How to choose an AI platform for your business

An AI platform is the combination of service, contract and tools a company uses to access artificial intelligence models. It may be a ready-made assistant for employees, an API for developers to integrate into systems or a cloud environment that brings several models together.

The choice usually starts with the wrong question: “which is the best model?” Models change every few months. What remains is the contract, the integration with systems and the teams’ habits. This article proposes seven criteria, in the order in which it is worth analyzing them. It does not compare vendors; the examples cited serve to show what to look for in each one’s documents.

1. Define the use case before the vendor

Different platforms solve different problems. Before comparing, answer:

• Who will use it? Employees in a chat assistant, an automated system calling an API, or both.
• For which task? Writing, customer service, document analysis, programming, data extraction.
• With which data? Public, internal, personal, sensitive.
• With what consequence of error? A draft reviewed by a person tolerates more error than an answer sent straight to the customer.

These answers rule out options right away. A tool that does not offer a corporate contract is not suitable for customer data, however capable it may be. If the goal is still vague, the guide on how to automate business processes with AI helps you choose and map the process first.

2. Measure quality with real tasks

Public rankings measure performance on standardized tests, which may not resemble your work. The most reliable way to compare is to build your own evaluation set:

• Set aside a few dozen real examples of the task (the number is a newsroom suggestion, not a rule), with the expected answer or clear quality criteria.
• Run the same examples on each candidate platform, with the same instructions.
• Ask people who know the subject to evaluate the answers without knowing which platform they came from.
• Also record response time and the cost of each run.

This set remains useful after the choice: it serves to test each new version of the model. The article on how to evaluate the quality of AI responses details evaluation methods, and the one on AI hallucinations explains why wrong but convincing answers require this care.

3. Read what happens to the data

This is the criterion that most separates a consumer tool from an enterprise platform. Look in the documentation and the contract for the answer to five questions:

1. Is the data used to train models? OpenAI, for example, states that it does not use inputs and outputs from the ChatGPT Business, Enterprise and Edu plans or from the API for training by default, while for individual services the use depends on the user’s settings (OpenAI, How your data is used to improve model performance, consulted on October 8, 2026).
2. How long is it kept, and why? Even without training, many services retain data to monitor abuse.
3. Who can read it? Microsoft’s documentation on models sold through Azure, for example, describes that prompts flagged by the abuse monitoring system may undergo human review by authorized employees, and that approved customers may request modification of this monitoring (Microsoft Learn, Data, privacy, and security for Foundry Models).
4. Where is it processed? The same page explains that, in Global deployments, prompts and responses may be processed in any region where the model is available, while regional deployments stay in the geography chosen by the customer. This difference matters for the LGPD.
5. What is the vendor’s role? Under the LGPD (Lei Geral de Proteção de Dados, Brazil’s general data protection law), the company that decides on the processing is the controller, and the vendor that processes data on its behalf is, as a rule, a processor, required to follow the instructions received (Planalto, LGPD, arts. 5 and 39).

If data leaves Brazil, international transfer is only allowed in the cases listed in Article 33 of the LGPD, such as countries with adequate protection or contractual guarantees. Involve the legal team and the data protection officer at this stage. The article on data privacy when using AI tools shows the user’s side of the same issue.

4. Assess the security of the application, not just the vendor

The vendor takes care of its own infrastructure. The risks of how AI is used inside your company remain yours. The list by OWASP, a reference community in software security, brings together the top ten risks of LLM applications in 2025, including prompt injection, sensitive information disclosure, supply chain failures, excessive agency and unbounded consumption (OWASP, Top 10 for LLMs 2025).

When comparing platforms, check whether they offer:

• Per-user access control and integration with corporate login.
• Usage logs (who sent what, when) accessible to the administrator.
• Spending and request limits per project or key.
• Granular permissions for connectors and agents that access other systems.

NIST, the US standards institute, notes in its AI risk management guide that third-party data, software and systems speed up work but complicate risk measurement, and that all parties should manage the risks of the systems they develop, deploy or use (NIST AI RMF 1.0). The guide organizes this management into four functions: govern, map, measure and manage. Using the same structure in the evaluation helps avoid forgetting steps.

5. Calculate total cost, not list price

Billing models vary: per-user license, consumption per token (the unit of text the model processes) or combinations of both. The price per million tokens says little on its own. The real cost depends on the length of the instructions, how many times the system calls the model per task, batch or cache discounts and human review time.

Use the test data from step 2 to estimate the cost per completed task, and add integration, team training, governance and maintenance. The article on how to calculate AI ROI in companies shows how to put this calculation together, and the one on tokens in AI models explains the billing unit.

6. Check integration with what already exists

An excellent platform that does not talk to the company’s systems becomes one more open tab. Ask:

• Are there ready-made connectors for the systems the company uses (email, file storage, CRM, ERP)?
• Is the API documented and stable? Are there libraries for the team’s languages?
• Is it possible to connect internal documents with per-user access control? The article on RAG explains this technique.
• Is the platform also available on the cloud the company already uses? This can simplify contract, billing and compliance.

7. Plan the exit before the entry

Models have a lifecycle. Anthropic, for example, documents that deprecated models receive a retirement date, that requests to retired models fail and that customers with active usage are notified at least 60 days in advance for publicly released models (Anthropic, Model deprecations). Other vendors have their own policies. In practice, every company that uses AI will migrate versions from time to time, and each migration requires testing again.

Some practices reduce dependence on a single vendor:

• Isolate the model call in its own layer of the system, so you can switch vendors without rewriting everything.
• Keep instructions, examples and the evaluation set outside the platform.
• Prefer open formats for documents and knowledge bases.
• Assess whether an open model, which can run on your own infrastructure, covers some of the cases. The article on open or proprietary models discusses this choice.

A summary roadmap

1. List the use cases, the data involved and the cost of an error.
2. Eliminate options without a corporate contract or incompatible with the data.
3. Test two to four candidates with real tasks and blind evaluation.
4. Read the contract and documentation on training, retention, human review and processing region.
5. Check security, integration and cost per completed task.
6. Start with a limited pilot, with metrics defined in advance, and keep the evaluation set for future version changes.

In the newsroom’s assessment, many problems show up before the technology is chosen, such as a poorly defined goal or a lack of suitable data. The article on why enterprise AI projects fail covers these points.

Key takeaways

• The best platform is the one that solves your use case with quality measured on real tasks, not the one at the top of a ranking.
• Contract terms and documentation on data (training, retention, review, region) weigh as much as the model’s capability.
• Total cost, security, integration and an exit plan prevent surprises when the model changes or usage grows.