Built for frontier RAG teams
A vector database built for trillion-scale RAG
Production retrieval infrastructure that stays performant and predictable as your embeddings grow from millions to trillions.
- Trillion+ scale (cloud-native)
- Predictable pricing at massive scale
- Store embeddings + text chunks together
Book a 20-min slot (Zoho Calendar)
Prefer self-serve integration? API Docs and Python Client Docs.
Platform advantages
Why EigenLake
Trillion+ scale, lower cost at massive scale, and RAG-ready retrieval.
01
Trillion+ scale
Storage/compute separation with an object-storage-first architecture so capacity scales without re-platforming.
02
Lower cost at massive scale
Dense storage + amortized ingestion + pay-for-query economics designed for predictable cost as vectors grow.
03
RAG-ready retrieval
Fast filtered retrieval over metadata + chunks to support production RAG workloads.
Developer experience
Start with the Python SDK
Use API Docs for REST endpoint integration, Python Client Docs for SDK workflows, and the API endpoint for direct service access.
Python SDK
pip install eigenlake
from eigenlake import Client
db = Client(api_key="YOUR_API_KEY", region="us-east-1")
index = db.index("docs", dims=256)
index.upsert([
{"id": "doc-1", "vector": v1, "text": "...", "meta": {"source": "wiki"}},
])
results = index.query(vector=q, top_k=10, filter={"source": "wiki"})
print(results[0]["text"])Pricing clarity
Cost calculator
Estimate monthly cost for your workload in seconds.
Inputs
1,000,000,000
256
10,000
0.00 KB
Results
$505.81
Total monthly cost
- Storage
- $319.81
- Ingest (PUT amortized)
- $88.84
- Query
- $97.17
- Total size
- 5,330.15 GB
- Monthly query volume
- 48,583.73 TB
Current inputs: 1,000,000,000 vectors, 256 dimensions, 10,000 queries/mo, 0.00 KB metadata/record.
Assumes float32 vectors, avg chunk 4.14KB, filtered chunk 3.66KB, key 0.17KB, metadata = user input, storage $0.06/GB-mo, PUT $0.20/GB amortized over 12 mo, query $0.002/TB.
Published comparison
768d price comparison at 100,000 queries/mo
One dimension, one query tier, and a shared set of vector scales. This removes the noise from the comparison.
Published monthly prices for 768-dimensional vectors at 100,000 queries/mo, normalized from the provided scenario sheet.
The 50M Weaviate bar is clipped visually so lower-priced vendors remain readable; the exact value is shown in the label and value grid.
Common objections, answered
FAQ
Answers to common questions on scale, filtering, ingestion, and migration.
Get architecture feedback
Prefer email? Contact us directly.
Share your use case, current scale, and what you are trying to improve. We reply with practical next steps.
Talk to the founders
Book a 20-min call - we'll sanity-check your scale + cost assumptions and recommend an EigenLake rollout plan.
Talk to the foundersBook a 20-min slot (Zoho Calendar)