If you’re digging into Axelera AI, you’re probably not just kicking tires. You’re likely serious about building some heavy-duty AI into a project, especially if it involves edge computing. But figuring out the price of advanced tech like this is never as simple as looking at a price tag. It’s about understanding the total cost to get a solution up and running.
The real question isn’t just "How much is the hardware?" but "What am I actually trying to accomplish?" Are you in the business of building a custom AI-powered device from scratch? Or are you trying to use AI to solve a business problem, like streamlining your operations?
This guide will break down Axelera AI’s hardware costs, but more importantly, we’ll explore the other path: using a ready-made AI software solution to automate parts of your business. We’ll compare the "build it yourself" approach with the "buy a solution" model so you can figure out which road makes the most sense for you.
What is Axelera AI?
Put simply, Axelera AI is a company that designs and sells specialized hardware, the engines for artificial intelligence. Their main products are AI Processing Units (AIPUs) and accelerator cards, like their Metis M.2 and PCIe cards.
Their whole game is about AI inference at the "edge." All that means is running the AI smarts directly on a device, think a security camera, a factory robot, or a retail sensor, instead of sending data to a distant cloud server and waiting for an answer. This approach makes AI faster, more private, and way more reliable, especially if you’re working with a shaky internet connection.
It’s important to know who their products are for. They’re selling to developers, system integrators, and companies that build their own hardware. These are teams creating bleeding-edge applications in fields like computer vision, industrial automation, and robotics. They aren’t selling a simple plug-and-play tool for the average business; they’re providing the core components for engineers to create something entirely new.
How Axelera AI products and features affect pricing
Before we can talk dollars and cents, it helps to know what you’re actually buying. Axelera AI’s products are essentially the raw ingredients for a custom AI solution. Let’s take a quick look at the main components.
How core hardware impacts pricing
The heart of their technology is the Metis AIPU, a custom-designed chip. It’s built with an in-memory computing architecture that makes it both powerful and energy-efficient. That’s a big deal for edge devices that might be running on a battery.
You can get this tech in a few different packages:
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M.2 AI Inference Acceleration card: A small card made for devices where every millimeter counts. It has a single quad-core Metis AIPU and is great for embedding AI into smaller systems.
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PCIe AI accelerator card: This is a larger, more powerful card that fits into a standard PCIe slot on a computer’s motherboard. It can hit up to 214 TOPS (Trillions of Operations Per Second), which is enough muscle for more demanding jobs.
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M.2 MAX and multi-AIPU PCIe cards: These are the heavyweights. The M.2 MAX is built to handle complex models like LLMs directly on a device, while the multi-AIPU card stacks four chips for when you need absolute maximum performance.
Here’s a quick rundown of the main hardware options:
| Product | Form Factor | Key Feature | Best for |
|---|---|---|---|
| M.2 AI Accelerator Card | M.2 2280 | Single Metis AIPU | Small-footprint, embedded systems |
| PCIe AI Accelerator Card | PCIe | Up to 214 TOPS | Demanding computer vision apps |
| M.2 MAX Card | M.2 | Up to 16GB Memory | LLM and VLM applications at the edge |
| 4x AIPU PCIe Card | PCIe | Up to 856 TOPS | Highest performance vision tasks |
The role of development tools and complete systems in pricing
Hardware alone doesn’t do much. To bring these cards to life, developers need software. Axelera provides the Voyager SDK, which is a toolkit engineers use to get their AI models running on the Metis hardware.
For teams that want a bit of a head start, Axelera also works with partners like Dell, Lenovo, and Advantech to sell "Metis Systems." These are pre-built workstations with an accelerator card already installed. This just goes to show the level of technical know-how involved. It’s a platform for engineers, not an everyday business tool.
A deep dive into Axelera AI pricing
Alright, let’s get to the main event. Axelera AI sells physical hardware, so you’re looking at a per-unit cost. It’s a capital expense, but as you’ll see, it’s just the starting point of the total investment.
Official hardware pricing
Here are the prices for their cards and development systems, taken from the official Axelera AI store. It’s worth noting that when we checked, many of these products were listed as "Unavailable" or for "Pre-order," so getting your hands on them might take some planning.
| Product | Price (USD) | Availability | | :--- | :--- | :--- | :--- | | M.2 AI Inference Acceleration card | From $259.00 | Unavailable | | PCIe AI accelerator card | From $359.00 | Unavailable | | Metis Compute Board | $539.00 | Pre-order | | Metis Dev System with Arduino | $499.00 | Pre-order | | Metis M.2 System with Aetina | $999.00 | Unavailable | | Metis PCIe System with Lenovo | From $1,399.00 | Unavailable | | Metis PCIe System with Dell | $1,899.00 | Unavailable | | Metis PCIe System with Advantech | $1,999.00 | Unavailable |
Hidden costs beyond the listed price
That price list above? Think of it as the entry fee. When you decide to build a custom AI solution from the chip up, the sticker price of the hardware is just one piece of a much larger financial puzzle.
Here’s what you really need to budget for:
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Engineering Talent: This is the big one. You’ll need a team of skilled developers and AI specialists who know their way around hardware integration, SDKs, and AI model optimization. These aren’t your typical software engineers. Finding, hiring, and keeping this kind of talent is both expensive and incredibly competitive.
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Development Time: Building, testing, and deploying a custom AI application isn’t something you knock out in a weekend. It took Axelera AI 25 months from launch to start shipping its platform. Even if your project is smaller, you should realistically plan for many months, maybe even over a year, of development before you have something ready for the market.
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Ongoing Maintenance: Your work isn’t done once the application goes live. You’re on the hook for maintaining everything, the software, the hardware, pushing updates, fixing bugs, and making sure nothing breaks when operating systems and other software libraries change.
This "build" path is the right one for companies with serious R&D budgets and in-house engineering teams, especially if their main product is aI-powered hardware. But for most other businesses, there’s a much faster and saner way to use AI.
The alternative: Turnkey AI solutions
This brings us to a different way of thinking: shifting from "building" AI to simply "using" it. For a lot of businesses, this change in approach can save a ton of time, money, and frustration.
When a software-first approach makes more sense
Ask yourself this: is your goal to design and sell a new AI-powered camera, or is it to solve an immediate business problem, like cutting down on customer support tickets or giving your team instant answers to their questions?
If you’re in the second camp, you don’t need to start from scratch. AI-powered Software-as-a-Service (SaaS) platforms are built specifically to solve these kinds of problems. They plug into the tools you’re already using and start delivering value in minutes, not years.
A great example of this is eesel AI. It’s an AI platform designed to automate customer support and organize internal knowledge. Instead of buying computer chips and hiring a team of engineers, you just connect it to your existing systems and let it do its thing.
Complexity and cost: A comparison
Let’s put the two approaches next to each other to see how different they are.
This video provides a technology overview of Axelera AI, covering their approach to cost-efficient edge AI solutions.
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Setup: With Axelera AI, you’re looking at installing hardware, making sure your computer is compatible (they test on Ubuntu 22.04), and having a team that can work with their SDK. In contrast, a platform like eesel AI has one-click integrations. You can connect it to your help desk, whether you use Zendesk or Freshdesk, and your knowledge sources like Confluence or Google Docs, in just a few minutes. No developers required.
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Time to Value: Building a solution with hardware can take a long, long time to see any return on your investment. With eesel AI, you can be up and running the same day. Its AI trains on your past support tickets and existing documents, so it learns your business and brand voice right away.
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Pricing: The Axelera AI route involves buying hardware upfront, followed by the large and often unpredictable ongoing costs of an engineering team. eesel AI uses a clear SaaS pricing model based on usage. You won’t get hit with a surprise bill, and you’re not penalized with per-resolution fees just for being successful.
One of the biggest differences is the risk involved. With the "build" approach, you sink a lot of time and money into a project before you even know if it will work the way you hope. eesel AI offers a simulation mode that lets you test the AI on thousands of your past support tickets before it ever talks to a real customer. This gives you a risk-free way to see exactly how it will perform and calculate your potential return on investment, something that’s pretty much impossible when you’re building from the ground up.
Choosing your AI path beyond just Axelera AI pricing
So, what’s the takeaway here? There isn’t a single "best" option. It all comes down to your company’s goals, resources, and how quickly you need to get things done.
The "Build" path, using hardware from companies like Axelera AI, is for you if your core business is creating new, hardware-centric AI products. If you’re designing the next generation of smart drones, medical scanners, or autonomous robots, this is your world. The upfront investment is steep, but it gives you complete control to build something unique.
Then there’s the "Buy" path. Using a ready-to-go software platform like eesel AI makes a lot more sense for most other companies. If you want to use AI to make your business run smoother, improve your customer service, and give your team the information they need, this is the fastest and most cost-effective way to do it. It’s less risky, doesn’t require a specialized engineering team, and delivers results you can see almost immediately.
Get started with AI today, no hardware required
If your main goal is to reduce your support ticket volume, help your agents respond faster, and automate the repetitive questions that eat up your team’s day, you don’t need a hardware lab.
You can see what a turnkey AI solution feels like right now.
Sign up for a free trial of eesel AI and see how you can automate your support in just a few minutes.
Frequently asked questions
The listed Axelera AI pricing for their accelerator cards starts from $259 for an M.2 card and goes up to $1,999 for a pre-built system. These are per-unit costs for the physical hardware.
Beyond the initial Axelera AI pricing, significant costs include hiring specialized engineering talent, extensive development time (potentially many months to over a year), and ongoing maintenance for both software and hardware. These are often the largest parts of the total investment.
Many Axelera AI products are listed as "Unavailable" or "Pre-order" on their store. This means actual project timelines and the immediate ability to acquire components, which directly influences Axelera AI pricing for a solution, might require careful planning and patience.
Axelera AI pricing is most appropriate for companies whose core business involves building new, hardware-centric AI products, requiring deep R&D and in-house engineering. For businesses aiming to solve immediate operational problems, a turnkey AI software solution is generally faster and more cost-effective.
A custom solution involving Axelera AI pricing incurs upfront hardware costs, substantial engineering expenses, and long development cycles. In contrast, a SaaS AI platform typically has clear, usage-based pricing with immediate setup and much lower risk and development overhead.
The Voyager SDK is a toolkit provided by Axelera AI for engineers to get models running on their hardware. While direct costs for the SDK aren’t itemized, the need for specialized engineering skills to use it significantly adds to the overall Axelera AI pricing for development.
Yes, Axelera AI partners with companies like Dell and Lenovo to offer "Metis Systems" which are pre-built workstations with accelerator cards installed. The Axelera AI pricing for these systems ranges from $999 to $1,999, providing a head start for development.
