# Home

## Scale anything with infrastructure that manages itself.

Burla is a self-hostable compute platform for scaling big data workloads in your private cloud.\
Run embeddings, inference, or ML-pipelines with instant feedback and a 2-5x boost in efficiency.

Burla only has one function:

```py
from burla import remote_parallel_map

my_inputs = list(range(1000))

def my_function(x):
    print(f"[#{x}] running on separate computer")

remote_parallel_map(my_function, my_inputs)
```

This example runs `my_function` on 1000 VMs in less than one second:

<figure><img src="/files/Zq7l5LD6L5Jtm3yTWe1v" alt=""><figcaption></figcaption></figure>

## Stop gluing cloud services together.                                  It's 2026, compute infrastructure is trivial now.

Simply define the hardware or container you need inside your code next to the function that needs it.\
Burla can scale up to 10,000 CPUs in a single function call, thousands of GPUs, and any container.

This code:

```python
remote_parallel_map(process, [...], image="rocker/geospatial:latest")
remote_parallel_map(aggregate, [...], func_cpu=64)
remote_parallel_map(predict, [...], func_gpu="A100")
```

Creates a data-pipeline like:

<figure><img src="/files/lnQKTjYvg7BRy3fm93J6" alt=""><figcaption></figcaption></figure>

## Infra that manages itself is over twice as efficient.

Burla vertically scales hardware available to each function call live while the program is running.\
This frequently more than doubles compute efficiency, and eliminates out of memory errors.

<figure><img src="/files/IErsLqmG2xEPpidC86BW" alt=""><figcaption></figcaption></figure>

```python
remote_parallel_map(..., func_ram="dynamic", func_cpu="dynamic")
```

Few workloads use 100% CPU the entire time they run. Burla automatically puts extra space to use.\
Read [our blog post](/blog/dynamic-hardware.md) to learn more about dynamic hardware.

## Code like your laptop has 1000 CPUs or GPUs.

Running code in the cloud shouldn't feel any different from running code locally.

```python
return_values = remote_parallel_map(my_function, my_inputs)
```

When a Python function is run using `remote_parallel_map`, it runs in the cloud but:

* Anything it prints appears locally (and inside the dashboard).
* Any exceptions are thrown locally.
* Any packages or local modules are (very quickly) cloned on all remote machines.
* Code starts running in under one second! Even with millions of inputs, or thousands of machines.

## A full platform for managing compute infrastructure.

Burla has all the features you need to seamlessly scale up and monitor any workload.\
Manage data-pipelines, filter logs, upload/download files in our network filesystem, and much more.

<figure><img src="/files/Cr77N8Bm7vPWDhQvDJWn" alt=""><figcaption></figcaption></figure>

## Pricing:

Zero compute markup.\
Burla is $250/month per enterprise user, and free for everyone else.

<figure><img src="/files/4vINYG7U8VcajkPo3rnV" alt=""><figcaption></figcaption></figure>

## Run your first 1000-CPU job in 2 minutes:

Zero setup required. Sign in, open the Colab, and follow along. It's only 4 steps!

1. [Sign in](https://login.burla.dev/) using your Google or Microsoft account.
2. Run the quickstart in this Google Colab notebook:

{% embed url="<https://colab.research.google.com/drive/1msf0EWJA2wdH4QG5wPX2BncSEr5uVufv?usp=sharing>" %}

***

Questions?\
[Schedule a call](http://cal.com/jakez/burla), or email **<jake@burla.dev>**. We're always happy to talk.


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