EigenLake is a platform designed for storing vector data and executing a range of machine learning workloads directly on that data. It addresses the challenge of running complex analyses—such as clustering, topic modeling, anomaly detection, and time-series analysis—on large-scale vector datasets without the need to move data between disparate systems or build custom pipelines. The platform is positioned as an integrated solution that goes beyond traditional vector databases by combining both data storage and an execution layer for higher-level vector intelligence tasks.
The core architecture of EigenLake consists of two main components: EigenStore and EigenRun. EigenStore serves as the data service, allowing users to create indexes, add records, attach metadata, and store model-ready vector data in a centralized location. EigenRun provides the execution service, enabling operations like nearest-neighbor search, clustering, agent-driven queries, and other advanced workloads such as anomaly detection, topic modeling, and time-series analysis. The platform supports up to 1024-dimensional vectors, with each vector able to store up to 10 KB of metadata, of which 2 KB can be filterable. Search operations are measured in search units, each returning up to 100 results, with additional units consumed for larger result sets.
EigenLake is aimed at developers, data scientists, and teams working with large-scale vectorized data, such as support tickets, logs, sensor events, and operational notes. It provides a Python client for connecting, indexing, searching, clustering, and running agent queries. The platform can be tried in a live sandbox environment, allowing users to experiment with its capabilities before committing to a production deployment.
Pricing is structured into three tiers: Starter at $50 per month, Pro at $500 per month, and a Custom plan with negotiable terms. All plans include the full EigenLake platform—EigenStore for storage and metadata, and EigenRun for workload execution—with differences based on usage limits for stored vectors, search units, executions, and compute hours. This unified approach is intended to streamline the workflow for teams needing both storage and advanced analytics on vector data within a single system.
EigenLake is a Databases (SQL, NoSQL, vector, graph) product. It focuses on running advanced vector-based analytics and AI workloads on large datasets without complex infrastructure. It is built as a B2B product for data scientists and AI engineers. It runs on the web, the command line, and API.
EigenLake first shipped in 2026. PulseGate's similarity index finds few close equivalents — EigenLake occupies a relatively distinct niche. Key capabilities include vector search, anomaly detection, and topic modeling. It exposes integrations via a public API.
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