So, you’re checking out Modal AI. It’s a seriously powerful serverless platform for building AI applications, and you’re right to be curious. Digging into Modal AI pricing is probably one of the smartest first steps you can take before your team sinks time and resources into it.
You need to know if it fits the budget, and that’s exactly what we’ll get into. But there’s a bigger picture here. While figuring out the cost of running the tech is a good start, the total cost of getting an AI solution off the ground involves a lot more than just paying for servers.
In this guide, we’ll break down Modal’s pricing, piece by piece. We’ll also look at a more direct, application-focused approach that could save you a ton of time and money, especially for business use cases like customer support.
What is Modal AI?
Modal is a serverless compute platform built for developers and data teams who need to run some pretty heavy-duty stuff, think machine learning, model fine-tuning, and huge data processing jobs.
Its main appeal is that it handles all the messy infrastructure parts of AI development for you. Instead of wrestling with servers and configurations, developers can spin up powerful, GPU-ready containers in less than a second. It’s incredibly fast, thanks to a custom backend they built from scratch in Rust.
Basically, Modal is an infrastructure-as-a-service (IaaS) tool. It gives developers the raw power and building blocks they need, but it’s completely up to them to build, launch, and maintain their own applications on top of it.
A deep dive into Modal AI pricing
At first glance, Modal’s pricing can feel a little complicated. It’s not a simple flat fee; it’s based on the specific, second-by-second resources your code is actually using. This can be super efficient, but it also makes it tough to guess what your monthly bill will look like.
Breaking down the usage-based model
With Modal, you’re billed for the exact compute resources you use, right down to the second. If your code isn’t running, you’re not paying a dime. This is great for workloads that have peaks and valleys, like a chatbot that gets slammed during business hours but is quiet overnight.
Here’s a look at their pay-as-you-go rates for the main resources:
| Resource | Unit | Price (per second) |
|---|---|---|
| GPU (Nvidia H100) | GPU | $0.001097 |
| GPU (Nvidia A100, 80GB) | GPU | $0.000694 |
| GPU (Nvidia T4) | GPU | $0.000164 |
| CPU | Physical Core | $0.0000131 |
| Memory | GiB | $0.00000222 |
This model is a bit of a double-edged sword. As <quote text="they saved money because they ended up paying for "3x less total gpu hours" than they would have with a traditional provider." sourceIcon="https://www.iconpacks.net/icons/2/free-reddit-logo-icon-2436-thumb.png" sourceName="Reddit" sourceLink="https://www.reddit.com/r/MachineLearning/comments/1hzq0ac/d_cheaper_alternative_to_modalcom/"> But there’s a catch: you have to be careful. A small bug or an inefficient script that runs for hours can rack up a surprisingly high bill.
Understanding the subscription plans
On top of the usage costs, Modal has subscription plans that give teams higher limits and extra features.
It’s important to know that for the Team and Enterprise plans, the monthly cost is a platform fee. Your compute usage is a separate bill on top of this. This is a fairly standard practice for enterprise software, but it’s definitely something to factor into your budget.
| Plan | Monthly Fee | Included Compute Credit | Key Features |
|---|---|---|---|
| Starter | $0 | $30 / month | Up to 3 seats, 100 containers, 10 GPU concurrency |
| Team | $250 | $100 / month | Unlimited seats, 1000 containers, 50 GPU concurrency, Custom domains |
| Enterprise | Custom | Custom | Volume-based pricing, Custom GPU concurrency, Private Slack support, SSO |
The hidden costs beyond the pricing page
The numbers on the pricing page are just the direct costs. The total cost of owning a solution built on a platform like Modal is much, much higher. Think of it this way: Modal gives you a high-performance engine, but you still have to design, build, and maintain the rest of the car yourself.
Here are the hidden costs that often get missed:
-
Developer hours: Let’s be honest, building a production-ready AI app from scratch takes a lot of time from specialized (and expensive) engineers. They’re the ones writing the code, deploying it, debugging it, and making sure it doesn’t fall over.
-
Maintenance overhead: Your work isn’t done once the app is live. You have to constantly manage dependencies, update models, and fix bugs that pop up. This is an ongoing operational cost that pulls your team away from creating new things.
-
Delayed time-to-value: Building from the ground up takes time, often months. While your team is heads-down in code, the business problem you’re trying to solve, like a mountain of customer support tickets, is still there. That opportunity cost can be huge.
Who is Modal for?
Modal is an amazing tool for its intended audience: a developer, ML engineer, or data scientist who needs fine-grained control over their computing environment. They’re the ones building custom fine-tuning pipelines, running complex simulations, or crunching massive datasets.
Companies like Ramp and Substack are perfect examples. Ramp used Modal to fine-tune its own LLMs, which gave them the freedom to run a bunch of experiments at the same time. For a technical team with a very specific, custom ML workflow, that level of control is priceless.
But this flexibility comes at the cost of complexity. Modal isn’t a plug-and-play solution for a business department that just wants to use AI to solve a problem. If you’re a Head of Support, your goal isn’t to optimize GPU usage; it’s to cut down your team’s ticket resolution time. Using a developer-first platform like Modal for that is like buying a box of engine parts when all you really need is a car to get from point A to B.
Modal Founder Erik Bernhardsson explains the company's consumer app approach to solving AI infrastructure challenges.
An application-first alternative
For most business teams, there’s a much faster and more direct path to getting value from AI. Instead of building everything from scratch on raw infrastructure, you can use an application-layer platform that gives you a ready-to-use solution right out of the box.
Focus on outcomes, not infrastructure
This is where a tool like eesel AI comes into play. It’s an application-first AI platform designed specifically for customer service, IT support, and internal knowledge. Instead of giving you a box of parts, eesel AI gives you the fully assembled car.
For example, rather than spending months building an AI support bot on Modal, you can use eesel AI to deploy a powerful AI Agent that plugs directly into the helpdesk you already use, like Zendesk or Freshdesk. eesel AI handles all the complicated backend stuff, managing models, orchestrating compute, connecting to data, so your team can focus on what they do best: helping customers.
Go live in minutes, not months
The biggest difference is how fast you see results. A custom AI project built on infrastructure like Modal can take months to get into production. With eesel AI, you can be up and running in minutes. Here’s how:
-
It’s actually self-serve: You don’t need to sit through a mandatory demo or talk to a salesperson just to get started. You can sign up, connect your helpdesk, and have an AI Copilot drafting replies for your team in less time than it takes to drink a coffee.
-
One-click integrations: eesel AI connects instantly to the tools you’re already using. Whether your knowledge is scattered across Confluence, Google Docs, or old support tickets, you can bring it all together without writing a single line of code.
eesel AI's one-click integrations for connecting to various knowledge sources instantly.- Powerful simulations: One of the scariest parts of launching an AI agent is trusting it to do the right thing. eesel AI has a simulation mode that lets you test your setup on thousands of your past tickets. You can see exactly how it would have responded, get solid forecasts on resolution rates, and tweak its behavior before it ever talks to a real customer.
The eesel AI simulation mode forecasts resolution rates based on past tickets, offering a clear advantage over complex Modal AI pricing models for business use cases.Transparent and predictable pricing
Bringing it all back to the main topic, the application-first approach also makes your costs much clearer. The variable, per-second billing of infrastructure platforms is powerful, but it can make budgeting a total guessing game.
eesel AI offers straightforward, predictable pricing based on a set number of AI interactions per month. There are no surprise fees for heavy compute days or for each ticket it resolves. This means your costs won’t spin out of control as your support volume grows. You get all the power of an enterprise-grade AI stack without the financial rollercoaster of building and managing it yourself.
Modal AI pricing: Choose the right tool for the job
Modal is an excellent platform. For technical teams that need total control over a serverless environment for custom AI work, it’s one of the best tools on the market. Its pricing and complexity make perfect sense for that powerful, developer-first focus.
But for business teams, in support, IT, or operations, the goal is different. You need to solve specific problems quickly and without breaking the bank. For that, a fully-managed, application-first platform like eesel AI is the faster, simpler, and more direct route. It delivers all the power of AI without the headaches of building and maintaining the infrastructure yourself.
Ready to see the application-first approach in action?
You can start automating your support workflows in minutes. Connect your helpdesk and try eesel AI for free.
Frequently asked questions
Modal AI pricing is primarily usage-based, meaning you pay per second for the exact GPU, CPU, and memory resources your code consumes. They also offer subscription plans (Starter, Team, Enterprise) that provide higher concurrency limits and additional features, with compute usage billed separately on top of the monthly platform fee.
Beyond the direct Modal AI pricing, you should consider significant hidden costs like developer hours for building and maintaining applications, ongoing maintenance overhead for dependencies and updates, and the delayed time-to-value due to lengthy development cycles. These can often outweigh the compute costs.
While Modal offers a Starter plan with a $30 monthly credit, its core Modal AI pricing model and platform are best suited for technical teams (developers, ML engineers) who need fine-grained control and are prepared to build and maintain their own AI applications from scratch. For simple business solutions, an application-first platform might be more cost-effective.
Estimating your monthly bill with Modal AI pricing can be challenging due to its per-second usage model. It requires careful monitoring of your actual compute consumption, especially for GPU and CPU hours. Inefficient code or unexpected spikes in usage can significantly impact your final cost.
The different tiers of Modal AI pricing (Starter, Team, Enterprise) primarily offer increased limits on containers and GPU concurrency, custom domains, and dedicated support. While the Starter plan includes some compute credit, the Team and Enterprise plans have a separate monthly platform fee, with all compute usage billed on top of that.
Yes, the Starter plan for Modal AI pricing is technically free with a $0 monthly fee, and it includes a $30 monthly compute credit. This allows individuals and small teams to experiment with the platform and run smaller workloads without an upfront financial commitment.
Modal AI pricing is based on raw compute usage and platform fees, requiring you to build and manage your AI solution. In contrast, application-first platforms often have transparent, predictable pricing based on AI interactions or features, covering all underlying infrastructure and development, leading to faster time-to-value and clearer budgeting.
