Sold by: Actian, a division of HCLSoftware
Deployed on AWS
Actian VectorAI DB is a portable vector database built for AI beyond the cloud. It enables developers to store, retrieve, and reason over multimodal data locally, delivering low-latency vector search on embedded, edge, on-prem, and hybrid systems. Teams can build once and deploy consistently across environments, maintaining full data ownership and predictable behavior without relying on cloud-native vector databases.
Overview
Imagine a world where AI applications run reliably anywhere, at the edge, in disconnected environments, or in the cloud, without sacrificing performance, consistency, or trust. Actian VectorAI DB is a portable vector database that makes this possible.
Actian VectorAI DB delivers production-grade reliability with a lightweight, embeddable design built for edge and OEM environments. It supports modern AI workloads such as retrieval-augmented generation (RAG), agentic AI, and autonomous systems without forcing teams to manage separate databases or re-architect as applications scale. Key capabilities include: Native vector storage with high-performance semantic search Embed multimodal data for vector indexing across text, images, audio, and video Portable deployment across edge, hybrid, and cloud environments Low-latency local retrieval for real-time and disconnected use cases Actian VectorAI DB is designed for a future where AI operates everywhere, not just in a centralized cloud, where efficiency and data residency matter. It provides a durable foundation for portable AI, enabling teams to move faster, scale confidently, and bring intelligence closer to where data is created and decisions are made.
Highlights
- Runs anywhere without cloud dependency - Reliable AI execution across edge, disconnected, hybrid, and cloud environments with low-latency local vector search and retrieval.
- Edge-to-Cloud Scalability - Build once and scale from local development to enterprise cloud without re-platforming, redesigning data systems, or rewriting pipelines.
- Developer-first experience for fast adoption - Frictionless onboarding and familiar workflows make it easy to learn, experiment, prototype, and move AI applications into production.
Details
Delivery method
Delivery option
64-bit (x86) Amazon Machine Image (AMI)
Latest version
Operating system
Ubuntu 24.04
Deployed on AWS
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Pricing
Pricing is based on the duration and terms of your contract with the vendor. This entitles you to a specified quantity of use for the contract duration. If you choose not to renew or replace your contract before it ends, access to these entitlements will expire.
Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator to estimate your infrastructure costs.
Dimension | Description | Cost/12 months |
|---|---|---|
Free Tier | Up to 5000 vectors | $0.00 |
Starter Tier | Up to 1M vectors | $5,000.00 |
Growth Tier | Up to 5M vectors | $15,000.00 |
Dimensions summary
Pricing scales by the number of vectors you store, a vector being a numeric representation of your data. You choose one of three capacity tiers. The Free Tier covers up to 5,000 vectors and suits early prototyping. The Starter Tier raises capacity to 1 million vectors. The Growth Tier extends it to 5 million vectors. Each step up expands how much data the database can index and search. All tiers use the same architecture, so moving between them does not require re-building your setup. Billing follows a contract model.
Top-of-mind questions for buyers
A vector is a numeric representation of your data, such as text, images, audio, or video, produced by an embedding model you provide. Each stored embedding counts as one vector toward your tier limit. The database indexes and searches these vectors during agent retrieval.
Each tier caps how many vectors you can store and index: 5,000, 1 million, or 5 million. To store more vectors, you move to a higher-capacity tier. All tiers share the same architecture, so scaling up does not require re-building your setup or rewriting code.
No. The product is model-agnostic and works with embeddings from any provider or a custom model you bring. Your tier limit counts the number of stored embeddings, not the model that generates them. It also stores embeddings from text, images, audio, and video.
www.actian.com
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Vendor refund policy
All fees are non-cancellable and non-refundable except as required by law
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Delivery details
64-bit (x86) Amazon Machine Image (AMI)
Amazon Machine Image (AMI)
An AMI is a virtual image that provides the information required to launch an instance. Amazon EC2 (Elastic Compute Cloud) instances are virtual servers on which you can run your applications and workloads, offering varying combinations of CPU, memory, storage, and networking resources. You can launch as many instances from as many different AMIs as you need.
Version release notes
ZenDev Jenkins build test#3 Sept.23, 2026
Additional details
Support
Vendor support
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AWS Support is a one-on-one, fast-response support channel that is staffed 24x7x365 with experienced and technical support engineers. The service helps customers of all sizes and technical abilities to successfully utilize the products and features provided by Amazon Web Services.
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