ObsessionDB
ObsessionDB provides a fully managed ClickHouse® service engineered for low-latency analytical workloads at scale, using a distributed cache mesh to keep hot data in memory and eliminate S3 round-trips. The platform supports compute-compute separation for isolated agent workloads and includes CHKit, an open-source toolkit for schema management and migrations in TypeScript or Python.
- Data & Analytics
- Enterprise Software
- Developer Tools
- Software Only
- Artificial Intelligence
- AI Agents
Funding
Raised to date
$500KRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
across 1 round
AVSVTYIncomplete round history. $500K of the $500K reported total isn’t matched to a known round (1 known round has an undisclosed amount).
Founders
Founder details are not available yet.
Product
Problem
Traditional managed ClickHouse® solutions and cloud data warehouses impose high costs and latency penalties as data volumes grow, with per-byte billing and architectures that separate storage from serving layers introducing pipeline complexity and data staleness. Queries that scan large datasets become expensive, and teams often suffer from stale data as transformations and copy pipelines delay fresh results.
Solution
ObsessionDB provides a fully managed ClickHouse® service designed for low-latency analytical workloads at scale. It uses a distributed cache mesh as the query layer, ensuring that reads never hit S3 and writes persist asynchronously, keeping hot data always accessible. The platform supports compute-compute separation, allowing agentic workloads to run on isolated, read-only compute without impacting production traffic. It also offers CHKit, a schema management and migration toolkit for TypeScript and Python, enabling teams to manage ClickHouse schemas as code and sync data from APIs directly into ClickHouse tables.
Target Audience
Primary customers are data engineering and product teams serving real-time analytical workloads, including blockchain analytics platforms, DeFi API providers, and organizations with large-scale, low-latency serving requirements.
Features
- Distributed cache mesh that keeps hot data in memory, eliminating S3 round-trips for queries
- Compute-compute separation for isolated, read-only agent workloads that don't slow production
- Full query logging for every agent action, enabling replay and auditability down to the column
- CHKit open-source toolkit for schema management and migrations in TypeScript or Python
- MCP connectivity for Claude, Cursor, Codex, and other AI agents to query databases directly
- Incremental materialized views that update as data arrives, avoiding rescanning large source tables
- CHKit includes pipeline and sync tools for HTTP APIs and databases, with migration review before applying