● data & ai infrastructure

Describe the data pipeline.
AI ships it to production.

Your team describes the data pipeline. The agent writes the Airflow DAG, tests it, deploys it and watches every run. Dexaflow is the first product from DexaData, data and AI infrastructure as a service.

Join the waitlist View on GitHub

● available today: Dexaflow OSS○ coming soon: Dexaflow Cloud with AI

works with the stack you already run

  • dbt
  • DuckDB
  • Snowflake
  • Databricks
  • BigQuery
  • Google Cloud
  • AWS
● use cases

Data engineering,
without the busywork.

Dexaflow is a data engineering tool. It takes on the repetitive part of the job: moving data between systems, cleaning it, running it on schedule and fixing it when it breaks. A few things teams ask for:

● SALES

Daily sales report

“Every morning at 6, pull yesterday's orders from the store database and show revenue by state on the dashboard.”

→ the dashboard is current before the team logs in

● FINANCE

Month-end close

“Combine the ERP, the bank statements and the finance spreadsheets every night, and flag what doesn't match.”

→ nobody copies and pastes between spreadsheets

● MARKETING

Return per campaign

“Bring in the Google Ads and Meta spend every day and cross it with sales.”

→ cost and return per campaign on one screen

● CRM + BI

Customers in one place

“Copy Salesforce into BigQuery every hour so our Looker and Power BI reports read from it.”

→ reports read today's data, not last week's export

● AI / ML

Fresh data for models

“Refresh the tables that feed our models and assistants every day, and warn me if the data looks off.”

→ models and assistants answer with current numbers

● AIRFLOW

Airflow without the servers

“Move our DAGs to Dexaflow and stop maintaining the cluster.”

→ the same DAGs, no infrastructure to look after

● FROM REQUEST TO PRODUCTION

By hand

  1. Write the Python code
  2. Set up connections and credentials
  3. Build the image and the CI/CD
  4. Test on sample data
  5. Configure alerts
  6. Dig through logs when it fails

With Dexaflow AI coming soon

  1. Describe it in one sentence
  2. Review the diff
  3. Approve

The agent does the rest and watches every run.

Who it is for

  • Data engineers

    Skip the boilerplate: code, connections, tests, CI/CD and alerts. You review the diff, and the code stays yours.

  • Data leaders and CTOs

    Ship more data products without growing the team or running a cluster.

  • Analysts and business teams

    Ask for the data in plain language and get it ready in the warehouse, with an engineer approving the change.

● dexaflow ai · coming soon

The Airflow experience,
built with AI.

You say what the data needs to do. Dexaflow handles the rest: code, tests, deploy and operations. It is still a Python DAG your team reads, reviews and versions.

● NATIVE DBT

Native dbt, on the warehouse you already use

Point to your dbt project. Each model becomes an Airflow task, with dependencies, tests and lineage on one screen. Dexaflow installs the adapter, builds profiles.yml from your connections and keeps the credentials.

  • Databricks
  • BigQuery
  • Snowflake
  • DuckDB
# dexaflow.yaml
dag_id: sales_daily
dbt:
  project: ./analytics
  target: bigquery   # databricks | snowflake | duckdb
  select: daily_revenue+
  tests: true
● SELF-HEALING

Failures explained and fixed

When a task fails, the agent reads the logs, explains the cause in one sentence and proposes the fix as a diff. You approve and it ships.

● MIGRATION

AI-guided Airflow migration

Point it at your DAG repository. The agent reviews each one, adjusts what does not run on Dexaflow and shows you the plan before migrating.

● MCP SERVER

Your agent runs your data pipelines

A native Model Context Protocol server. Claude, Cursor or any compatible agent reads the context, creates pipelines, triggers runs and follows the logs.

● DEXAFLOW.YAML

Declarative data pipelines, no Dockerfile

One short file describes the DAG, the Python version and the dependencies. It is the format the agent writes and you review.

You stay in control. The agent proposes, you review the diff and decide what goes to production. The code is yours, in Python, in your repository.

● the engine · apache airflow compatible

//Dexaflow

Airflow compatible.
Built for production.

The same UI and REST API as Apache Airflow, with a Go control plane underneath. Your DAGs stay in Python. What changes is what happens when they run.

01_ Airflow compatible

The unmodified Apache Airflow 3.2 UI and the same REST API. DAGs, tasks, variables and connections, in the vocabulary your team already knows.

02_ Go control plane

Scheduling and coordination outside the Python loop. No scheduler stalling between tasks and no re-parsing every DAG on every cycle.

03_ One task, one pod

Every task runs in its own pod. No long-lived workers piling up memory and connections until they die overnight.

04_ Dependencies per DAG

Each DAG gets its own Python environment. pandas 1 in the legacy DAG and pandas 2 in the new one live side by side.

05_ Map-reduce for ML and AI

Fan-out and reduce written as a Python list comprehension. No XCom plumbing, no broker, no special operator.

06_ From laptop to cluster

The same engine runs on a single host, no Kubernetes, or with Helm on your cluster. Start small and rewrite nothing to grow.

● open core

Open core.
Managed cloud.

The engine is open source and runs wherever you want. Dexaflow Cloud is the same engine, run by us, with AI built in.

Dexaflow OSS

Apache 2.0

For teams that want to run it on their own infrastructure.

  • The full engine, compatible with the Airflow UI and API
  • Lite on a single host or Pro with Helm on your Kubernetes
  • An MCP server to use with your own agent
  • You operate it: upgrades, database, security and scale
Code on GitHub Read the docs →

Dexaflow Cloud

coming soon

For teams that want data pipelines in production, not a cluster.

  • Everything in OSS, run and upgraded by DexaData
  • Built-in AI builder: generate, test and ship with nothing to set up
  • AI-assisted failure fixes and Airflow migration
  • Corporate login, teams and plans starting at Free
See pricing

Dexaflow OSS is the new name for Leoflow. The code is open at github.com/dexadata/dexaflow.

● dexaflow cloud · coming soon

Managed Dexaflow,
no cluster to babysit.

Capacity measured in DCUs, clear limits per plan and no cluster to look after. Start on Free and move up when your volume asks for it.

● what is a DCU

Capacity measured in DCUs, the Dexa Compute Unit.

Each plan sets how many DCUs run at the same time. You pick the size of each task in dexaflow.yaml; if you say nothing, a task uses 1 DCU. When work goes over the limit, extra tasks wait and run as soon as there is room, without failing.

Example: with 6 DCUs on Small, you run 6 tasks of 1 DCU in parallel, or 3 tasks of 2 DCUs.

Free

$0

To try it out with real data pipelines.

Concurrent DCUs
2
Task runs / month
1k
Active DAGs
5
Total tasks
20
Minimum interval
1 h
Users
1
Log retention
7 days
Join the waitlist

Small

for teams getting started

$50/month

For the first data team in production.

Concurrent DCUs
6
Task runs / month
25k
Active DAGs
30
Total tasks
150
Minimum interval
15 min
Users
3
Log retention
14 days
Join the waitlist

Medium

$250/month

For several teams and minute-level schedules.

Concurrent DCUs
20
Task runs / month
250k
Active DAGs
150
Total tasks
1.5k
Minimum interval
1 min
Users
10
Log retention
90 days
Join the waitlist

Large

$900/month

For the whole company's data platform.

Concurrent DCUs
80
Task runs / month
2M
Active DAGs
500
Total tasks
10k
Minimum interval
1 min
Users
Unlimited
Log retention
1 year or more
Join the waitlist
  • Every plan includes the MCP Server, dexaflow.yaml pipelines and the Airflow-compatible UI.
  • On Free, schedules with no dashboard or MCP access for 30 days are paused. We email you first.
  • Prices in US dollars, per month. Prices and limits may change before launch.

Enterprise

A dedicated environment, custom limits and support with an SLA. Or run Dexaflow OSS on your own cluster.

Talk to us →

Built to hold your credentials

  • Isolated per organization. Every task runs in its own pod; on Free, inside a gVisor sandbox.
  • Your data stays in your warehouse. Dexaflow orchestrates the work and keeps no copy of your tables.
  • Encrypted credentials. Connections are stored encrypted and masked in the logs.
  • Open source you can audit. The engine code is public, under Apache 2.0.
● why now

A large market,
still built by hand.

Companies run more data pipelines every year, and most of them are still written, fixed and operated by hand. Teams already reach for AI to write them, but generic assistants stop at the code.

● COST

The same pipelines for a fraction of the cost

Why it costs less

  • No idle environment. Each task runs in its own pod and stops when it ends. Nothing sits running waiting for the next schedule.
  • Backend rewritten in Go. Scheduler, API and control plane are compiled Go instead of Python processes: less CPU and memory for the same work, shared and isolated across customers, so nobody pays for a scheduler, web server and database of their own. Your DAGs stay in Python.
  • You pay for the task, not the peak. Capacity is counted in DCUs per task, not in a cluster sized for the busiest hour.

Sources: Google Cloud Managed Airflow (formerly Composer 3) at the US$0.06 per DCU-hour list price in us-central1; small is Google's 12-DCU example, medium was measured on a real environment's bill (September 2026, about 13.7 DCUs on average). Astronomer Astro Team, its production plan, pay as you go: Small from US$0.42/hour, Medium US$0.57/hour, plus one A5 worker at US$0.13/hour. All running 24/7, compute only, October 2026 list prices. Neither has a free plan; Astro offers a 14-day trial.

Our bet: the AI that writes data pipelines should also test, ship and run them, with the context of your environment. Dexaflow does that on the Airflow standard, with an open-source core and a managed cloud billed by usage.

Investor? Talk to us

Market and survey figures are third-party estimates. Follow each source for its method and date.

● about dexadata

DexaData builds the data and AI infrastructure teams use as a service, without assembling a platform. Dexaflow is the first product. The next ones follow the same rule: an open core, AI in the workflow and operations on us.

● get in touch

Let's get your
data pipelines running.

Want early access to Dexaflow Cloud, to migrate DAGs from Airflow or to run Dexaflow OSS at your company? Tell us about your setup. We usually reply within one business day.

email [email protected]
● OPEN SOURCE

Open source under the Apache 2.0 license. No lock-in.

● AIRFLOW COMPATIBLE

Your DAGs and the Airflow vocabulary still apply.

● AI NATIVE

AI proposes, you approve. The code stays yours.