Building products with AI is one of the most exciting things you can do right now. But let's be honest, figuring out the infrastructure costs can be a real headache. It’s way too easy to get lost in a sea of acronyms, instance types, and pay-per-token models. One platform that keeps popping up in these chats is Baseten, a popular pick for deploying and scaling machine learning models with the promise of speed and efficiency.
My goal here is simple: to give you a clear, no-fluff guide to Baseten pricing. We’ll pull apart its different models, explain what actually drives your final bill, and point out a few things to watch for. It's also worth understanding the difference between building on raw infrastructure like Baseten versus using a fully integrated application that just works straight away.
What is Baseten?
Baseten is what the tech world calls an "inference infrastructure" platform. In normal-speak, it provides the powerful computers (GPUs) and underlying software needed to run AI models so other applications can use them. It’s made for machine learning engineers and developers who need a solid place to deploy their own custom models or popular open-source ones.
Think of it this way: Baseten gives you a world-class engine, but you still have to build the rest of the car. The application, the user interface, the logic that connects it all to your business tools, that part is up to you. It has some powerful features to make a developer's life easier, like autoscaling for traffic spikes and fast cold starts to cut down on lag. But at its heart, it's a tool for builders who are comfortable getting their hands dirty with the technical side of AI.
Understanding the different Baseten pricing models
Baseten’s pricing isn’t a single number. It’s a mix of different models that change depending on how you use the platform. Let's break down the main ways you'll get charged.
Model API pricing: Pay-per-token for popular models
This is the simplest way to get going with Baseten. You can tap into a library of popular, pre-optimized models like DeepSeek or Llama and pay based on how much you use them. The cost is calculated per one million tokens (a token is just a small piece of a word, about four characters). It’s good to know you're charged different rates for "input" tokens (what you send the model) and "output" tokens (what it sends back).
Dedicated deployment pricing: Pay-per-minute for compute power
If you have your own model or need guaranteed performance for a specific open-source one, you’ll probably end up using dedicated deployments. Here, you’re paying for the time a specific piece of hardware, like an NVIDIA GPU or a standard CPU, is running just for you. The billing is super granular, calculated right down to the minute.
This gives you a ton of control, but it also means you’re responsible for managing how much it's being used. Baseten does have a scale-to-zero feature, so you won't pay for hardware that's completely idle. Still, your costs are tied directly to your application's traffic, so a busy day means a bigger bill.
Training infrastructure pricing: Pay-per-minute for fine-tuning
If you need to tweak a model using your own data, Baseten offers the infrastructure for that, too. Just like with dedicated deployments, the pricing is based on the hardware you use and is billed by the minute.
Plan tiers and enterprise options
On top of the usage-based pricing, Baseten has a few different tiers. The Basic plan is straight-up pay-as-you-go. The Pro plan is for teams with more volume who might be able to negotiate better rates. The Enterprise plan is for big companies with complex needs, like hosting Baseten on their own cloud. Just to give you an idea of scale, the Baseten offering on the AWS Marketplace kicks off with a $5,000 per month contract, which tells you that serious usage often comes with a serious price tag.
Key factors that affect your Baseten pricing
The prices you see on the website are just the beginning. Your real monthly bill will swing based on a few key variables you need to get a handle on.
How hardware choice affects your bill
The biggest chunk of your cost will come from the type of GPU you select. Running a model on a shiny new NVIDIA H100 GPU is way more expensive than using an older, less powerful T4. The performance difference is huge, but so is the price. You're paying for access to top-of-the-line hardware, and that doesn't come cheap.
Here’s a quick comparison to show the difference in cost for just one hour of use:
| GPU Instance | VRAM | Cost per Hour (approx.) |
|---|---|---|
| T4 | 16GB | ~$0.63 |
| A10G | 24GB | ~$1.21 |
| A100 (80GB) | 80GB | ~$4.00 |
| H100 (80GB) | 80GB | ~$6.50 |
How traffic and autoscaling affect your bill
Since a big part of your cost is per-minute, your bill is directly tied to how many people are using your product. If you have an app that gets sudden bursts of traffic, Baseten’s autoscaling will fire up more GPU instances to handle it. That's great for keeping things running smoothly, but it also means your costs will shoot up just as quickly. This can make budgeting a real headache for businesses with unpredictable traffic.
How cold starts and model complexity affect your bill
A "cold start" is that little delay when a model has been sitting idle and needs to boot up to handle a new request. Baseten has worked hard to make these as fast as possible, but there's still a bit of a lag you can't get around, especially with big, complicated models. This is another one of those technical details that someone on your team has to manage and optimize to keep users happy.
The hidden costs: When raw infrastructure isn't enough
The bill you get from Baseten only covers the computing power. But that’s just one piece of the puzzle. The real cost, and often the biggest bottleneck, is everything else you have to build around it.
You can have the world's fastest model, but if it doesn't actually plug into your business processes, it isn't doing you much good. This is where the hidden costs of developer time and resources start to stack up.
For example, to make that Baseten-hosted model useful for your support team, your engineers will need to:
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Build a custom integration to hook it up to your helpdesk, like Zendesk or Freshdesk.
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Write code to manage authentication and API calls.
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Figure out the logic for how the AI should triage tickets, draft replies, or pass things to a human.
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Connect it to your internal knowledge bases in Confluence or conversations in Slack so it has the right context.
Baseten provides the engine, but you still need a team of developers to build the car. For teams that just want to drive, integrated platforms like eesel AI handle both the engine and the car. It connects to your helpdesk, Slack, and knowledge bases in a few minutes, not months, so you don't have to worry about the infrastructure at all.
An infographic explaining how eesel AI integrates with various knowledge sources to provide comprehensive support automation, which is a key factor when considering Baseten pricing versus an all-in-one solution.Baseten pricing tables
To give you the full picture, here are the detailed pricing tables based on what's publicly available on Baseten's website.
Model APIs (Price per 1 Million Tokens)
| Model | Input Cost | Output Cost |
|---|---|---|
| GPT OSS 120B | $0.10 | $0.50 |
| Qwen3 Coder 480B | $0.38 | $1.53 |
| Qwen3 235B 2507 | $0.22 | $0.80 |
| Kimi K2 0905 | $0.60 | $2.50 |
| DeepSeek V3.1 | $0.50 | $1.50 |
| DeepSeek R1 0528 | $2.55 | $5.95 |
| DeepSeek V3 0324 | $0.77 | $0.77 |
Dedicated Deployments (Price per Minute)
| GPU Instances | Specs | Price per Minute |
|---|---|---|
| T4 | 16 GiB VRAM, 4 vCPUs, 16 GiB RAM | $0.01052 |
| L4 | 24 GiB VRAM, 4 vCPUs, 16 GiB RAM | $0.01414 |
| A10G | 24 GiB VRAM, 4 vCPUs, 16 GiB RAM | $0.02012 |
| A100 | 80 GiB VRAM, 12 vCPUs, 144 GiB RAM | $0.06667 |
| H100 MIG | 40 GiB VRAM, 13 vCPUs, 117 GiB RAM | $0.0625 |
| H100 | 80 GiB VRAM, 26 vCPUs, 234 GiB RAM | $0.10833 |
| B200 | 180 GiB VRAM, 28 vCPUs, 384 GiB RAM | $0.16633 |
| CPU Instances | Specs | Price per Minute |
|---|---|---|
| 1x2 | 1 vCPU, 2 GiB RAM | $0.00058 |
| 2x8 | 2 vCPUs, 8 GiB RAM | $0.00173 |
| 4x16 | 4 vCPUs, 16 GiB RAM | $0.00346 |
| 8x32 | 8 vCPUs, 32 GiB RAM | $0.00691 |
| 16x64 | 16 vCPUs, 64 GiB RAM | $0.01382 |
Picking the right tool for the job
Baseten is a seriously powerful and flexible platform for technical teams. If you have machine learning engineers who need to deploy custom models and are ready to manage the infrastructure that comes with it, it's a great choice. The usage-based Baseten pricing offers flexibility, but it also means costs can be a bit of a rollercoaster, swinging based on your hardware, traffic, and model complexity.
For most people in support, IT, or operations, though, the goal isn't to manage GPUs. It's to solve real problems, like cutting down ticket resolution times or giving employees instant answers. The infrastructure is just a way to get there.
This video explores how to effectively price and reprice AI products, covering usage metering, cost analysis, and margin considerations, all crucial factors when evaluating Baseten pricing.
If your goal is to automate customer support or give your team an AI boost today, you don't need to start from scratch with raw infrastructure. A platform like eesel AI gives you a ready-to-use solution with predictable, transparent pricing. You can set up AI agents and copilots that learn from your existing data and plug right into your helpdesk in minutes. This lets you focus on the results, not the hardware.
Go live with AI in minutes, not months
Your support and IT teams need solutions, not long-term infrastructure projects. With eesel AI, you can deploy powerful AI agents and copilots across your existing tools without writing a single line of code.
You get:
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Predictable pricing: No surprise bills from GPU usage or traffic spikes.
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Instant integration: Connect to Zendesk, Slack, Confluence, and over 100 other tools in one click.
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Risk-free simulation: Test your AI on thousands of past tickets to see the impact before you go live.
Start your free trial of eesel AI today and see how simple AI automation can really be.
Frequently asked questions
Baseten pricing is primarily determined by the chosen deployment model (Model APIs vs. dedicated deployments), the specific hardware (GPU/CPU) used, and your application's traffic patterns. Your final bill will reflect both the type of compute power consumed and the duration of its use.
Hardware choice significantly impacts Baseten pricing. More powerful GPUs, like the H100, are considerably more expensive per minute than less powerful options like the T4. Selecting the appropriate GPU for your model's needs is crucial for cost optimization.
Yes, Baseten pricing can fluctuate with unpredictable traffic patterns, especially for dedicated deployments. The platform's autoscaling feature will provision more GPU instances to handle spikes, directly increasing your costs during peak usage. This can make budgeting challenging for applications with variable demand.
Beyond the direct compute costs, hidden expenses in Baseten pricing often include the significant developer time required for integration. You'll need to build custom application logic, user interfaces, and connect the deployed models to your existing business tools, which adds considerable overhead.
Yes, Baseten offers different plan tiers: Basic (pay-as-you-go), Pro (for teams with higher volume, potentially negotiated rates), and Enterprise (for large organizations requiring custom setups, often starting around $5,000/month). These tiers cater to varying levels of usage and support needs.
Baseten pricing for Model APIs is calculated per million input and output tokens, making it a pay-per-consumption model for pre-optimized models. In contrast, dedicated deployments are billed per minute for the specific hardware (GPU/CPU) running your custom or open-source model.
