AI deploy is still a relatively new area within DevOps and SRE practices. At the same time, it is evolving extremely fast.
In today’s reality, if you are not a highly specialized infrastructure expert, it can be difficult to navigate the landscape and find practical guidance for your specific case.

In this guide, we share the most useful tips and common pitfalls that we at Kernul have collected through hands-on work with AI startups. The focus is on practical AI hosting, GPU AI usage and sustainable growth.

1. Start your project with an API-first approach

At the early stage of a project, we strongly recommend not starting with hosting your own model.

Reasons are simple:

  • it takes a lot of time.
  • it is expensive in terms of infrastructure.
  • it is expensive in terms of specialist work.

Self-hosting rarely brings additional revenue at this stage, but it consumes valuable resources and time. And time is one of the most critical assets early on.

For a small audience, up to around 1000 active users, using external APIs can even be more cost-efficient. In many cases, this depends on token optimization, but for most early AI products, API-first AI deploy is the safest path.

2. Avoid the oldest and the newest GPU hardware for AI

GPU vendors frequently update their APIs. Because of this, local AI models may fail to work reliably and silently fall back to CPU execution.

For example, at the moment some open-source models and runtimes such as Ollama cannot properly utilize AMD RX 9700 and RX 9070 GPUs. The reason is simple: RDNA4 architecture is not fully supported by default yet.

As a result, a powerful and expensive GPU can become temporarily unusable for GPU AI workloads. When planning AI hosting, always choose GPUs with proven and stable ecosystem support.

3. Think about security before it is too late

Security is one of the hardest topics in AI model hosting. Most agent-based systems treat it as an afterthought.

The reality is that AI is currently one of the most attractive targets for attackers. That makes proactive security a must, not an option.

A basic security checklist for AI models and agents:

  • Agent-level firewall. Everything that is not explicitly required is blocked by default.
  • Proper prompt configuration that covers all expected usage scenarios. Often referred to as an AI firewall.
  • Data protection in RAG systems.
  • Runtime defense and guardrails. Defines and monitors how AI agents interact with users and data, enforcing rules during active operations.

Building these controls early makes AI deploy far more predictable and safe.

4. Plan infrastructure costs in advance

It is always tempting to rent a powerful GPU cluster and run top-tier models. This works well until the budget runs out.

Your infrastructure should not significantly exceed your actual goals for accuracy, reliability and load. Smart AI hosting is about finding the balance between cost and performance.

Careful capacity planning helps you avoid painful scaling decisions later.

5. Bring niche expertise into your team

AI infrastructure is still a young and fast-moving field. Truly experienced specialists are rare.

At Kernul, we already have hands-on experience with AI deploy, AI hosting and GPU AI workloads. We help startups turn ideas into reliable products and bring them to production without unnecessary risk.

If you want to move faster and avoid common infrastructure mistakes, working with a team that has already been there can save you months of trial and error.