Enterprises are generating unprecedented volumes of data, and AI agents can only deliver value when they can access and reason over that information. But as datasets grow into the billions, most search and vector databases break down, leaving teams to choose between slow results, spiraling costs, or operational overhead. TopK removes those tradeoffs. With our latest release, TopK supports billions of documents inside a single collection while maintaining predictable, low-latency performance — without the need for manual sharding or complicated infrastructure.
Why This Matters
As organizations adopt AI-powered applications and knowledge retrieval pipelines, search infrastructure has become a critical bottleneck. Teams often spend significant engineering resources maintaining shards, tuning indexes, and building layers of caching just to meet latency targets. TopK’s unified architecture eliminates these pain points. You can grow a single collection to billions of records, query with hybrid search (dense and sparse vectors, keywords, and metadata filters), and still achieve sub-100 millisecond latencies without introducing operational overhead.
For AI teams, this means their search infrastructure can scale to enterprise knowledge bases without degraded user experience. For product teams, it means search features stay fast and reliable as customer data grows. For technology leaders, it means fewer infrastructure components to manage, reduced engineering burden, and predictable costs as your datasets scale.
Predictable Performance at Scale
TopK was built from the ground up to handle the challenges of scale. Its distributed indexing engine, vector-aware storage, and adaptive caching work together to deliver consistent performance without manual tuning. Whether you are supporting AI agents, enterprise search, or analytics platforms, TopK allows your teams to focus on delivering value—not babysitting infrastructure.
In our benchmarks, we show that TopK can index billions of documents in hours, and deliver ~50ms query latencies for both dense and sparse vectors. Additionally, TopK's hybrid query engine supports text search, filtering, and flexible scoring at this scale, giving users full flexibility without sacrificing quality of results.
For dense vector search, TopK can deliver ~60ms p99 latency which improves to ~30ms when using filters with no degradation in results quality. This is contrary to other solutions which often degrade performance and results quality when using filters.
For sparse vector search, TopK can deliver ~50ms p99 latency which improves to ~40ms when using filters. Similar to dense vector search, filtering does not degrade results quality.
If you’re building AI agents, powering RAG pipelines, or modernizing your enterprise search stack for the AI era, we’d love to hear about your challenges and goals. Reach out to our team at TopK to share your use case and explore how TopK can help you scale without compromise.