Proven at Scale
How We Handle Billion-Scale
Disaggregated architecture
LambdaDB completely separates compute, memory, and storage into independently scaling layers. Each runs as a shared pool that auto-balances load just like S3.
Use cases :
Enterprise assistant: One index across wiki/code/product details with stable p99 latency.
Multimodal search: Full text search + vector documents + image embeddings in a single query.
Partitioning and pay-for-use
Read only what you need. Pay less without sacrificing quality.
Use cases :
RAG/agents: Only the segments your team actually queries read frequently. You avoid scanning the entire index, so cost stays predictable and stable even as your dataset grows.
Catalog search: Load specific category segments without touching the rest.
Zero-copy fork
Fork terabyte-scale collections in seconds. No copying, No reindexing.
Use cases :
Knowledge bases: Test a policy change on a branch. Merge when it is better.
Agent memory: A/B test new embeddings or filters on live traffic. Promote or roll back instantly.
