Built for RAG and Agent
Hybrid Search on a Flexible Document Model
Perform multi-field vector search across text and images simultaneously — without flattening your schema.
Store vectors, keywords, text, and complex nested objects in a single document, and search your data exactly as it is.
query = {
"rrf": [
# Keyword search on raw text
{"queryString": {"query": user_query, "defaultField": "text"}},
# Semantic search on text embeddings
{"knn": {"field": "text_vector", "queryVector": q_vec, "k": 5}},
# Semantic search on image embeddings
{"knn": {"field": "image_vector", "queryVector": q_vec, "k": 5}}
]
}
results = lambda_db.collections.query(
collection_name="assets",
query=query
)
Serverless Elasticity for Agent Storms
Compute, memory, and storage scale independently — with automatic shard scaling. No manual sharding. No capacity planning.
Whether it’s a single RAG query or a swarm of recursive agents, our disaggregated architecture instantly adapts using virtual sharding to maintain stable performance. Your ingestion pipeline never blocks your search.
Zero-Waste Scoped Retrieval
Don't search the whole library to find a single page. Retrieve only specific partitions based on tenant or category. Pay only for what you read, not for idle infrastructure.
Deploy 30+ regions worldwide
Deploy anywhere your service runs.
Your data stays where your users are.
Git-like Branching for your collection data
Tame data entropy. Fork your production index in seconds to test new embedding models or hybrid weights. Apply to production only when validated.
Comparison
Why teams choose LambdaDB
Serverless-native vector search. No idle costs, no ops burden, no surprises.
| LambdaDB | Pinecone | Turbopuffer | Milvus (Zilliz) | |
|---|---|---|---|---|
| Monthly minimum | $0 | $50 | $65 | Free (self-hosted) |
| Deployment | Serverless, BYOC | Pod-based, Serverless, BYOC | Serverless, BYOC | Self-hosted, serverless |
| Serverless region availability | 33 regions | 3 regions | 9 regions | 2 regions |
| Index types | Dense & sparse vectors, full-text (BM25), multiple vector fields | Dense & sparse vectors | Dense vector, full-text (BM25) | Dense & sparse vectors, full-text (BM25), multiple vector fields |
| Real-time retrieval | Configurable strong consistency | Not guaranteed | Not guaranteed | Configurable strong consistency |
| Write throughput per collection | >1 GB/s | 117 MB/s | 32 MB/s | 10 MB/s |
| Data branching | ||||
| Partitioning | ||||
| Automatic sharding | ||||
| Continuous backup & PITR |
