In most real-world cases, you’ll hit the same roadblocks again and again:
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Setting up and managing backend servers
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Handling APIs, model hosting, load balancing, and databases
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Scaling infrastructure as user load grows
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Paying high costs to run large language models (LLMs)
The result?
A lot of developers and teams spend more time maintaining infrastructure than working on what they actually care about: the AI and the user experience.
But what if you didn’t have to deal with the backend at all?
The Developer’s Dream Stack (Without the Pain)
Here’s what most AI developers want:
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A place to run LLMs affordably
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No DevOps or server maintenance
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An easy way to integrate AI into smart contracts or on-chain apps
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Scalable performance without lock-in
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Tools that just work, with minimal setup
Until recently, that kind of setup meant hacking together multiple cloud services, third-party APIs, and custom backend logic.
Today, that’s changing.
A Simpler Stack: LLMs on Chain, Backend Optional
Some developer platforms are rethinking AI infrastructure completely—by bringing model hosting, execution, and coordination on-chain.
This means:
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You don’t manage a backend server
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You don’t configure APIs or databases
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You deploy AI logic through simple endpoints or smart contract-like workflows
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You pay only for what’s executed, often with a
lower cost than traditional cloud setups
Enter: haveto.com
One such platform enabling this shift is haveto.com. It provides an on-chain way to host and run LLMs without managing infrastructure.
Here’s what it looks like from a developer perspective:
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You write your logic or AI call
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Deploy it to a permissionless network
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It scales automatically
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It’s 100% backend-free and verifiable
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You get ~20% lower cost compared to major cloud LLM hosting options
It’s not just hosting—it’s rethinking how AI dApps are built.
When Backend Disappears, You Build Faster
The core value here isn’t just technical. It’s developer velocity.
When you don’t have to:
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Spin up servers
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Configure API gateways
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Monitor infrastructure 24/7
You ship faster, test sooner, and focus on what matters: features, feedback, and product.
Who This Is For
This kind of stack is especially useful for:
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Solo builders or small teams working on AI MVPs
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Startups trying to minimize burn on infra
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dApp devs exploring AI + Web3 integrations
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Anyone frustrated with backend complexity slowing down AI features
What’s Next?
If you’re experimenting with AI and blockchain, this might be the simplest way to ship something useful without burning out on backend tasks.
Platforms like haveto.com are part of a growing trend toward modular, backend-free AI development—and it’s something more developers will likely adopt as the space matures.
Give it a look. Try an endpoint. See how fast you can ship something real.
If you’ve worked on AI dApps, I’d love to hear your thoughts below.
What would your dream dev stack look like if the backend just… disappeared?