disclaimer, i'm the data engineer dlthub cofounder., You might know us from dlt, the open source pythonic ingestion library

We built an agentic data pipeline builder for data teams. The first people who lean into it weren't data engineers, but roles that wanted to take the fast lane.

They were GTM engineers, data agencies, and solo data scientists. People paid on outcome, not on stability. A mature data team has rigor, process, and a tool footprint that took them two years to negotiate. They don't want to jump into something new without having validation of outcomes, which for an industry can take years.

A GTM engineer has performance goals in mind - you win on getting things done, not defending the way we do things. A consultancy that can deliver 1.5x faster sees their revenue from 33% (margin) to 83%, a 2.5x increase. These people don't wait around for someone to push them, they are professional builders with different incentive structures who choose performance over process.

What product am I talking about?

dltHub Pro provides Claude/Cursor/Codex users a way to build and run high quality custom code data engineering /GTM pipelines in python, without leaving your chat session. With end to end agentic context and toolkits, most data roles work is possible from simple prompt, including deployment to our infra. Our infra is lightweight serverless, priced slightly above what you'd pay yourself for the same serverless infra. Transparent pricing, no predatory surprises.

This is essentially for individuals or small teams who want to build data stacks quickly without worrying about tools, infra, or even architecture - everything comes with "training wheels" for agents to ensure quality generation.

We use it internally for our gtm ops around attio, enrichments, outreach, community, you know the stuff.

How to try it

run uvx dlthub-start on your agentic cli. there's a no card required trial that includes 30h free run time. If you want to upgrade, the entry tier is $119 (we designed it to be a cheap place to run small-medium ops)

You can find more details if you are interested all over our website and blog.

Thanks for reading!

Disclosure: I cofounded dltHub. Before that I spent 10 years as a data engineer, and dlt started as the library I wish i had, for everyone on the team. Many of you use dlt. Earlier this year dlt reached the milestone of over 10k companies in production.

Today we shipped dltHub Pro.

dltHub Pro is the Claude/Codex/Cursor-native platform that makes data engineering accessible to any Python developer, pairing agents that build dlt pipelines with the runtime that ships them to production.

What you get

• A place to run your dlt pipelines serverless, without overheads.
• One shared context for the stack: dlthub’s agentic toolkits use a shared context that enable writing ingestion, transformation, visualize data, deploy, debug runs and push fixes all from one Claude/Cursor/Codex chat session. Pipeline failed in prod? Tell Claude in your IDE to read the runtime logs and offer a fix.
• Tooling that extends dlt to enable end to end work: dlthub transformations, dlthub data quality, hosted Marimo and Streamlit apps enable you to work end to end.
• Team workspace for uniform local working setup across your team.

What it costs

We offer transparent, consumption-based pricing for managed compute: same class as serverless commodity compute (GH Actions, AWS Lambda), similar hourly billing model as familiar managed warehouses (Snowflake, Databricks). $30 free credit on signup, no card required.

The majority of teams currently running dlt would be sufficiently served by the entry price of $119/month with included 50 runtime hours. Overage costs $1/h.

How can I try it?

To get started with onboarding, run uvx dlthub-start in your CLI.

Who is dltHub Pro for?

We designed dltHub Pro for single professionals or small data teams running a commercial data stack. It removes much of the friction between data engineering workflow steps, enabling single individuals to manage the stack across ingestion, transformation, execution or serving layers in a single session.

What is dltHub Pro for?

building, running, and operating dlt-based ingestion + transformation pipelines end to end, with coding agents doing the build work and the managed runtime handling production.

What dltHub Pro is NOT for

Being serverless is great for small teams at normal scale running batches, but it is expensive for streaming or always-on use cases For medium and enterprise teams or needs, we are preparing dltHub Scale for August and Enterprise for early next year.

Do I need to code to use dltHub?

No, but you really should read any generated code. Through the AI Workbench, we do our best to ensure your generated code follows best practice and is low entropy, easy to maintain.

What does the AI tookits and context actually add on top of my coding agent?

LLMs tend to work like a sloppy junior unless directed otherwise. The AI toolkits serve to guide your LLM into producing high quality outcomes while minimizing risks. The shared context enables the agent to traverse the entire stack from serving to ingestion and translate requirements into end to end code in a single chat session.

Why should I deploy my code to your serverless platform?

We made it so, so simple to build, deploy, run, manage and serve! Unless you're running on bare metal to save cost, you've already accepted that managed compute is worth paying for. We just made it work really well for dlt pipelines and data engineering workflows. Our platform is not vendor locked, and you can easily move your code if the runtime doesn’t meet your needs.

How to start?

$30 free credit on signup, no card required. run uvx dlthub-start in your CLI.

Thank you as usual!
- Adrian