Cloud AI Services or Your Own Models

The four questions

1. Where can the data go? If it cannot leave your infrastructure, the decision is largely made.
2. What is the volume? Low and spiky favours managed; high and steady favours self-hosted.
3. Do you need customisation? A fine-tuned model on your own data may not be available as a service.
4. Who operates it? Self-hosting means someone owns GPU capacity, model updates and serving performance.

Most teams answer these and find the decision obvious. The difficult cases are usually those where data policy and cost point in opposite directions.

What managed services cover well

• Speech to text and text to speech — mature, accurate, and expensive to match yourself.
• Optical character recognition and document extraction — a great deal of engineering behind a simple API.
• Translation — strong quality across many language pairs.
• Image labelling and moderation — well-trodden problems with reliable services.
• Text embeddings — cheap, fast, and a sensible default for search and retrieval.
• General language models — the largest models are impractical to self-host for most organisations.

For all of these, the managed option is usually better and cheaper than a self-hosted equivalent unless volume is very high or data cannot leave.

What self-hosting is good for

• Data that cannot leave your environment for legal or contractual reasons.
• High, steady volume where per-request pricing has become the dominant cost.
• Models fine-tuned on proprietary data.
• Predictable latency without dependency on an external service.
• Specialised domains where general-purpose services perform poorly.
• Avoiding dependency on a provider’s pricing and availability decisions.

The middle option

Most platforms now offer a managed way to run open or custom models on dedicated capacity — a model garden, an inference endpoint, or a deployed container. You supply the model and they operate the serving infrastructure.

This suits organisations that need a specific model and do not want to run GPU infrastructure. Cost sits between the two extremes, and the operational burden is much closer to managed.

Data handling, specifically

Before sending anything to a managed AI service, establish:

1. Whether inputs are retained, and for how long.
2. Whether inputs may be used to improve the provider’s models, and whether that can be disabled.
3. Which region processes the request, and whether that satisfies residency requirements.
4. Whether a data processing agreement is in place.
5. What the provider logs, and who can access it.

All major providers publish this and most offer enterprise terms with retention disabled. Read the specific service’s terms rather than assuming the platform-wide position applies. See cloud compliance basics.

A pragmatic path

1. Start with a managed service. It is faster to a working product and tells you whether the feature is valuable at all.
2. Instrument request volume and cost per request from day one.
3. Watch for the point where monthly API cost approaches the cost of dedicated capacity at reasonable utilisation.
4. Prototype self-hosting before you need it, so the decision is informed.
5. Keep the interface abstracted, so swapping the backend is a configuration change rather than a rewrite.

That last point costs little upfront and preserves the option. A thin interface between your application and whichever model serves it is worth writing early.

Common questions

Where to go next

See training versus inference cost and vector search and retrieval.