Actian VectorAI DB
by Actian (a division of HCLSoftware)
A local-first vector database for AI that runs on the edge, on-prem and air-gapped
Actian VectorAI DB is a portable vector database built for AI systems that cannot run in the cloud, supporting semantic and hybrid search on edge hardware, on-premises servers and air-gapped facilities from a single API. It targets regulated manufacturing, healthcare, financial services and government teams whose data cannot leave their own infrastructure.
Actian VectorAI DB, launched on 28 April 2026 at AI Dev 26 in San Francisco by the data and AI division of HCLSoftware, is a vector database designed around the assumption most of its competitors reject: that the workload will not be running in a hyperscaler. It is local-first and portable, delivering semantic and hybrid search across multimodal data — text, images, audio, video and documents — from a single API and consistent behaviour across edge devices, on-premises servers, air-gapped facilities and cloud. Supported targets run from NVIDIA Jetson boards and Raspberry Pi devices through industrial edge servers to full enterprise infrastructure, and the database is designed to work offline and synchronise when connectivity returns. It uses modern approximate nearest neighbour indexing including HNSW and is model-agnostic, accepting embeddings from OpenAI, Anthropic, Cohere, Hugging Face and custom models, with Python and JavaScript SDKs and integrations for LangChain, LlamaIndex and Hugging Face. Actian's published benchmark, run on VectorDBBench against 10 million 768-dimension vectors on identical self-hosted hardware in April 2026, reports 745.2 queries per second — more than 22 times the throughput of open-source alternatives Milvus and Qdrant Local — and 72% throughput retention scaling from one million to ten million vectors against roughly 12% for those competitors, alongside 99% recall and 13ms p99 latency. Security posture centres on data sovereignty: AES-256 encryption with customer-managed keys, no third-party cloud processing, and configurations Actian describes as ready for HIPAA, GDPR, ISO 27001 and SOC 2 Type II. Pricing is published by tier, from a free Community edition to Starter at $417 per month and Growth at $1,250 per month, with custom Enterprise and Edge tiers.
The data or platform architect building retrieval-augmented AI for a plant floor, hospital, air-gapped facility or embedded device, where sending embeddings to a cloud vector service is not permitted.
One vector database and one API that runs unchanged from a Raspberry Pi to an enterprise server, so an edge deployment does not require re-architecting the retrieval layer.
At a Glance
- Category
- Data & Analytics
- Pricing
- Subscription, Freemium
- Target Market
- CTOs, Data Scientists, Enterprise Developers, Data Architects
- Deployment
- Self-hosted, Edge-first, Hybrid
Key Features
- ✓Local-first portable engine
Runs unchanged on edge devices, on-premises servers, air-gapped facilities and cloud from a single unified API
- ✓Constrained-hardware support
Deploys to NVIDIA Jetson boards, Raspberry Pi devices, embedded systems and industrial edge servers, not just datacentre-class machines
- ✓Multimodal semantic and hybrid search
Indexes embeddings from text, images, audio, video and documents with modern ANN indexing including HNSW
- ✓Model-agnostic embeddings
Accepts embeddings from OpenAI, Anthropic, Cohere, Hugging Face and custom models rather than binding to one provider
- ✓Framework integrations and SDKs
Python and JavaScript SDKs with LangChain, LlamaIndex and Hugging Face integrations to reduce migration friction
- ✓Sovereign security posture
AES-256 encryption with customer-managed keys and no third-party cloud processing, keeping data inside customer infrastructure
- ✓Offline operation with sync
Continues serving queries while disconnected and synchronises when connectivity is restored, which matters for field deployments
Capabilities
Use Cases
- •Air-gapped RAG for government and defence
Run semantic search over classified document sets inside facilities with no external network connectivity at all
- •Clinical retrieval inside a hospital datacentre
Serve HIPAA-constrained semantic search over patient and research data without sending embeddings to a third-party cloud
- •Plant floor and industrial inspection
Run multimodal similarity search on edge servers next to production lines where cloud latency would stall the process
- •Embedded and field devices
Deploy vector search to Jetson or Raspberry Pi hardware in vehicles, kiosks or remote sites that operate offline
- •Low-latency retrieval at the edge
Eliminate the 200-400ms per-query network round trip that cloud vector databases add to every retrieval call
- •Migrating an existing self-hosted vector store
Move an existing Qdrant deployment across using Actian's published migration guide while keeping LangChain and LlamaIndex code intact
Ideal For
Best For
- ✓Retrieval-augmented AI in air-gapped or disconnected facilities where a cloud vector database is simply not reachable
- ✓Regulated healthcare, financial services and government workloads with hard data residency and sovereignty constraints
- ✓Industrial and manufacturing edge deployments on constrained hardware such as NVIDIA Jetson boards and Raspberry Pi devices
- ✓Latency-sensitive retrieval where the 200-400ms cloud round trip per query is unacceptable
- ✓Teams that want one API and consistent behaviour across edge, on-premises and cloud rather than separate stacks per environment
- ✓Buyers who need published per-tier list pricing to model cost before procurement
Not Ideal For
- ✗Cloud-native teams at hyperscaler scale, where Pinecone, Zilliz Cloud and Weaviate offer mature managed services and far larger ecosystems
- ✗Teams wanting a fully managed vector service with no operational burden — VectorAI DB is something you deploy and run yourself
- ✗Cost-sensitive projects that would otherwise self-host Qdrant, Milvus or pgvector for free, since Starter begins at $417 per month for one million vectors
- ✗Anyone needing a meaningful free tier for prototyping, as the Community edition caps at 5,000 vectors outside the 30-day trial
- ✗Buyers who require independently verified benchmarks, since the 22x claim is vendor-run and unverified by third parties
Integrations
Deployment
Market Analysis
Pros
- ✓Addresses a real and under-served gap — data-sovereign, disconnected and edge AI deployments that cloud-first vector databases structurally cannot serve
- ✓Supports mainstream frameworks (LangChain, LlamaIndex, Hugging Face) and is model-agnostic on embeddings, keeping migration friction low
- ✓Published per-tier list pricing makes cost modellable before procurement, which most enterprise database vendors avoid
- ✓Backed by HCLSoftware rather than venture funding, which reduces the vendor-viability risk that regulated buyers weigh heavily
- ✓Deployment reach is unusually wide, from Raspberry Pi and Jetson through air-gapped facilities to cloud, from one codebase
Cons
- ✗The headline 22x throughput claim is a vendor-run VectorDBBench test; Constellation Research explicitly noted the launch coverage provided no independent verification and no comparison against commercial competitors
- ✗The product is absent from the major independent 2026 vector database comparisons, which cover Pinecone, Milvus, Qdrant, Weaviate, pgvector, MongoDB Atlas, Chroma, LanceDB and Faiss — a clear mindshare gap
- ✗No public user reviews exist on G2, TrustRadius, Capterra or PeerSpot for VectorAI DB specifically, and there is no Hacker News or Reddit practitioner discussion
- ✗The free Community edition caps at 5,000 vectors, which is too small for realistic prototyping once the 30-day trial expires
- ✗At $417 per month for one million vectors, it is materially more expensive than self-hosting Qdrant, Milvus or pgvector for free
- ✗There is no fully managed cloud service — operations remain the customer's responsibility, which offsets some of the portability benefit
- ✗Compliance is described as ready configurations for HIPAA, GDPR, ISO 27001 and SOC 2 Type II rather than as audited vendor certifications, so the certification burden sits with the deploying organisation
Pricing
Community
$0
- ✓Up to 5,000 vectors
- ✓Local development environment
- ✓30-day free trial extending to 1 million embeddings
Starter
From $417/mo
- ✓Up to 1 million vectors
- ✓Smaller servers and VMs
- ✓Full API and SDK access
Growth
From $1,250/mo
- ✓Up to 5 million vectors
- ✓Large servers and VMs
Enterprise
Contact for pricing
- ✓10 million+ vectors
- ✓Enterprise infrastructure
- ✓Compliance-ready configurations
Edge
Contact for pricing
- ✓Custom vector capacity
- ✓Embedded and edge device deployment
Actian publishes list pricing by vector capacity, which is unusual in this category: Community is free but capped at 5,000 vectors, Starter is $417 per month for one million vectors, Growth is $1,250 per month for five million, and Enterprise (10 million-plus) and Edge are custom-quoted. A 30-day free trial lifts the limit to one million embeddings. Because the metering is vector count rather than queries or storage, cost scales with corpus size regardless of traffic, and the Starter tier is materially more expensive than self-hosting Qdrant, Milvus or pgvector for free — the premium buys portability to constrained edge hardware and commercial support rather than raw capability.
Security & Compliance
Sources
This page was written from 5 sources, 2 on domains other than actian.com.
- 1.actian.com — vectorai dbvendor
- 2.actian.com — actian launches vectorai db with 22x faster vector search fovendor
- 3.constellationr.com — actian launches vectorai db designed premises edge ai deploy
- 4.marktechpost.com — best vector databases in 2026 pricing scale limits and archi
- 5.actian.com — blogvendor
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