[anentropic]

I am very interested in this but have some questions after a quick look

It mentions "Serverless pipelines. Run fast, stateless Python functions in the cloud." on the home page... but it took me a while of clicking around looking for exactly what the deployment model is

e.g. is it the cloud provider's own "serverless functions"? or is this a platform that maybe runs on k8s and provides its own serverless compute resources?

Under examples I found https://docs.bauplanlabs.com/en/latest/examples/data_product... which shows running a cli command `serverless deploy` to deploy an AWS Lambda

for me deploying to regular Lambda func is a plus, but this example raises more questions...

https://docs.bauplanlabs.com/en/latest/commands_cheatsheet.h... doesn't show any 'serverless' or 'deploy' command... presumably the example is using an external tool i.e. the Serverless framework?

which is fine, great even - I can presumably use my existing code deployment methodology like CDK or Terraform instead

Just suggesting that the underlying details could be spelled out a bit more up front.

In the end I kind of understand it as similar to sqlmesh, but with a "BYO compute" approach? So where sqlmesh wants to run on a Data Warehouse platform that provides compute, and only really supports Iceberg via Trino, bauplan is focused solely on Iceberg and defining/providing your own compute resources?

I like it

Last question is re here https://docs.bauplanlabs.com/en/latest/tutorial/index.html

> "Need credentials? Fill out this form to get started"

Should I understand therefore that this is only usable with an account from bauplanlabs.com ?

What does that provide? There's no pricing mentioned so far - what is the model?

[zenlikethat]

> or is this a platform that maybe runs on k8s and provides its own serverless compute resources?

This one, although it’s a custom orchestration system, not Kubernetes. (there are some similarities but our system is really optimized for data workloads)

We manage Iceberg for easy data versioning, take care of data caching and Python modules, etc., and you just write some Python and SQL and exec it over your data catalog without having to worry about Docker and all infra stuff.

I wrote a bit on what the efficient SQL half takes care of for you here: https://www.bauplanlabs.com/blog/blending-duckdb-and-iceberg...

> In the end I kind of understand it as similar to sqlmesh, but with a "BYO compute" approach? So where sqlmesh wants to run on a Data Warehouse platform that provides compute, and only really supports Iceberg via Trino, bauplan is focused solely on Iceberg and defining/providing your own compute resources?

Philosophically, yes. In practice so far we manage the machines in separate AWS accounts _for_ the customers, in a sort of hybrid approach, but the idea is not dissimilar.

> Should I understand therefore that this is only usable with an account from bauplanlabs.com ?

Yep. We’d help you get started and use our demo team. Send [e-mail] an email

RE: pricing. Good question. Early startup stage bespoke at the moment. Contact your friendly neighborhood Bauplan founder to learn more :)

[anentropic]

So there's no self-hosted option?

I think currently the docs are lacking some context if you arrive there via a link rather than via your SaaS home page

[jtagliabuetooso]

Thanks a lot for the feedback, point taken!

Wrt deployment: the system has a control plane (on Bauplan AWS, never see any data, just auth and metadata), and data planes for customers (single tenant, private link, Soc2 compliant and all that).

If by hosting you mean "move the data plane to my cloud", that is entirely possible but not as recommended as the managed offering: in the end, the only dependency we have are off-the-shelf VMs in which we install our binary - and your bucket of course, but that is yours.

If you mean "installing the control plane on my cloud", that is not in the cards at the moment, unless a very special deployment is needed.

My suggestion - before complex deployment discussion - is always super simple: try it for free on public datasets and decide if you like it; running the quick start takes three minutes, just send over your email for access.

If you do like it, we can have a discussion on deployment, which has never been a blocker before.

[anentropic]

so like the Lambda funcs in the examples - do I deploy those myself to my own infra? or they have to be defined using Serverless framework and get deployed to Bauplan-controlled infra? are they in the control plane or the data plane?

Just trying to understand how it all fits together

[jtagliabuetooso]

Sorry for the confusing example.

So, the AWS lambda in the data product example is a bit of a red herring, and it's used as the outer process to create branches and launch bauplan pipelines through the Python client (https://github.com/BauplanLabs/data-products-with-bauplan/bl...).

It can be your laptop, an Airflow task, a prefect flow or a step function or a cron job on a VM - it's the "host" process (for the data product we picked lambda because it's the easiest way for people to "run small Python stuff every 5 minutes" - this is a prefect example: https://www.prefect.io/blog/prefect-on-the-lakehouse-write-a...).

When you interact with the Bauplan lakehouse, all the compute happen on bauplan, nothing happens in the lambda: think of launching a Snowflake query from a lambda - the client is in the lambda but all the work is done in the SF cloud. Unlike many (all?) other lakehouses, Bauplan is code-first, so you can program the entire branching and merging patterns with a few lines of code, offloading the runtime to the platform.

The platform itself runs on standard EC2, which contains the dockerized functions needed for execution - typically we manage Ec2 in single tenant, private link, soc2 compliant account we own for simplicity, but nothing prevents the VMs to be somewhere else (given connectivity is ok etc.). It is our philosophy that you should not worry about the infra part of it, so even in case of BYOC we will be in charge of managing that.

Does it help clarify the mental model?

[anentropic]

Totally - got it now, thanks