API vs local AI models
Your staff may already be using tools such as ChatGPT, Claude, Gemini and Copilot. But if you need to add your own rules, guardrails, security controls or build systems you can rely on, you have two main options.
- API access to frontier models
- Maintain your own local model
The choice comes down to four things: cost, performance, maintenance and security.
APIs
An API is a direct connection between your software and a model hosted by a provider such as OpenAI, Anthropic or Google. It gives your systems the same frontier models the chat tools use, but on your terms: your system decides what is sent, what comes back and what happens next.
Local models
Open models, such as Llama, Mistral, Gemma and Qwen, can be downloaded and run on infrastructure you control: your own servers or a private cloud. Your systems use them in the same way as an API, but nothing is sent to an AI provider.
How the three compare
Cost
API
An API is paid for per use. There is nothing to buy up front and no hardware to maintain. The bill grows with volume, so extensive usage on higher tier models can get expensive. To keep costs lower, most providers offer a range of models and many day to day tasks can utilise their cheaper models.
Local model
A local model can draw significant capital costs. Hardware bought up front, or GPU hosting rented by the month, plus a technical team to run and maintain it. At very high, steady volumes it can work out cheaper per task than an API but without significant spending on infrastructure you may be throttled on performance.
Performance
API
The most capable models are only available through an API. They are the strongest choice for complex, varied or open-ended work. You take advantage of the processing power and the latest frontier technology provided by the likes of Anthropic, Google and OpenAI.
Speed and availability depend on the provider, and busy periods, usage limits and outages are all outside your control.
Local model
Local models are smaller. They are less capable on open-ended work but handle focused, well-defined tasks well, and can be tuned for a particular job. Their speed depends on your hardware and the complexity of your tasks but can suit very high volume, well scoped work.
Being local, they are not affected by provider limits or outages, and they can even keep working without an internet connection.
Maintenance
API
With an API, the provider runs and maintains the model. Updates are pushed regularly, a range of models are available at the flick of a switch and all infrastructure, security and outages are dealt with by the provider.
Local model
With a local model, you run it: the hardware, the updates, the monitoring and the security patches. This gives you more control but also considerably more liability.
Security
API
With an API, your data goes to the provider. Under business API terms, none of the large providers use your data to train their models, and many offer controls on how long it is kept and where it is processed, but the terms vary and need checking against your obligations.
Your protection is by contract and your level of trust in the provider.
Local model
With a local model, your data never leaves infrastructure you control, which can be the only acceptable option for highly regulated or contractually restricted data. The flip side is that keeping it secure is your responsibility and you will need the resources to ensure that.
Your protection is by physical infrastructure that you control and your level of trust in your design and your team.
Which to choose
Choose an API for the most capable models, a quick start and uneven demand. Choose a local model when data must stay in-house, when volumes are high and steady, or when you need to be completely independent or isolated.
It does not have to be one or the other. If the system around the model is designed properly, the model is a replaceable part and you can make that decision further down the line as your needs change.