Asteroid is a memory-friendly vector database for AI workloads. It keeps large vector indexes mostly on disk, reducing RAM needs and helping teams lower AI infrastructure cost.
Try Asteroid on a real RAG or semantic search workload. Free during the pilot.
Lower memory usage is the path to lower AI infrastructure cost.
Asteroid keeps most index data on disk instead of RAM. As your vector dataset grows, memory stays small and predictable.
Less RAM means smaller machines, simpler capacity planning, and lower cost for AI workloads that rely on vector search.
In our benchmarks, Asteroid delivers solid build and query performance while using far less memory — comparable with the leading vector databases we tested. See the benchmarks.
Bring your embeddings. Insert vectors. Search with metadata filters. Asteroid handles storage and k-NN.
Full quickstart →# 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)The basics you need for AI search workloads.
Search vectors and filter by metadata in the same query.
Store text, source, tags, prices, or any JSON metadata with each vector.
Load a new index in one pass instead of inserting vectors one by one.
Insert, replace, and delete vectors without rebuilding the index.
Euclidean and cosine similarity, accelerated with SIMD.
Use the Python client or call the REST API from any language.
Tell us your use case and rough scale. We'll follow up by email.