VectorX
Add Security to your GenAI

Security-first Vector Database with Performance at Scale

Queryable Encryption for security at-rest, in-transit & in-memory.
Blazing-fast queries. Cost-efficient vector searches.

* No Credit Card Required
Why Choose VectorX

Ready for Production-Grade Enterprise AI

VectorX is designed from the ground up to meet the enterprise requirement of high security, high performance and low operating cost for high-adoption generative AI applications.

Security & Compliance
Queryable Encryption

Performing ANN searches on encrypted vector data stored in VectorX does not require decryption - full data security without any performance trade-offs.

Data security at-rest and in-memory ensures compliance with regulations such as HIPPA and SOC 2 in you GenAI and RAG applications.

Cost Efficient Scaling
Hybrid Graph Memory Management

VectorX’s proprietary hybrid graph memory approach requires just 1/10th of the memory required by leading vector databases.

Upto 90% cost reduction for vector searches while enhancing security and performance.

Ready for Enterprise AI

Built for Enterprise AI Applications

Fast, Secure, and Scalable by Design

High recall and precision
Consistent sub-10ms query times
Fast encryption and decryption
Queryable encryption for peak performance
Scaling to 100 Million+ vectors
Fully Managed Cloud or On-prem Deployment
Multi-Region Managed Cloud
Audit logging & Compliance reporting
Integration with leading AI frameworks
Customizable Enterprise SLAs
Language Support

Major Programming Language Support

Secure your GenAI application across all major programming languages with VectorX.


from vecx.vectorx import VectorX

vx = VectorX(token="your-token-here")
encryption_key = vx.generate_key()

vx.create_index(
    name="my_index",
    dimension=768,
    key=encryption_key,
    space_type="cosine"
)

index = vx.get_index(name="my_index", key=encryption_key)

index.upsert([
    {
        "id": "doc1",
        "vector": [0.1, 0.2, 0.3],
        "meta": {"text": "Example document"},
        "filter": {"category": "reference"}
    }
])

results = index.query(
    vector=[0.2, 0.3, 0.4],
    top_k=10,
    filter={"category": {"eq": "reference"}}
)

for item in results:
    print(f"ID: {item['id']}, Similarity: {item['similarity']}")
    print(f"Metadata: {item['meta']}")

Start Building your GenAI App with VectorX Today

Add the power of VectorX Encryption, and Performance to your RAG based Gen AI solutions. Its super simple to start.

Start Free with $300 Credits
Consult Our Experts
Build GenAI RAG Applications

Vector Database Use Cases

VectorX powers a wide range of AI applications across industries.

Retrieval-Augmented Generation (RAG)

Enhance large language models with real-time retrieval from external data sources for accurate and verifiable outputs.

Personalized Recommendations

Deliver user-specific product, content, and media suggestions based on behavioral data and similarity models.

Semantic Text Search

Enable high-precision semantic search across documents, support content, and enterprise knowledge bases.

Image Similarity Search

Instantly retrieve visually similar images from large datasets for e-commerce, media, and creative workflows.

Audio & Video Matching

Search and match audio clips and video segments by content similarity for media management, moderation, and compliance workflows.

AI Chatbots

Equip chatbots with real-time retrieval of enterprise knowledge for accurate, context-aware, and human-like interactions across support, sales, and internal teams.

GenAI RAG Case Studies

Case Studies

See how leading companies are transforming their businesses with VectorX

AI Sales Coach

How VectorX helped in delivering 20 Years of sales wisdom in real-time while keeping business data secure in an AI Coach.

Healthcare Compliance

Healthcare Agentic SaaS platform achieved HIPAA compliance with VectorX to be able to enter regulated markets.

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