VectorAI DB
Actian VectorAI DB is a portable vector database built for production AI workloads in regulated, disconnected, and edge environments where cloud-native solutions fall short.
Publisher review
Actian VectorAI DB is a portable vector database built for production AI workloads in regulated, disconnected, and edge environments where cloud-native solutions fall short. It is designed for developers building RAG pipelines, AI agents, and edge applications in industries such as manufacturing, healthcare, financial services, and government, where data sovereignty and low-latency search are critical. The database runs local-first across edge devices (including NVIDIA Jetson and Raspberry Pi), on-premises servers, and cloud, ensuring sensitive data and embeddings never leave the organization's infrastructure.
VectorAI DB delivers 745.2 queries per second on 10 million vectors at 768 dimensions, achieving over 22x faster throughput than leading open-source alternatives like Milvus and Qdrant in identical self-hosted hardware benchmarks using the VDBBench methodology. It supports multimodal data (text, images, audio, video, documents) and integrates with Python, JavaScript, LangChain, LlamaIndex, and Hugging Face. Security features include AES-256 encryption, customer-managed API keys, and compliance configurations for HIPAA, GDPR, ISO 27001, and SOC 2 Type II. Developers build once using the same architecture and APIs across all deployment environments, avoiding re-architecting as applications scale.
Competing against cloud-first vector databases like Pinecone, Azure AI Search, and Zilliz Cloud, VectorAI DB differentiates by prioritizing on-premises and edge deployments. Its architecture does not depend on cloud resources to scale, making it suitable for air-gapped facilities, plant floors, hospital data centers, and hybrid environments. However, this local-first focus may limit appeal for teams that prefer fully managed cloud services with zero infrastructure overhead.
Honest trade-offs include limited publicly available pricing details beyond a 30-day free trial and a free Community Edition, which may frustrate budget planning. The database is primarily optimized for on-premises and edge use cases, so cloud-only deployments may find it less streamlined than native cloud services. Additionally, as a relatively new entrant (launched April 2026), its ecosystem and community support are smaller than established competitors. Organizations requiring extensive cloud-native features like auto-scaling or serverless compute may need to evaluate whether the local-first model fits their operational model.
How it works
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Local-first deployment
Runs on edge, on-premises, and disconnected environments, keeping data and embeddings within the organization's infrastructure.
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22x faster throughput
Delivers 745.2 queries/second on 10M vectors at 768 dimensions, outperforming open-source alternatives by over 22x.
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Multimodal data support
Embeds text, images, audio, video, and documents, enabling diverse AI workloads beyond text-only search.
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AES-256 encryption
Includes AES-256 encryption and customer-managed API keys for data security and compliance.
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Compliance-ready configurations
Supports HIPAA, GDPR, ISO 27001, and SOC 2 Type II, meeting strict regulatory requirements.
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Framework integrations
Works with LangChain, LlamaIndex, and Hugging Face, plus Python and JavaScript SDKs.
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Consistent architecture across environments
Same APIs and architecture for edge, on-prem, and cloud, eliminating re-architecting when scaling.
Strengths and trade-offs
Strengths
- Achieves 745.2 queries per second on 10M vectors at 768 dimensions, outperforming open-source alternatives by 22x in throughput.
- Supports deployment on resource-constrained hardware like NVIDIA Jetson and Raspberry Pi, enabling edge AI use cases.
- Includes AES-256 encryption and customer-managed API keys, with compliance configurations for HIPAA, GDPR, ISO 27001, and SOC 2 Type II.
- Offers a free 30-day trial and a free Community Edition, allowing developers to evaluate without upfront cost.
Trade-offs
- Pricing details beyond the free trial and Community Edition are not publicly disclosed, complicating cost estimation for production use.
- Primarily designed for on-premises and edge deployments, which may limit appeal for teams seeking fully managed cloud-native services.
- As a new entrant launched in April 2026, its ecosystem, community, and third-party integrations are less mature than competitors like Pinecone.
- Cloud-only deployments may find the local-first architecture less streamlined than cloud-native alternatives with auto-scaling and serverless options.
Pricing context
Free 30-day trial and free Community Edition available. Flexible pricing for production deployments across local, on-prem, and cloud environments, with no public per-tier figures.
Getting started with VectorAI DB
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Sign up for VectorAI DB
Visit the Actian VectorAI DB website and register for a free 30-day trial or download the free Community Edition. Provide your email and organization details to receive access credentials and installation instructions.
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Install on your target environment
Download the VectorAI DB package for your deployment target—edge device (e.g., NVIDIA Jetson, Raspberry Pi), on-premises server, or cloud instance. Follow the platform-specific installation guide to set up the database binary and dependencies.
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Connect and load your data
Use the Python or JavaScript SDK to establish a connection to your local VectorAI DB instance. Load your dataset—text, images, audio, or documents—and generate embeddings using your preferred embedding model or Hugging Face integration.
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Configure security and compliance
Enable AES-256 encryption and set customer-managed API keys via the configuration file. If required, apply compliance settings for HIPAA, GDPR, ISO 27001, or SOC 2 Type II by adjusting the security policies in the database configuration.
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Run your first vector search
Write a query using the SDK to perform a similarity search on your vector index. For example, in Python, call `db.search(query_vector, top_k=10)` to retrieve the nearest neighbors. Verify the response time and accuracy against your benchmark.
Frequently Asked Questions
What is VectorAI DB and who is it for?
VectorAI DB is a portable vector database for production AI workloads in regulated, disconnected, and edge environments. It targets developers building RAG pipelines, AI agents, and edge applications in manufacturing, healthcare, financial services, and government where data sovereignty and low-latency search are critical.
How does VectorAI DB compare to Pinecone or Milvus?
VectorAI DB prioritizes local-first deployment across edge devices, on-premises servers, and cloud, unlike cloud-native solutions like Pinecone. It achieves 745.2 queries per second on 10 million vectors, over 22x faster than Milvus and Qdrant in self-hosted benchmarks, but has a smaller ecosystem as a newer entrant.
What are the performance benchmarks for VectorAI DB?
VectorAI DB delivers 745.2 queries per second on 10 million vectors at 768 dimensions, achieving over 22x faster throughput than leading open-source alternatives like Milvus and Qdrant in identical self-hosted hardware benchmarks using the VDBBench methodology.
Can VectorAI DB run on edge devices like Raspberry Pi?
Yes, VectorAI DB runs on resource-constrained hardware including NVIDIA Jetson and Raspberry Pi. It supports local-first deployment across edge devices, on-premises servers, and cloud, ensuring sensitive data and embeddings never leave the organization's infrastructure.
What security and compliance features does VectorAI DB offer?
VectorAI DB includes AES-256 encryption, customer-managed API keys, and compliance configurations for HIPAA, GDPR, ISO 27001, and SOC 2 Type II. These features meet strict regulatory requirements for industries like healthcare and government.
What are the trade-offs of using VectorAI DB?
VectorAI DB has limited publicly available pricing beyond a free trial and Community Edition, complicating budget planning. It is optimized for on-premises and edge use cases, so cloud-only deployments may find it less streamlined than native cloud services with auto-scaling.
Alternatives
How VectorAI DB compares
Direct head-to-head against 3 competitors. Picked by 7wData.
VectorAI DB
- Pricing
- Free 30-day trial and free Community Edition available. Flexible pricing for production deployments across local, on-prem, and cloud environments, with no public per-tier figures.
- Target
- Actian VectorAI DB is a portable vector database built for production AI workloads in regulated, disconnected, and edge environments where cloud-native solutions fall short.
- Strength
- Achieves 745.2 queries per second on 10M vectors at 768 dimensions, outperforming open-source alternatives by 22x in throughput.
- Watch for
- Pricing details beyond the free trial and Community Edition are not publicly disclosed, complicating cost estimation for production use.
Pinecone
- Pricing
- Free tier up to 100k vectors; paid starts at $70/month for 2M vectors.
- Target
- Teams needing zero-ops managed vector search at any scale.
- Deployment
- Fully managed cloud.
- Strength
- Zero-ops managed service with ultra-low latency at billions of vectors.
- Watch for
- Usage-based pricing can spike unexpectedly as index grows; vendor lock-in risk.
Milvus / Zilliz Cloud
- Pricing
- Open-source free; Zilliz Cloud starts at $0.70/hour for serverless.
- Target
- Teams managing billions of vectors at lower cost with engineering resources.
- Deployment
- Self-hosted or managed cloud.
- Strength
- Proven at billion-scale vector search with high performance per dollar.
- Watch for
- Complex setup and tuning required for self-hosted; managed cloud pricing can escalate.
Qdrant
- Pricing
- Free tier 1GB storage; paid starts at $25/month for 4GB.
- Target
- Budget-conscious teams with under 50M vectors needing best free tier.
- Deployment
- Self-hosted or managed cloud.
- Strength
- Best free tier with generous limits; simple API and Rust-based performance.
- Watch for
- Performance degrades above 50M vectors; limited advanced filtering compared to Weaviate.
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