Connect everything. Query anything. Agents included.
Exchange a few credentials in the console and Thunderduck makes S3, GCS, Azure, PostgreSQL, MySQL, MsSQL and BigQuery queryable in one place for your analysts, your AI agents via MCP and applications via Alchemy, JDBC and Rest Api. A self-learning engine sizes every query and checks its cost before it runs, so agents can explore freely without blowing your budget or touching production.
From credentials to AI-queryable in about 5 minutes. No pipelines. No warehouse. No data movement.
AI is only as good as its access to your data.
Your data isn't AI-ready
It's scattered across buckets and databases, and the 'fix' is a six-month warehouse + ETL project before a single agent can ask a question.
Agents write expensive queries
An LLM will happily full-scan 400 GB, because it never sees the bill. Warehouses meter the damage after it's done.
Warehouse economics matters
Idle compute and per-credit pricing mean every AI experiment shows up on the invoice — and the trend only goes up.
One engine. Your analysts and your agents. Every source.
AI-ready in minutes
Exchange a few credentials and the engine discovers your catalogs. Point any MCP client at one endpoint and your agents can query across every source.
Zero ETL
Join Iceberg and Parquet in your object store with live PostgreSQL and MySQL in one SQL statement no pipelines, no copies, no sync lag.
Cost Control
Cost-checked before execution, capped by budgets and concurrency limits, fully audited and you choose which connection each source uses.
Runs anywhere
The same engine — MCP server included — deploys as SaaS, in your cloud (BYOC), or fully on-prem for regulated and air-gapped environments.
Making your data AI-accessible?
Point your agents at one MCP endpoint. They discover your catalogs and query across every source — inside budgets you set.
Coming from Snowflake, BigQuery or Athena?
Cut the bill without a risky migration. Data stays in your buckets, in open formats. Run both in parallel while you switch.
Already running DuckDB?
Stop babysitting instances. Bring your workload; the optimizer handles sizing, memory limits and monitoring.
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Your data, AI-ready by lunch.
And at a fraction of warehouse cost — that part still matters.