How to Make Infrastructure Decisions When Implementing AI in Your Company
For some, this decision might seem obvious, while for others, it's a question without clear answers. There are many options to consider, but regardless of the situation, it's worth taking the time to think it through, as choosing the right approach can be crucial to a company's success.
It appears that AI services are becoming another layer of cloud services. However, it's important not to limit ourselves to this perspective. There are many companies striving for unique and non-standard solutions to build a competitive edge without relying on what everyone else has.
So, how can we help companies make the right decision?
From an infrastructure perspective, three main approaches can be considered:
1. Cloud
This approach relies entirely on services that can be purchased from professional AI solution providers such as OpenAI, Microsoft, or Google. The key benefits here are the speed of implementation, a wide range of ready-to-use services, and high quality. Additionally, competition among providers works in favor of the customer—every few months, new discounts become available.
However, for many companies, a significant drawback might be the fact that their data is being passed to the provider. Although large tech companies guarantee that this data won't be used for training future models, not all companies find these assurances satisfactory. A step in the right direction is the localization of processing centers within the European Union, which may be particularly important for companies in this region.
2. Hybrid
This approach combines the flexibility of using your own resources with data processing in a data center, without the need to purchase expensive and hard-to-get GPU processors. This method is popular among companies that process large amounts of data locally or with another cloud provider, maintaining full control over the data enrichment and integration process. Only selected steps are carried out using AI services from external providers.
The hybrid approach offers flexibility in quickly changing AI providers and excellent cloud scalability for components that require GPU usage. This allows for the handling of a high volume of queries to available services during critical times.
3. On-Premise
This approach is the most demanding, as it requires all components to be hosted on the company's own servers. Companies must make a series of complex decisions, typically handled by large tech firms. These include selecting the right software and libraries to support model operations on physical hardware and optimizing the infrastructure. Businesses need to be prepared for operational workloads that demand quick response times, as well as for batch processing of large data sets.
Despite the challenges, the Full On-Premise solution offers significant advantages. It provides access to models that are not available from cloud providers, such as specialized medical models or those tailored to specific languages.
Conclusion
This is not the end of the choices facing companies looking to implement AI into their processes. In fact, it might just be the beginning. The options mentioned are not the only possibilities—each has different variations and comes with future opportunities. It’s possible to start with one of these approaches and later transition to another. There are possible strategies, that minimize risk, reduce costs, or shorten the implementation time.
Everything depends on business goals. There are many possibilities, and it’s important to understand them!
Author: Mariusz Jażdżyk
