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)
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
Install and query in minutes. Store embeddings + chunks, then retrieve with metadata filters.
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
1,000,000
Results
$3,594.54
Total monthly cost
- Storage
- $161.64
- Ingest (PUT amortized)
- $44.90
- Query
- $3,388.00
- Total size
- 2,694.00 GB
Current inputs: 1,000,000,000 vectors, 256 dimensions, 1,000,000 queries/mo.
Assumes avg chunk 1.5KB, filtered 0.5KB, key 0.17KB, storage $0.06/GB-mo, PUT $0.20/GB amortized over 12 mo, query $0.002/TB, retrieved 2026-02-11.
Comparative economics
The cost curve: EigenLake vs major vector DBs
Same workload, same assumptions - compare the cost curve from 50M to 1T vectors.
Monthly cost (USD)
X-axis: log scale (vectors)
EigenLake at 1T: $3,594,540.00 / month
EigenLake is 1.7x cheaper than Zilliz at this scale (41% lower).
| Scale | EigenLake | Pinecone | Weaviate | Qdrant | Zilliz |
|---|---|---|---|---|---|
| 50M | $179.73 | $2,244.55 | $1,241.00 | $1,090.31 | $319.93 |
| 500M | $1,797.27 | $22,445.51 | $12,185.00 | $10,903.10 | $3,062.56 |
| 1B | $3,594.54 | $44,891.02 | $24,345.00 | $21,806.20 | $6,109.92 |
| 10B | $35,945.40 | $448,910.20 | $243,225.00 | $218,062.00 | $60,962.52 |
| 100B | $359,454.00 | $4,489,102.00 | $2,432,025.00 | $2,180,620.00 | $609,488.46 |
| 1T | $3,594,540.00 | $44,891,020.00 | $24,320,025.00 | $21,806,200.00 | $6,094,747.87 |
Assumes 256 dims, 1,000,000 queries/mo, avg chunk 1.5KB, filtered 0.5KB, key 0.17KB, retrieved 2026-02-11.
Competitor estimates are based on public pricing pages/calculators; actual bills vary by region, HA/replication, and plan.
Common objections, answered
FAQ
Answers to common questions on scale, filtering, ingestion, and migration.
Get architecture feedback
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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)