Axoniz: The High-Performance Vector Database for Secure, Scalable AI
Axoniz is an advanced vector similarity search engine written in Rust, featuring quantized storage, hybrid search, snapshot recovery, and encryption. Optimized for secure and efficient AI pipelines.
Modern AI pipelines demand more from their vector databases — more speed, more scalability, and far more integrity. That’s where Axoniz comes in.
Under development for over 8 months, built from the ground up in Rust, Axoniz is a high-performance, memory-efficient, and production-ready vector similarity search engine that goes beyond standard ANN indexing. From secure quantized storage to hybrid text + vector relevance, Axoniz is designed to power the next generation of AI infrastructure.
What Makes Axoniz Different?
Axoniz is not just another vector store. It brings enterprise-grade features into a compact and modular core that supports:
- Blazing-fast performance with Rust
- Encrypted index storage
- Quantized vector compression via RaBitQ
- Hybrid BM25 + vector scoring
- Snapshot integrity and recovery
- Multi-tenant architecture
- Multi-algorithm indexing
- Flexible distance metrics
Multiple Index Types
Axoniz supports a wide range of ANN indexing algorithms for different performance, memory, and recall trade-offs:
- Flat: Brute-force exact search with 100% recall
- HNSW: Hierarchical Navigable Small World graph for fast approximate search
- IVF: Inverted File index with coarse quantization
- SPFresh: Partition-based index using the LIRE protocol for dynamic updates
- DiskANN: Disk-based ANN optimized for massive datasets
- RaBitQ: Proprietary 1-bit quantized index for extreme compression
- RaBitQ HNSW: Combines quantization with graph-based retrieval
- RaBitQ IVF: Combines quantization with large-scale IVF partitioning
Flexible Distance Metrics
- Euclidean distance
- Cosine similarity
- Dot product
- Manhattan distance
- Hamming distance
Encrypted Indexes
All index and snapshot data are protected using:
- AES-256-GCM
- ChaCha20-Poly1305
With support for key rotation, versioning, and full disk-level security.
Tiered Storage
Choose your storage tier:
- In-memory vectors for ultra-low latency
- SSD-backed disk index with background prefetching
- Object storage (S3, IPFS) with compression and streaming support
Snapshot System: Tamper-Resistant, Secure, and Enterprise-Grade
Security & Tamper Resistance
- End-to-end encryption
- Key rotation and versioning
- Checksums and digital signatures
- Access control enforcement
Enhanced Storage Features
- Multipart uploads & chunked downloads
- Adaptive compression: Deflate, Gzip, Brotli, Zstd, LZ4
- Snapshot lifecycle management
Data Protection
- Point-in-time consistency
- Transactional atomicity
- Secure deletion
- Full audit trail
Performance Optimizations
- Background processing
- Parallelized I/O
- Memory-efficient streaming
Storage Backend Support
- Local encrypted filesystems
- S3-compatible storage
- NAS/network filesystems
- Pluggable custom backend interface
Benchmark Results & Performance
Axoniz delivers exceptional performance across various workloads:
Search & Compression Efficiency
| Dataset Size | Dimensions | Original Size | Compressed Size | Search QPS | Recall@10 |
|---|---|---|---|---|---|
| 1M vectors | 384 | 1.5 GB | 48 MB | 8,000 | 0.97 |
| 10M vectors | 768 | 30 GB | 960 MB | 5,000 | 0.95 |
| 100M vectors | 1024 | 400 GB | 12.8 GB | 3,000 | 0.93 |
Operation-Level Throughput
In-Memory Operations:
- Vector insertion: 1M+ vectors/sec
- Vector search (HNSW): 10K+ QPS
- Vector search (Flat): 1K+ QPS
- Vector search (RaBitQ HNSW): 8K+ QPS
- Vector search (RaBitQ IVF): 5K+ QPS
Disk-Based Operations:
- Vector insertion: ~125K vectors/sec
- Vector search: ~460 QPS
Hybrid Search (Text + Vector):
- Combined query throughput: ~350 QPS
Memory Usage (1M vectors, 384 dimensions):
- Full-precision: ~1.5GB
- RaBitQ compressed: ~48MB (~32× reduction)
Comparison with Other Quantization Methods
| Method | Compression Ratio | Search Speed | Recall@10 | Training Time |
|---|---|---|---|---|
| RaBitQ | 32:1 | Fast | 0.95 | Medium |
| PQ (8-bit) | 4:1 | Medium | 0.92 | Slow |
| ScaNN | 8:1 | Fast | 0.94 | Slow |
| LSH | 16:1 | Medium | 0.88 | Fast |
Use Cases
- AI agent frameworks with persistent memory
- Enterprise-grade RAG pipelines
- Privacy-respecting edge AI deployments
- Multi-tenant SaaS platforms
- Zero-trust environments needing full encryption and rollback
Features Under Development
- OpenTelemetry for observability
- Cluster replication and horizontal sharding
Get Involved
Axoniz is currently in private alpha and available for early access upon request.
All core features have been implemented, with upcoming capabilities actively roadmapped. Currently assembling a team of collaborators and like minded individuals to help shape the future of Axoniz as it approaches its open-source release.
Whether you’re passionate about high-performance vector systems, secure AI infrastructure, or building cutting-edge tools in Rust — now is the time to get involved.
Interested in contributing or collaborating?
Contact me directly to become an active collaborator and contributor.