When we talk about your “hosting setup,” we simply mean where your AI solution is actually run: the servers, cloud services, or infrastructure that power things like your API, model inference, or training jobs. Different hosting choices can have big cost, performance, and maintenance implications, so it’s worth choosing an approach that matches your team’s capacity.
As you think about hosting options, it can be helpful to distinguish between the major cloud providers (AWS, Google Cloud, Azure), which offer a wide range of managed AI and data services, and the many lighter-weight cloud hosting options (such as VPS [Virtual Private Server] providers or simple app-hosting platforms) that offer simpler, lower-cost environments. The right balance depends on your workload, budget, and appetite for managing infrastructure — as well as how comfortable you are relying on one vendor’s ecosystem long term.
If you want simplicity and strong security defaults, going “all-in” on a major cloud platform can make sense. These providers offer managed services for model fine-tuning, vector search, RAG components, and scalable storage without requiring you to maintain infrastructure yourself. This is ideal when you expect frequent training, need autoscaling, or prefer a single integrated environment. The tradeoff is that you become more tied to that provider’s tooling and pricing model over time.
For teams trying to keep costs low, lighter-weight cloud hosting (such as virtual private servers paired with something like NGINX) can be a more affordable way to host APIs, internal tools, or other non-ML components. Cloud resources from the major providers can then be used only when you need heavier compute capabilities, like training or running ML workloads — reducing cost and limiting vendor lock-in.
Both approaches are valid. The decision often comes down to how much convenience you want from managed services versus how much flexibility and cost control you want to retain.
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This response has been generated by an LLM based on notes from PJMF technical consultations. All responses go through human review by our PJMF Products & Services team and are anonymized to protect our consultation participants.