Build, govern, heal and serve data infrastructure for the AI era
There is no limit when it comes to data infrastructure; since dltHub is made up of code, the engineers and their agents will extend it to wherever your data is located.
Build
Your coding agent writes the pipeline, for any REST API, database or file, with ready-made context for the source.
Govern
Expectations, schema contracts and PII redaction run on the way in, so bad data stops before the warehouse.
Heal and serve
Failures reach their owner with the diagnosis attached, and governed models serve every consumer on the same scheduler.
The dltHub platform
One agentic layer, one runtime underneath it
Your coding agent builds and fixes pipelines through the AI harness and the context graph. dltHub runs what it writes: scheduled, observable, with secrets and regions handled. It reaches the sources your GUI tool has no connector for, and lands them in the warehouse you already bought.
Your coding agent
Claude Code, Cursor or Codex
operates through↓
agentic layer
What the agent works through
dltHub AI harness
Agentic primitives to build, run and fix pipelines
dltHub context graph
Lineage, schema, data quality, governance, run state
grounds and runs↓
dlt Sources
The systems a GUI tool has no connector for
+10,000 more
managed infrastructure
What dltHub runs for you
Warehouse
any warehouse, yours
Every part of the platform
Managed infrastructure
Scheduling, instance sizes, observability and the CLI, with nothing to keep warm between runs.
See the platformTransformations
Annotate your sources, generate the canonical model, and keep your dbt models running.
See the modelsData quality
Expectations, schema contracts and PII redaction, running on the way into the warehouse.
See the checksAI harness
The skills, commands and MCP servers your coding agent calls to build and fix pipelines.
See the harnessSecurity and compliance
Access controls, credential handling, encryption, data residency and audit, with the control set published.
See the controlsPricing
Pick a machine size per job and pay by the second, with the meter running only while the job does.
See pricing
"What I didn't expect is how much it unblocks the team. A mid-level engineer can spin up a prototype, browse the raw data in dltHub's local DuckDB workspace, validate the SQL schema - all without pulling in a senior. That loop of prototype, inspect, fix, re-run - that's the real unlock."
Marcello Victorino
Staff Data Engineer, Tasman Analytics
Marcello Victorino
Staff Data Engineer, Tasman Analytics
See dltHub on your own sources
Thirty minutes with our team. We build a pipeline for a source you actually load and run it on the platform.
Load Oracle BICC into Snowflake, hourly
dlthub-router
routed to sql-database
create-sql-database-pipeline
scaffolded oracle_bicc, 14 tables
deploy-workspace
Frequently Asked Questions
What is dlt?
dlt (data load tool) is an open-source Python library for building data pipelines. It handles schema inference, incremental loading, nested data normalization, and works with 10,100+ sources. Apache 2.0 licensed and always free to use.
What is dltHub?
dltHub is the managed agentic platform for running dlt pipelines in production. It bundles a managed runtime (deploy with one command, no infra to patch), Python and SQL transformations orchestrated inside your pipeline, data quality checks that fail fast with actionable errors, a managed Iceberg lakehouse with the option to bring your own storage, and an MCP server so agents can analyze pipelines and datasets directly. The outcome: teams ship trustworthy data faster, without owning the infrastructure. See the full feature list in the dltHub docs.
How is dltHub different from a Claude skill or tools like Replit?
Tools like Claude skills or Replit are great for writing and running code. But they are not built for data engineering workflows end to end. dltHub gives your team complete agentic workflows that cover every phase: coding, running, deploying, and debugging pipelines, on infrastructure you control.
How is dlt different from Fivetran or a Python script that uses the request library?
dlt is the perfect match between standardization and customization. You get the automation that matters: schema inference, incremental state, normalization, and loading, while keeping the full flexibility and portability of plain Python. And with agentic dltHub workflows, your team can code, run, deploy, and debug pipelines faster.
How do I get access to dltHub?
dltHub is available now. Book a demo with our team to get set up, or see our pricing page for plans and what's included.