A memory-friendly vector database for AI workloads. It keeps large vector indexes mostly on disk — cutting RAM needs and lowering the cost of vector search at scale.
Try Asteroid on a real RAG or semantic-search workload. We help you measure memory footprint, query behavior, and whether Asteroid can lower your bill — free during the pilot.
The same vector search, on cheaper machines — because the index lives mostly on disk, not in RAM.
Asteroid keeps most index data on disk instead of RAM. As your vector dataset grows, memory stays small and predictable.
Lower RAM needs mean smaller, cheaper cloud machines — lower monthly spend and more predictable costs. See how the cost works.
Solid build and query performance while using far less memory — comparable with the leading vector databases we tested. See the benchmarks.
# pip install lsmvec-client
from lsmvec_client import Client
client = Client(api_key="sk-live-...",
base_url="https://api.lsmvec.com")
client.insert(1, [0.10, 0.20, ...],
metadata={"category": "docs"})
hits = client.search([0.10, 0.20, ...], k=10)
for h in hits:
print(h.id, h.distance)Tell us your use case and rough scale — we'll follow up by email.