vectors.do
Semantic Search at the Edge, <10ms Globally
Store, index, and query vector embeddings across 300+ edge locations. Built-in similarity search, automatic clustering, and native OpenAI/Claude integration. Scale from prototype to billions of vectors with zero configuration.
import { Vectors } from 'vectors.do' // Edge-native vector store with HNSW indexingconst store = Vectors({ dimensions: 1536, metric: 'cosine', index: 'hnsw' }) on.Document.created(async $ => { const embedding = await $.OpenAI.embed.text($.content) store.insert({ id: $.id, vector: embedding, metadata: $.metadata })}) // Semantic search with <10ms latencyon.Search.submitted($ => store.query({ vector: $.embed(), topK: 10 }))
Frequently asked questions
Vectors.do runs at the edge with <10ms query latency globally versus 50-200ms for traditional vector databases. No separate infrastructure to manage—embeddings are stored across Cloudflare's 300+ locations automatically. Built-in integration with OpenAI, Claude, and local models eliminates embedding pipeline complexity. Scale to billions of vectors with automatic sharding, no capacity planning required. First 1M queries daily are free.
HNSW (Hierarchical Navigable Small World) indexing provides 95%+ recall with <10ms query times. IVF (Inverted File Index) available for trillion-scale datasets. Automatic index optimization based on query patterns—no manual tuning required. Support for approximate and exact nearest neighbor search with configurable accuracy/speed tradeoffs.
Yes. Bring your own embeddings from any model: OpenAI, Cohere, sentence-transformers, custom PyTorch/TensorFlow models, or domain-specific fine-tuned models. Any vector dimensionality from 128 to 4096 supported. Automatic normalization, quantization, and compression for optimal storage and query performance.
Store full-text metadata alongside vectors for powerful hybrid search. Combine semantic similarity with keyword filters, date ranges, categorical facets, and geo-location bounds. Use re-ranking algorithms to optimize result relevance. Full SQL-like query language with vector similarity operators built-in.
Scale to billions of vectors with automatic horizontal sharding across edge locations. No connection limits, no cold starts, sub-millisecond billing precision. Storage: $0.25/GB/month, queries: <$0.0001 each with volume discounts. Enterprise plans support dedicated capacity, private deployments, and custom SLAs.