Whether you're running processing large datasets or highly scaleable backend apps, you need infrastructure that can actually handle the load without burning through your budget. Having spent considerable time in this space and testing various platforms with real-world applications over the years, in this article, we’ll share an honest, detailed comparison of your hosting options for CPU-intensive python apps.

Table of contents

• Quick platform overview

• Detailed platform reviews

• Making a choice

• The bottom line

Quick platform overview

First, let's look at what we're dealing with. I've tested each platform using a real FastAPI application that includes data processing endpoints and ML model inference - you know, the kind of stuff you'll actually build in production.

Platform Free Tier ML Support GPU Access Auto-scaling Best For Cerebrium $30 credits + 50GB storage Native Yes Advanced ML & Data Processing Railway $5 monthly credit Good No Basic Python Apps Beam 15 hours (Approx $5) Native Yes Basic ML & Data Processing Render 750hrs/month Basic No Basic Web Services PythonAnywhere 1 web app Limited No No Learning

Detailed platform reviews

Cerebrium: We’re built for data-intensive ML workloads

Let me be upfront here - I work at Cerebrium, and we built this platform specifically because we were frustrated with deploying ML models and data processing applications on platforms that weren't designed for it. Here's what you get with us:

A free tier that makes sense

We're not playing games with the free tier. You get:

• $30 in credits that let you properly test your application

• 50GB persistent storage (yes, actually 50GB, not the usual 1-3GB you get elsewhere)

• Additional credits for startups because we want you to succeed

• Full access to all features - no artificial limits on compute power

• Python-native environment

• No credit card required to start

Here's why this matters: When you're working with ML models or processing data, you need enough resources to actually test your application under real conditions. Those "hobby tier" limits on other platforms? They'll have you hitting walls before you can even validate your setup.

Costing to suit your growth

We've made our pricing dead simple:

• Pay-per-use at $0.000026 per second for a basic CPU application (memory & CPU pricing included)

• Only pay when your code is actually running

• No hidden fees or surprise bills at the end of the month

• Typical costs range from $20-100 for moderate workloads

Let me break this down with a real example: If you're running a FastAPI application that processes data for 8 hours a day, you're only paying for those 8 hours. Your 3 AM quiet period? That's not costing you anything.

Support from your peers

This isn't your typical "have you tried turning it off and on again" support:

• Direct access to our engineering team

• Access to our discord community

• Response times in hours, not days

• Engineers who've actually deployed ML models

• Proactive monitoring - we often spot issues before they impact your application

Join our discord, here.

Deployment That Just Works

Here's what deployment looks like with us:

• Configure your Python environment

• Deploy your application securely and expose your endpoints

• We handle dependency installation and management

• We automatically handle the scaling of your application based on your configuration settings

• Monitor your application in real-time from your own highly personalized dashboard

Check out our full deployment guide, here.

What Sets Us Apart

Let's talk about features that matter for data and ML apps:

• Autoscaling that understands different workloads:

  • Scale to zero when inactive (yes, actually zero cost)

  • Scale up based on actual resource usage, not just request count

  • Smart concurrency handling for batch processing

• GPU access that makes sense:

  • Switch between CPU and GPU through your dashboard

  • Deploy frontend and inference applications in the same environment

  • Pay only for resources (CPU, GPU, Memory & Storage) you actually use

• Real debugging capabilities:

  • Full logs access

  • Performance metrics

  • Resource usage tracking

Being Honest About Our Limitations

We're not perfect for everything:

• There's a learning curve for complex configurations

• Our documentation assumes some development knowledge

• If you're building a simple blog, we're probably overkill

Railway: Modern Python Deployment

Railway has been making waves in the Python community, and I can see why. They've built a platform that developers enjoy using, though it comes with its own quirks.

Free Tier Worth Noting

Their free tier looks good on paper:

• $5 trial credit (note: trial, not monthly)

• Includes a PostgreSQL database

• No project number restrictions

• Automatic HTTPS and custom domains included

What makes this interesting is that you can actually build something real with these resources, but keep in mind that $5 credit runs out quickly with active development.

Deployment Experience

Their process is modern but has some friction points:

• One-click template deployments (nice for getting started)

• GitHub integration that works well

• Deployment takes 2+ minutes for a basic FastAPI app

• Configuration managed through railway.json (more complex than it needs to be)

• Good but sometimes convoluted environment variable management

The template system is a standout feature - you can deploy common setups like:

• Full-stack Python applications

• PostgreSQL databases

• Redis instances

• MongoDB deployments

• MySQL servers

Cost Structure

Railway's pricing is simple but needs watching:

• Usage-based pricing that's easy to understand

• Starting around $10/month for production workloads

• Separate pricing for databases and additional services

• Credit system that helps track usage

Here's the thing though - while the pricing is clear, you'll need to watch your usage. Those background data processing jobs can eat through credits faster than you might expect.

Limitations to Consider

Being honest about the downsides:

• ML support is more basic than specialized platforms

• Resource limits can surprise you

• Configuration through railway.json can be confusing

• Deployment times are longer than advertised

• Less control over the underlying infrastructure

Beam: Python-Native Serverless Platform

Beam takes an interesting approach with their serverless platform built specifically for Python. Let's look at what they offer for data processing applications.

Free Tier Reality

Their offering is straightforward:

• 15 hours of free compute

• Python-native environment

• No credit card required to start

What's refreshing here is that you get access to all features right away - no artificial limitations on what you can build or deploy.

The Code Experience

Beam's approach to deployment is unique. Instead of complex configurations, you just add decorators to your Python functions:

from beam import schedule, Image

@schedule(
    when="@weekly",
    name="my-weekly-job",
    cpu="100m",
    memory="100Mi",
)
def process_data():
    return {"status": "completed"}

This simplicity is powerful - what you write is what you deploy.

Deployment Reality

Their deployment process is clean, however, it also has some issues:

• Single command deployments

• Automatic environment management

• Built-in scheduling capabilities

• Support for multiple Python frameworks

• Easy dependency management

• We often have customers moving over to us because of beam’s slow cold-start times, long deployments and poor reliability.

The platform does, however, handle the heavy lifting of containerization and deployment, which is particularly great for data processing workloads.

Limitations to Consider

Being honest about the tradeoffs:

• 15-hour limit on free tier isn’t much, especially considering that most python apps need to be always-on

• Less control over underlying infrastructure

• Primarily focused on serverless workloads

• Some advanced features require paid tier

• Would need to really need to test your ability to iterate quickly

Render: Modern platform with limits

Render aims to simplify deployment, but comes with some specific requirements and limitations for Python applications.

Free Tier Details

The free tier comes with strict monthly limits:

• 750 instance hours per service

• 100 GB bandwidth

• 500 pipeline minutes

• Forces sleep after 15 minutes of inactivity (problematic for ML services)

• Usage beyond these limits gets charged automatically

To put this in perspective, 750 hours covers a single service running continuously for a month (720 hours), but you'll need to watch your bandwidth and build minutes carefully.

Deployment Requirements

Here's what you need to know about deployment options:

• Only three ways to deploy:

  1. GitHub integration (most common)

  2. Public GitHub repository

  3. Docker image

• Requires a render.yaml configuration file:

yaml
Copy
services:
  - type: web
    name: fastapi-example
    runtime: python
    plan: free
    buildCommand: pip install -r requirements.txt
    startCommand

• Quick-start templates available for common setups

The GitHub-only deployment might be a dealbreaker if you're using other version control systems. The render.yaml configuration is straightforward but less flexible than some alternatives.

Real Costs

• $7/month starting point

• Resource scaling gets expensive

• Additional costs for any serious storage needs

• Bandwidth costs can surprise you

• Automatic charging for exceeding free limits

Practical Limitations

Important to understand:

• Resource constraints hit fast

• Limited ML framework support

• Sleep on free tier affects service availability

• Basic scaling options only

• Deployment options restricted to GitHub or Docker

• Need to carefully monitor bandwidth and build minutes

PythonAnywhere: Stuck In the Past

PythonAnywhere is interesting - great for learning, but let's see how it handles production workloads.

Free Tier Realities

Clear but limited:

• One web application

• Heavily restricted CPU access

• Basic Python console

• Limited outbound network access

Perfect for learning, problematic for production data processing.

Cost Structure

• I’m still not sure how this works (You’ve got a limit of 100 CPU seconds that reset every 24 hours and 512 MB of storage capacity) and would opt for a different platform just because costs aren't transparent

• Additional resources get expensive

• Fixed pricing regardless of usage

Real-World Performance

The limitations become clear in production:

• Resource restrictions affect processing speed

• No GPU access for ML workloads

• Limited for production applications

• Basic deployment options only

Making a choice

Look, choosing a platform comes down to what you're actually building. We suggest:

• For ML, data processing and backend apps:

  • Choose Cerebrium if you need serious compute power and ML support

  • Consider Railway if you want a good balance of features and simplicity

  • Look at others if you have very basic needs

• For Standard Web Apps:

  • Railway is great for typical Python applications

  • Render works well for simple deployments

  • PythonAnywhere is perfect for learning

The bottom line

After working with all these platforms, here's the truth: if you're doing serious data processing, backend or ML work, you need infrastructure built for it. While platforms like Railway offer great developer experiences, and others have their specialties, you'll hit fewer walls with a platform that understands data workloads from the ground up.

That said, always test your specific use case. The free tiers are there for a reason - use them to make sure the platform fits your needs before committing.