A complete guide to CoreWeave pricing in 2025

So, you’re looking to build or run some serious AI workloads and, quite rightly, are trying to figure out the costs. It’s a smart move. In the world of AI, the infrastructure you choose can be the difference between staying on budget and getting a nasty surprise on your monthly bill. That’s probably why you’re here, trying to get a straight answer on CoreWeave pricing.

CoreWeave has popped up on everyone’s radar as a go-to cloud provider, especially for anyone needing access to those in-demand NVIDIA GPUs. But here’s the deal: just looking at their hourly rates is only seeing a tiny piece of the puzzle. The true cost of getting an AI project off the ground goes way beyond just renting hardware.

This guide will break down CoreWeave’s pricing, for sure. But more importantly, we’re going to zoom out and look at the whole picture. We’ll get into the hidden costs of AI development and help you think about your return on investment, so you can make a decision that actually works for your goals.

What is CoreWeave?

Before we jump into the numbers, let’s do a quick recap of what CoreWeave actually is. Think of it as a specialized cloud platform that was built from the ground up for huge, power-hungry computing tasks. While the big, general-purpose clouds like AWS or Google Cloud have to be a jack-of-all-trades, CoreWeave is laser-focused on one thing: delivering the raw horsepower needed for AI model training, inference, visual effects rendering, and other demanding jobs.

Their big claim to fame is providing access to a massive fleet of the latest NVIDIA GPUs, all hooked together with super-fast networking in a Kubernetes-native environment. This specialized setup is why they’ve become a key partner for some of the biggest names in AI, like OpenAI and Mistral AI. In a way, they’re providing the digital picks and shovels for the companies building the next generation of AI.

An in-depth look at CoreWeave pricing

On the surface, CoreWeave’s pricing model is pretty straightforward. It breaks down into three main buckets: Compute, Storage, and Networking. Let’s take a look at each one.

GPU and CPU compute

This is where the bulk of your CoreWeave bill will come from. Compute costs are billed on-demand, by the hour, for the virtual server instances you use. They offer a seriously impressive menu of NVIDIA GPUs, from the more accessible RTX series all the way up to the top-of-the-line H100s, B200s, and GB200s that are powering the world’s most advanced AI models.

To give you a real-world idea of the costs, here are a few popular high-performance setups. These are often rented in clusters of eight GPUs at a time.

Just a heads-up: These prices were accurate when I wrote this. You should always double-check the official CoreWeave pricing page for the latest numbers.

Along with their GPUs, they also have standard CPU instances running on hardware from AMD and Intel for any work that doesn’t need a GPU.

Storage

CoreWeave prices its storage per gigabyte, per month. They have a few different options to fit what you’re doing, including Object Storage, Block Storage, and high-performance File Storage.

One of the best things they highlight on their site is a commitment to keeping prices simple. They don’t have those confusing extra charges for data egress (moving data out) or IOPS (input/output operations per second) that can often lead to surprise bills on other cloud platforms. For instance, their standard Object Storage is a flat "$0.03 per GB / month". No hidden fees, no complicated math.

Networking and other services

Networking is another place where CoreWeave tries to make things easier. Core services like setting up a Virtual Private Cloud (VPC), using a NAT Gateway, and transferring data inside their network are all free. This is a huge bonus for large-scale training jobs where a bunch of machines need to talk to each other constantly without racking up costs.

The main extra you might run into is the cost for Public IP Addresses, which are billed at "$4.00 per IP / month".

The bigger picture: AI costs beyond CoreWeave pricing

Okay, so you’ve done the math on your GPU hours and storage needs. But that’s just the beginning of the story. To really understand the financial commitment of an AI project, you have to think about the Total Cost of Ownership (TCO). This includes all the "hidden" costs that pop up long after you’ve rented the hardware. I’ve seen teams get really excited about low hourly GPU rates, only to have their budget completely blow up a few months later.

Here’s what often gets missed:

The cost of your team. Renting the GPUs is the easy part. You still need a team of highly specialized (and very well-paid) ML engineers and data scientists to actually build, train, deploy, and tweak your models. This isn’t a weekend project; it’s a process that can easily stretch over months, if not years, and cost hundreds of thousands of dollars in salaries alone.

Keeping the lights on. AI models aren’t something you can just set up and forget about. They can "drift" over time, they need constant monitoring, and they have to be retrained on new data to stay accurate and useful. This creates an ongoing operational cost known as MLOps (Machine Learning Operations), which needs its own dedicated people and tools.

The integration headache. This is often the biggest hurdle of all. A brilliant AI model sitting on a server is completely useless until it’s connected to the tools your business actually uses. Whether it’s plugging into your helpdesk, your CRM, or your team’s internal chat, this integration step often means writing custom code and can cause major disruptions to how your team works.

A faster path to AI ROI: How application-first AI saves time and money

Instead of starting with raw infrastructure and a blank slate, there’s another way to think about this: using pre-built, specialized AI platforms that are designed to solve one specific business problem really well. AI for customer support is a perfect example of where this approach makes a ton of sense.

While CoreWeave gives you a world-class engine, a platform like eesel.ai gives you the entire car, ready to drive off the lot. It’s a completely different way of approaching AI.

Here’s how this "application-first" approach helps you sidestep the pitfalls of building from scratch:

• Go live in minutes, not months. Forget about figuring out servers and hiring engineers. With eesel AI, you can connect your existing helpdesk, whether it’s Zendesk, Freshdesk, or Intercom, with a single click. You can have a working AI Copilot drafting replies for your agents in less than five minutes. No developers needed.

The eesel AI Copilot drafting a reply within a helpdesk, showcasing a ready-to-use application that contrasts with raw infrastructure like CoreWeave.

• Pricing that’s actually predictable. The wild, hard-to-forecast costs of GPU time and engineering salaries are a huge source of stress. In contrast, eesel AI has simple, transparent plans based on your interaction volume. And this part is key: there are no per-resolution fees, so your costs won’t suddenly skyrocket just because you had a busy support month. It becomes a predictable expense, not a risky, open-ended investment.

• Total control without the technical mess. Getting great results from AI shouldn’t mean you need to learn to code. With eesel AI, you get a full workflow engine where you can shape your bot’s personality, limit its knowledge to specific documents, and even create custom actions (like having it look up an order in Shopify), all from a straightforward, self-serve dashboard.

The self-serve dashboard in eesel AI allows for easy customization of the AI's behavior, which is a key benefit over building from scratch and managing CoreWeave pricing.

How to choose the right AI investment strategy

CoreWeave offers incredibly powerful and specialized infrastructure for AI. For teams building custom, foundational models from the ground up, its pricing is competitive and it’s an excellent choice.

But it’s so important to remember that the infrastructure is just one line item in the total cost. The budget for development, integration, and ongoing maintenance can quickly eclipse what you spend on the hardware itself.

CoreWeave's CEO discusses the massive capital investment required for building AI infrastructure, reinforcing the blog's point about total cost of ownership.

For most businesses that are looking to solve specific, practical problems, an application-layer solution offers a faster, more predictable, and ultimately more cost-effective way to get a return on your AI investment. Instead of building from scratch, you can deploy a tool that’s already an expert in its field.

Ready to give your team AI that just works? See how eesel AI can unify your knowledge and start automating support tickets in minutes.