The 7 best Modal AI alternatives for developers in 2025

So, you’re using Modal. It’s a fantastic tool for getting Python code, machine learning models, and big batch jobs running in the cloud without a headache. That serverless approach to GPU computing is a real lifesaver for quick experiments. But as your project gets bigger, you might start noticing a few things, like unpredictable costs or platform limits, that have you wondering about Modal AI alternatives.

If you’re building an AI app and thinking, "What else is out there?", you’ve come to the right place. We’re going to look at two main types of alternatives: platforms that help you solve specific business problems without the DevOps deep dive, and others that give you more raw power and flexibility for your custom code.

What is Modal AI?

Let’s quickly recap. Modal is a serverless platform that lets developers run code in the cloud without having to mess with the underlying infrastructure. You can literally take a Python function, pop a decorator on it, and run it on some seriously powerful hardware (hello, H100 GPUs). People love it for its near-instant cold starts and a dev experience that feels like you’re still working on your local machine.

It’s most often used for things like:

• Running inference for large language models and image generators.

• Fine-tuning open-source models on custom data.

• Scaling up batch processing jobs to run across thousands of containers at once.

Basically, it’s for any developer who’d rather write code than get bogged down in Kubernetes configurations and endless YAML files.

Why look for Modal AI alternatives?

While Modal is a champion of simplicity, that focus comes with some trade-offs. As projects mature from a cool prototype into a real production system, a few common pain points tend to crop up.

• It’s tough to build full applications: Modal is a beast at running individual functions, but it wasn’t really designed for orchestrating a complete application with a frontend, a backend, and a database. Trying to glue multiple services together can feel a bit clunky.

• Costs can get unpredictable: The pay-per-second model is perfect for short, occasional tasks. But for jobs that need to run for a while or require constant GPU access, those seconds add up fast, and forecasting your monthly bill can become a real guessing game.

• You get locked in: The platform works its magic using Modal-specific code and infrastructure. That’s great for getting started, but it makes moving your application to another cloud provider a major refactoring project.

• Limited control and networking: You can’t use your own cloud account (BYOC) or deploy into your own private network (VPC). For bigger companies, this can be a non-starter due to data security, residency, and compliance rules.

• No built-in CI/CD or Git workflows: If you want a proper development pipeline with automated tests and deployments triggered from Git, you have to bolt on external tools. This adds back some of the complexity Modal was meant to help you avoid.

Our criteria for choosing the best Modal AI alternatives

To make this list actually useful, we picked platforms that directly address some of the gaps in Modal. We looked at each one through a few key lenses:

• Main purpose: Is it designed to build a specific AI product (like a support agent), or is it for general-purpose coding?

• Pricing model: Is the pricing clear and predictable, or will you need a spreadsheet to figure it out?

• Developer experience: How easy is it to get your code deployed and keep it running?

• Scalability and control: Can it handle real production traffic, and does it offer advanced features like BYOC?

A quick comparison of the top Modal AI alternatives

Here’s a high-level look at how our top picks stack up against each other.

Platform	Best For	Pricing Model	Key Feature	BYOC Support
eesel AI	AI customer support automation	Per interaction (predictable)	Go-live in minutes, no infra needed	No
Northflank	Full-stack AI product deployment	Usage-based	Git-based CI/CD & GPU orchestration	Yes
RunPod	Budget-friendly GPU compute	Pay-per-second	Low-cost GPU marketplace	No
Replicate	Public model demos & APIs	Pay-per-second	Easiest way to deploy open-source models	No
Baseten	Internal ML tools & model APIs	Usage-based	Integrated UI builder for demos	No
AWS SageMaker	Enterprise-grade MLOps	Usage-based (complex)	Deep AWS integration & security	Yes
Anyscale	Distributed training with Ray	Usage-based	Scalable Ray-based compute	Yes

The 7 best Modal AI alternatives in 2025

Alright, let’s dive into the details. Here are the platforms we think are worth considering once you’re ready to move past Modal.

1. eesel AI

First up is a different kind of alternative. If your main reason for using Modal was to build a specific AI application, like a customer support chatbot or an internal helpdesk agent, then a platform like eesel AI is a much more direct route. Instead of giving you the low-level building blocks, it gives you a finished, production-ready solution.

You can connect eesel AI directly to your helpdesk (like Zendesk or Intercom), knowledge bases (Confluence or Google Docs), and chat tools (Slack or Microsoft Teams). It reads your past support tickets to learn how to automate frontline support, draft replies for your human agents, and triage issues, all while matching your brand’s voice. You can go from signing up to having a working AI agent in a matter of minutes, not months.

Why it’s a great alternative to building it yourself on Modal:

You don’t need to talk to a salesperson to get started; you can set up, configure, and launch your AI agent all on your own. Before you even let it talk to a real customer, you can run a simulation on thousands of your historical tickets to see exactly how it would have performed. This lets you calculate your potential ROI ahead of time. Plus, you get a workflow engine that gives you full control over which tickets the AI handles and what actions it can take, whether that’s looking up order information or escalating a tricky issue to a human.

A screenshot of the eesel AI simulation feature, a powerful tool among Modal AI alternatives for testing performance.

• Pros: Go live in minutes, no infrastructure to manage, a powerful simulation mode, and clear pricing with no hidden fees per resolution.

• Cons: It’s focused specifically on customer service and internal support, so it’s not a platform for running general-purpose code.

• Pricing: eesel AI’s pricing is built around predictability. The Team plan is $299/month ($239/month if billed annually) and covers up to 1,000 AI interactions. The Business plan is $799/month ($639/month if billed annually) for 3,000 interactions and adds extra features like training on past tickets and AI-powered triage. This model means your costs stay flat, even if you have a busy month.

2. Northflank

If you’ve outgrown single-function scripts and need to build a complete, multi-part AI product, Northflank is a solid contender. Unlike Modal’s function-first approach, Northflank lets you deploy any containerized service, whether it’s a backend API, a frontend UI, or a database, and link them all together.

Its biggest selling points are the built-in, Git-based CI/CD pipeline, preview environments for every pull request, and smart GPU orchestration. It also supports bringing your own cloud (BYOC), giving you the kind of infrastructure control that Modal doesn’t offer.

• Pros: No vendor lock-in, clear pricing, built-in CI/CD, and designed for full-stack applications.

• Cons: It can feel a bit more complex than Modal if all you want to do is run a single script.

• Pricing: Northflank operates on a pay-as-you-go model based on the resources you use. You’re billed by the hour: CPU is $0.01667/hour, memory is $0.00833/GB/hour, and GPUs have their own rates (an NVIDIA H100, for example, is $2.74/hour). This gives you a lot of control but means you need to keep an eye on your usage to manage costs.

3. RunPod

If the main reason you’re looking for an alternative is to lower your GPU bill, then RunPod should be at the top of your list. It’s a cloud platform that provides on-demand GPU computing at some of the most competitive prices you’ll find anywhere. It pulls this off with a marketplace that includes both secure data centers and a "Community Cloud" of peer-to-peer machines.

RunPod gives you more direct control over your environment than Modal does, supporting custom Docker images and persistent storage. It’s a great fit for ML folks who need to run Jupyter notebooks, fine-tune models, or deploy inference APIs without breaking the bank.

• Pros: Extremely low GPU costs, a huge selection of available GPUs, and full control over your runtime environment.

• Cons: It requires a bit more manual setup than Modal and doesn’t have integrated CI/CD or support for building full-stack apps.

• Pricing: RunPod’s pricing is hard to beat, with per-second billing. For instance, an NVIDIA H100 GPU can be found for as low as $1.99/hour in their Secure Cloud. They also offer serverless GPU instances priced by the second, making it one of the cheapest options out there for spiky inference workloads.

4. Replicate

Replicate is built for one thing: deploying and sharing open-source models through a simple API. If you just want to get a cool new generative model online, share it with the world, or plug it into another app, Replicate is probably the easiest way to do it. You can deploy popular models from its extensive library with just a couple of clicks.

It shares Modal’s love for simplicity but is hyper-focused on the inference use case. It’s not the right tool for training models, running batch jobs, or building private, production-grade applications.

• Pros: Zero-setup model hosting, easy to share or even monetize your models, and a fantastic community library.

• Cons: Not really built for production applications, and it doesn’t offer private infrastructure or CI/CD.

• Pricing: Replicate’s pricing model is based on pay-per-second usage of the hardware. An Nvidia T4 GPU costs $0.000225/sec (which works out to $0.81/hr) and an Nvidia A100 is $0.001400/sec ($5.04/hr). For some proprietary models, they bill per output (like per image generated), which can make it easier to estimate costs.

5. Baseten

Baseten is aimed at machine learning teams who need to get their models served as APIs quickly, especially for internal tools or early-stage products. Its killer feature is an integrated "app builder" that lets you create a simple user interface for your models without touching any frontend code. This makes it super fast to share your work with colleagues and stakeholders.

It’s a fantastic choice for turning a model into a production-ready API endpoint with all the trimmings like autoscaling and logging, but it’s not as flexible for building out more complex backends or full applications.

• Pros: An incredibly fast way to get from a model to a live API, and the built-in UI builder is perfect for demos.

• Cons: Tends to be geared more toward internal tools and has limited options for orchestrating multiple services.

• Pricing: Baseten’s pricing is pay-as-you-go, billed by the minute for dedicated machines. GPU instances start at $0.01052/minute for a T4 and go up to $0.10833/minute for an H100. They also offer pre-packaged APIs for popular LLMs, which are billed per million tokens.

6. AWS SageMaker

For big companies that are already all-in on the AWS ecosystem, Amazon SageMaker is the heavyweight champion. It’s a massive MLOps platform that tries to do everything in the machine learning lifecycle, from labeling data and training models to deploying and monitoring them in production.

SageMaker ties in deeply with other AWS services like IAM and VPC, which gives it the enterprise-level security and compliance features that Modal doesn’t have. But all that power comes with a steep learning curve and can be serious overkill for smaller teams.

• Pros: A powerful and mature ecosystem with top-notch security and compliance features.

• Cons: Can be very complex to set up and manage, and the pricing is famously difficult to predict.

• Pricing: SageMaker’s pricing is a beast of its own, with a pay-as-you-go model that spans dozens of different components. You get billed separately for instance time, storage, data processing, and more. While it’s flexible, it makes forecasting costs a real headache, and it’s easy to get hit with a surprise bill if you’re not careful.

7. Anyscale

Anyscale comes from the creators of Ray, the go-to open-source framework for distributed computing. This makes it the perfect platform for teams who need to run huge, distributed training and inference jobs using Ray. If you’re hitting performance ceilings with single-GPU jobs on Modal, Anyscale gives you a straightforward path to scaling out across many machines.

It’s a very specialized tool that’s all about the Ray ecosystem, so it’s really best for teams that are already committed to using Ray for parallel computing.

• Pros: The best platform out there for scalable, Ray-based computing; it’s also fault-tolerant.

• Cons: Highly specialized and not meant to be a general-purpose application platform.

• Pricing: Anyscale offers usage-based billing with different rates depending on whether you use their hosted service or deploy in your own cloud (BYOC). Hosted compute starts at $0.0112/hour for a CPU-only instance and goes up to $1.8603/hour for an NVIDIA H100. The BYOC model has lower platform fees, but you’re still on the hook for your cloud provider bills.

This video explores some of the best and most powerful AI tools available, offering more Modal AI alternatives for various needs.

How to choose from the best Modal AI alternatives for your project

With all these options, picking the right one really boils down to what you’re trying to accomplish.

When to choose application-layer Modal AI alternatives like eesel AI

You want to solve a business problem, not manage servers. If your end goal is to launch a polished AI support agent that works with your existing tools, picking a platform like eesel AI will save you months of development effort. You can spend your time focusing on the customer experience instead of the tech stack underneath.

When to choose infrastructure-layer Modal AI alternatives

You need to run custom code or have unique requirements. If you’re developing a brand-new ML model, need total control over your container environment, or want to build a full-stack application from scratch, then a platform like Northflank or RunPod will give you the power and flexibility you need.

From building blocks to business solutions

Modal did a great job showing the world that developers love tools that hide away the messy parts of infrastructure. But the next step in this evolution is hiding away the application logic itself. While platforms like Modal give you the LEGO bricks, solutions like eesel AI give you the fully-built spaceship.

For most teams, the goal isn’t just to run code in the cloud, it’s to get a specific business result. If that result is smarter, faster, and more helpful customer support, then building it all from scratch on a developer platform is taking the scenic route. The smarter alternative is to use a solution that’s already solved that problem for you.

Ready to see how fast you can launch a world-class AI support agent? Try eesel AI for free and get started in minutes.