East Palo Alto, California Aug 7, 2025 (Issuewire.com) - At Dat1.co, we're building a serverless GPU platform that makes it easy to run any custom AI model with fast cold starts, usage-based pricing, and zero infrastructure headaches. Whether you're deploying a fine-tuned LLM or a diffusion model, Dat1.co gives you the flexibility of open models with the simplicity of an API.

Over the last year, open-source AI models have advanced rapidly, reaching performance levels that are often hard to distinguish from their proprietary counterparts. In areas like image generation, summarization, and code completion, the line between open and closed models continues to blur.

Yet despite the progress, most companies still hesitate to use open models in production. The issue is no longer model quality. It's infrastructure.

Proprietary models are typically deployed with managed APIs and pricing baked in. Open models, on the other hand, leave teams to figure out hosting, scaling, latency, and cost management themselves. This can lead to unexpected engineering overhead, cold start problems, and unpredictable billing especially when using general-purpose GPU platforms.

The tooling stack around open models has improved so much, said Leandro von Werra, Head of Research at Hugging Face, in a recent LinkedIn post. Fine-tuning a model or running inference is now just a few lines of code, as easy as it was to build a decision tree or linear regression a few years ago. And open models have become very strong, too.

The last piece of the puzzle is deployment. Running a fine-tuned model reliably and cost-effectively still requires careful backend setup and tuning. This is where many teams get stuck.

According to Arseny Yankovski, CTO of Dat1, that challenge can't be solved with a one-size-fits-all product. "We learned early on that making open models work in production means working directly with teams to help optimize not just their models, but how they run. Every use case has its own bottlenecks."

Dat1 is one of several emerging platforms focused on making open model deployment more accessible. By aligning compute usage with actual demand and reducing operational friction, the hope is to finally make open models as easy to use as they are powerful.

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