The agentic compute layer powering vector intelligence beyond search
EigenLake lets agents cluster, model topics, detect anomalies, and track change across large scale datasets.
Workloads
Run vector workloads where the data lives
Search, cluster, detect anomalies, and analyze trends over large-scale vector datasets without stitching together fragmented systems.
What Breaks at Scale
The hard part is the execution layer
Search returns neighbors. Pattern-finding across the full corpus still has to survive compute, permissions, metadata, retries, and a place to keep the answer.
idx.agent.query("find the warranty patterns changing this week")No shared runtime owns the full query path.
Product
EigenLake: one Platform for all your vector needs
EigenLake gives developers one SDK to store vector records, search them, and run ML-style workloads on the same indexed data. EigenStore manages the vectors and metadata. EigenRun turns that stored data into search results, clusters, anomalies, topics, and time-series signals.
EigenLake SDK
EigenStore
The data service inside EigenLake. Create indexes, add records, attach metadata, and keep model-ready vector data in one place.
EigenRun
The execution service inside EigenLake. Run nearest-neighbor search, clustering, agent queries, and higher-level workloads against stored records.
Pricing
Same platform. Different scale.
Every plan includes the full EigenLake's platform: EigenStore for storage and metadata, and EigenRun for ML workload execution. The difference between plans is usage: stored vectors, search units, and executions.
Starter
Pro
Custom
Try EigenLake free in the live sandbox, or schedule a call for production scale and deployment questions.
More details
Included in every plan
All plans include the same core product capabilities.
Search unit policy
Each search unit returns up to 100 results. Requests returning more than 100 results consume additional search units in blocks of 100.
FAQ
Questions about EigenLake
What is EigenLake?
EigenLake is a platform for storing vector data and running useful workloads on top of it. EigenStore gives embeddings, metadata, documents, and events a durable home. EigenRun lets teams search, cluster, model topics, detect anomalies, and analyze time-series signals against that stored data.
What workloads can run on EigenLake?
The landing page focuses on five workload families: search, anomaly detection, topic modeling, clustering, and time-series analysis. Search and clustering use the current client primitives directly, while other workload previews use agent queries until dedicated EigenRun APIs are available.
How is this different from a vector database?
A vector database is usually optimized for nearest-neighbor retrieval. EigenLake keeps that retrieval experience, then adds a unified data layer and execution layer for running higher-level vector workloads without copying embeddings into separate systems.
What is a search unit?
A search unit returns up to 100 results. If a request returns more than 100 results, it consumes additional search units in blocks of 100.
Can I try it now?
Yes. The sandbox lets teams try EigenLake in a live environment, and the docs cover the current Python client for connecting, creating indexes, adding records, searching, clustering, and using agent queries.
Schedule a call
See what your AI stack looks like when vector data and execution live in one system
Talk through your data, scale, and first workload with the EigenLake team.