LambdaDB is a serverless vector database purpose-built for AI memory workloads, offering hybrid search, version control, and automatic scaling with zero idle cost. It enables teams to store, search, and version-control AI knowledge without provisioning or managing infrastructure, scaling transparently from free tier to petabytes.
Funding
Funding not disclosed
Founders
Product
Problem
AI applications that rely on vector databases face a fundamental tradeoff: provisioned clusters bill for idle capacity and require manual sizing, while serverless alternatives impose quota ceilings that throttle performance during traffic spikes. Additionally, AI agents write to shared memory continuously, and standard databases keep only the latest version—offering no history, rollback, or reproducibility for debugging regressions in AI behavior.
Solution
LambdaDB provides a fully serverless vector lake for AI memory that eliminates both infrastructure management and capacity ceilings. The platform automatically scales with workload demand, charging only for actual usage with no minimums or idle costs, while delivering sub-300ms p99 latency at scale. LambdaDB uniquely combines hybrid search (lexical, vector, and filters in one request) with Git-like version control primitives—tags, branches, and aliases—that let teams pin production data, branch for evaluation, and roll back when AI memory is poisoned or corrupted. The service includes managed embeddings, multi-region deployment, continuous backups, and point-in-time recovery as standard features, with SDKs for Python, TypeScript, and Go plus an MCP server for AI tooling integration.
Target Audience
Primary customers are AI engineering teams and platform developers building agentic applications, RAG systems, or semantic search features who need managed vector infrastructure with production-grade reliability, versioning, and predictable usage-based pricing.
Features
- Serverless architecture with automatic sharding and redistribution, scaling from zero to billions of vectors without provisioning or cluster management
- Hybrid search combining Lucene-style lexical queries (fuzzy matching, boosting, minimum-should-match) with k-NN vector search and pre-filters, fused via RRF in a single request
- Version control primitives: tags for immutable snapshots, branches for copy-on-write evaluation, and aliases for zero-downtime traffic repointing
- Built-in analyzers for English, Korean, and Japanese, plus managed embedding generation with support for OpenAI models at $1.05 per 1M LIU
- MCP server for Claude and Cursor integration, with SDKs for Python, TypeScript, and Go, plus REST API and LangChain compatibility
- Point-in-time search and retention-based recovery, with API keys scoped to individual projects for data isolation