As your AI initiatives move from prototype to production MLOps, you'll face a critical decision: should you build an internal platform team or partner with specialized consultants? If you choose the latter, it's important to know what to look for in an AI infrastructure consultant. This decision is often tied to whether you truly need a custom solution; in fact, most companies don't need a custom ML platform and can succeed with existing tools.
The wrong choice can lead to either platform overengineering or a "black box" infrastructure that your team can't maintain.
The Case for Building Internal Teams
If AI is your core product and you have a high volume of unique infrastructure requirements, building an internal team is a long-term investment in institutional knowledge. However, the true cost of hiring and retaining senior AI infrastructure engineers is often 2-3x their base salary.
The Case for Specialized Consultants
Consultants bring "been there, done that" expertise in compliance, cost optimization, and high-availability serving. They are ideal for:
- Initial platform setup and Terraform codification.
- Bridging the gap while you hire.
- Solving specialized bottlenecks like latency tuning.
Final Takeaway
The most successful teams often use a hybrid approach: they partner with experts to build a robust, standardized foundation and then hire internal owners to maintain and evolve it. This minimizes time-to-market while ensuring long-term operational independence.
Deciding between building an internal team or bringing in external expertise? We help teams design and build production AI infrastructure while training their internal teams for long-term success. Book a free consultation and we’ll help you plan your infrastructure roadmap.
