TopK

TopK is an AI-native search engine built for scale and performance, combining dense (vector) and sparse (keyword) retrieval in a single composable query. It serves as a document database with native multi-vector, keyword, and faceted search, designed for modern AI applications in production. TopK is built for enterprise workloads, offering SOC 2 Type I compliance, 99.9% SLA, and deployment options in your own VPC or on the public cloud.

• True Hybrid Retrieval™ — Combines dense (vector) and sparse (keyword) retrieval to deliver accurate top-k results; get started by querying with fn.semantic_similarity and fn.bm25_score in a single call.
• Fast, High-Recall Filtering — Innovative filtering outpaces traditional vector databases in both query speed and result completeness; apply filter expressions directly in your query.
• Flexible Scoring — A powerful expression language lets you weight and combine multiple signals (semantic, keyword, vector distance) based on business logic or domain bias.
• Multi-Modal Search — Supports unlimited vector representations per document, enabling semantic search across text, images, video, and audio.
• Large-Scale & Multi-Tenant — Scales to millions (and billions) of documents in single and multi-tenant scenarios using simple filter expressions.
• Easy Integration via SDKs — Official SDKs for Python, JavaScript, and Rust; connectors for Postgres, MongoDB, and Kafka; get started by installing the SDK and pointing it at your TopK collection.
• Bring Your Own Cloud — Deploy on AWS, GCP, or Azure in your own VPC, or use TopK's managed public cloud offering.
• Provisioned Capacity Pricing — Usage-based pricing model covering storage, writes, and queries ensures predictable and scalable costs.
• Enterprise Security — SOC 2 Type I compliant with 99.9% availability SLA and 24/7 on-call support via a private Slack channel.
• Sub-10ms Latency at Scale — Benchmarks show consistent p50 latency of ~3.91ms and >98% recall at 1M+ documents.