Built for Speed, Designed for Scale
Revolutionary vector search engine with sophisticated algorithms and innovative optimization for AI applications, semantic search, and machine learning workflows.
Sophisticated algorithms and innovative data structures deliver up to 10x faster similarity search compared to traditional approaches, with sub-millisecond query times.
Sophisticated encoding techniques reduce memory footprint by up to 90%, enabling massive datasets to run efficiently on standard hardware.
Engineered to handle billion+ vector datasets with consistent performance, perfect for enterprise-grade AI applications.
Drop-in replacement for popular vector databases with familiar REST API patterns, making integration seamless.
Leverages modern CPU instruction sets (AVX2, SSE) for maximum throughput on standard x86 hardware.
Deploy on-premises, in cloud, or hybrid configurations with Docker support and Kubernetes compatibility.
Seamless integration with LangChain for building powerful RAG (Retrieval Augmented Generation) applications. Compatible with standard vector store interfaces.
Power sophisticated search experiences with natural language understanding, document similarity, and content recommendation engines.
Build high-performance recommendation engines for e-commerce, content platforms, and personalization systems.
Accelerate ML workflows with fast nearest neighbor search for clustering, classification, and similarity analysis.
Enable real-time similarity analytics on high-dimensional data for business intelligence and data exploration.
Support scientific computing, bioinformatics, and research applications requiring fast similarity search at scale.
Hemlis provides a clean, RESTful API that integrates seamlessly with existing applications and popular frameworks:
# Insert vectors
POST /vectors
{
"vectors": [
{"id": "doc1", "values": [0.1, 0.2, ...]},
{"id": "doc2", "values": [0.3, 0.4, ...]}
]
}
# Search similar vectors
POST /search
{
"vector": [0.1, 0.2, ...],
"k": 10,
"filter": {"category": "tech"}
}Choose the plan that fits your needs. All plans include full API access and documentation.
14-day free trial included
14-day free trial included
Hemlis leverages breakthrough algorithms and innovative techniques to deliver unmatched vector search performance:
Important: Search performance and memory footprint vary significantly based on vector properties, dataset characteristics, and hardware configuration.
🧪 Evaluation Recommended: We strongly encourage thorough testing during your free trial period to validate performance with your specific data and use case. Contact [email protected] for assistance with performance optimization and benchmarking guidance.