus.AI. All Rights Reserved
Products
Products
Products
Company
About Us
Culture
Press
Case Studies
Jobs
Terms of Service
Privacy
Security
Request Access For A Free Trial
Processing...
Processing...
Thanks for requesting access to Abacus.AI, we will get back to you shortly
Continue
Free Expert Consultation
Processing...
Thanks for requesting a consultation, we will get back to you shortly
Continue