Back to Home

Features Built for Production AI

Tensorus combines cutting-edge algorithms, AI-powered intelligence, and production-grade reliability to deliver the most advanced tensor database on the market.

Core Technology

True Multi-Dimensional Storage

Unlike vector databases that only handle 1D arrays, Tensorus natively supports tensors from 1D to 6D. Store images (2D), videos (3D), or complex scientific simulations (4D+) with mathematical precision.

  • 1D: Classic embeddings (768-dim, 1536-dim)
  • 2D: Image features, attention matrices
  • 3D: Video embeddings, time-series
  • 4D: Multi-view features, spatio-temporal
  • 5D-6D: Complex scientific data
# 1D Vector (traditional)
embedding_1d = [0.1, 0.2, 0.3, ...]  # 768-dim

# 2D Image Features
image_2d = np.array([[...], [...]])  # 224x224

# 3D Video
video_3d = np.array([[[...]]])       # T×H×W

# 4D Multi-view
multiview_4d = np.array([[[[...]]]])  # N×T×H×W

# Store in Tensorus
client.insert({
    "id": "video_001",
    "tensor": video_3d,
    "shape": [30, 224, 224],  # 30 frames
    "properties": {
        "fps": 30,
        "duration": 1.0
    }
})
AI Intelligence

Autonomous AI Agent

The built-in AI Agent continuously monitors your queries, analyzes access patterns, and automatically optimizes performance—no manual tuning required.

Query Optimization
Automatically rewrites queries for better performance
Index Recommendations
Suggests optimal indexes based on usage patterns
Predictive Insights
Forecasts bottlenecks before they impact users
Auto-scaling
Adjusts resources based on load predictions
AI Agent Dashboard
Current Optimization
Query rewrite: -34% latency
Prediction
Load spike in 2h → Pre-scaling
Recommendation
Add ALEX index on "category"
Search Technology

Three Specialized Search Engines

Choose the right algorithm for your use case—or combine them for hybrid queries

HNSW

Hierarchical Navigable Small World

Industry-standard approximate nearest neighbor search. Finds tensors with similar numerical content.

Use case: Semantic search
Latency: ~5ms p95
Accuracy: 95%+ recall

ALEX

Learned Index Structure

ML-optimized index for mathematical properties. Search by symmetry, rank, norms, eigenvalues.

Use case: Property search
Latency: ~3ms p95
Dimensions: 13 properties

Tucker

Tucker Decomposition

Advanced tensor factorization. Finds tensors with similar structural patterns, ignoring absolute values.

Use case: Pattern matching
Latency: ~8ms p95
Multi-dimensional only
Enterprise Grade

Production-Ready from Day One

Built with Rust for safety and speed. Battle-tested infrastructure patterns.

🔒 Security
  • • End-to-end encryption
  • • Role-based access control (RBAC)
  • • Audit logging
  • • SOC 2 Type II compliance
⚡ Performance
  • • < 10ms p95 latency
  • • 10K+ QPS per node
  • • Horizontal scaling
  • • Zero-copy serialization
🛡️ Reliability
  • • 99.99% uptime SLA
  • • Automatic failover
  • • Point-in-time recovery
  • • Multi-region replication
🔧 Developer Experience
  • • Python, JavaScript, REST SDKs
  • • Comprehensive documentation
  • • Migration tools
  • • 24/7 support (Pro+)

Ready to experience the difference?

Start building with Tensorus today. Free tier, no credit card required.