dynavec documentation
A serverless hybrid vector database that runs inside your own AWS account.
dynavec fuses Amazon DynamoDB (single-digit-millisecond metadata & document store) with Amazon S3 Vectors (billion-scale serverless ANN) into one Python client — a drop-in alternative to Pinecone, Qdrant, Milvus, Weaviate, and OpenSearch that bills only when you use it.
Explore by feature
Embeddings
Pluggable, bring-your-own-key: OpenAI, Gemini, Cohere, Bedrock, local.
Search
Semantic search with metadata pre-filtering and document hydration.
Metrics & rerank
Cosine, dot, euclidean, manhattan, weighted combos, and MMR.
Namespaces
Per-tenant / per-collection isolation on one index.
Knowledge graph
Entity-relationship traversal that guides the vector search.
Caching
Semantic, DynamoDB-TTL, or Redis / ElastiCache query caches.
Product quantization
Compress cached vectors up to 32× with ADC distance.
Ingestion & MCP
Pull, chunk, embed from any source — including any MCP server.
Credentials & IAM
Access keys, profiles, cross-account assume-role, least-privilege policy.
Integrations
LangChain, LlamaIndex, and a tool for LangGraph / CrewAI / Strands.
FAQ
Common questions about regions, limits, consistency, and costs.
Benchmarking
Recall, latency, and cost modeled across dimensions and scale.