KDB.AI
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About
KDB.AI is a high-performance vector database designed to handle the complexities of both unstructured data and time-series information. Built by KX, it allows developers to build scalable AI applications that require contextual search and pattern recognition. It excels at bridging the gap between traditional structured data and the high-dimensional embeddings used in modern generative AI and retrieval-augmented generation (RAG) workflows. The platform is optimized for managing large-scale datasets while maintaining low-latency retrieval, making it a foundation for real-world AI projects. The platform stands out for its hybrid search capabilities, which unify dense and sparse vectors in a single query. It integrates natively with NVIDIA cuVS for GPU-accelerated search, significantly boosting performance for massive workloads. A unique feature is its "Zero Embedding" capability, which performs searches up to 17x faster with 12x less memory for fast-changing temporal data by bypassing the embedding process entirely. It also offers advanced on-disk indexing methods like qHNSW and qFlat to optimize storage costs while maintaining high retrieval speeds without exhausting memory resources. This tool is specifically tailored for industries dealing with high-velocity data, such as financial services, manufacturing, and cybersecurity. It is ideal for quantitative analysts extracting alpha signals, research assistants processing SEC filings, and engineers building real-time image recognition systems. Because it supports multimodal RAG—handling text, video, audio, and images—it is a robust choice for developers creating sophisticated chatbots or automated knowledge bases. Its ability to process sensor streams at the edge makes it suitable for IoT and industrial automation applications. What sets KDB.AI apart is its heritage in time-series data management. Unlike general-purpose vector stores, it provides native temporal similarity search and massive compression—up to 100x—for time-based datasets. The ability to perform dynamic hybrid search, combining similarity, exact, and literal matches, ensures that query results remain relevant even as content changes rapidly. With a 90-day trial for its server edition and deep integrations with industry-standard tools like LangChain and LlamaIndex, it offers a production-ready environment for enterprise AI development.
Pros & Cons
GPU-accelerated search using NVIDIA cuVS for high-performance retrieval.
Native handling of time-series data alongside high-dimensional vector embeddings.
Significant storage reduction through 100x compression on time-based datasets.
Supports multimodal data inputs including text, audio, video, and image streams.
Flexible deployment options including on-premises and multi-cloud environments.
The server edition trial is limited to a 90-day period.
Full professional support requires a separate paid KX Support Services contract.
The 17x speed advantage for temporal data specifically requires the 'Zero Embedding' workflow.
Complex indexing methods like qHNSW may require specific setup requirements.
Use Cases
Financial analysts can extract alpha signals and detect market shifts using real-time temporal similarity search on market data.
AI researchers can transform SEC filings and internal documents into searchable insights for automated RAG-based assistants.
Portfolio managers can monitor portfolios and enable client-level rebalancing at scale by embedding specific risk rules.
Computer vision engineers can analyze image and sensor streams at the edge to trigger real-time, explainable actions.
Developers can build internal knowledge bases that extend LLMs by chunking documents and injecting relevant data.
Platform
Task
Features
• temporal similarity search
• metadata filtering
• 100x data compression
• zero embedding temporal search
• on-disk indexing (qhnsw, qflat)
• multimodal rag support
• nvidia cuvs gpu acceleration
• hybrid dense and sparse search
FAQs
What deployment options are available for KDB.AI?
KDB.AI can be deployed either on-premises or through a cloud provider of your choice. This flexibility allows organizations to maintain control over their data infrastructure while scaling as needed.
What is the 'Zero Embedding' feature?
Zero Embedding allows for searches on fast-changing temporal data without the need for pre-generated embeddings. This method is up to 17x faster and uses 12x less memory than traditional HNSW indexing.
Which third-party AI tools does KDB.AI integrate with?
KDB.AI offers native integrations with popular frameworks including LangChain, LlamaIndex, OpenAI, Cohere, Azure AI, Hugging Face, and Unstructured.io.
How does KDB.AI handle time-series data?
It uses specialized temporal similarity search and high-compression techniques to reduce disk storage by up to 100x for time-based datasets. This allows for rapid pattern detection in high-velocity data streams.
Does KDB.AI support GPU acceleration?
Yes, it natively integrates with NVIDIA cuVS to provide GPU-accelerated vector search. This integration significantly improves speed and scalability for RAG and AI agent workloads.
Pricing Plans
KDB.AI Server Trial
Free Plan• 90-day trial period
• On-premises or cloud deployment
• Unlimited memory and storage
• Unlimited reads and writes
• Support for qHNSW and qFlat indexing
• Temporal similarity search (TSS/TSC)
• Euclidean, Cosine, and Dot Product metrics
• Unlimited tables and index limits
• Access to sample applications
• Hybrid search capabilities
Job Opportunities
There are currently no job postings for this AI tool.
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