Technology profile
OpenAI
- What it is
- An artificial intelligence company that develops ChatGPT and, through the OpenAI API, offers programmatic access to its language, image and voice models.
- Type and licence
- Commercial service, largely proprietary models. The ChatGPT app has subscriptions; the API is paid by usage. Check the terms on the official website.
- Good for
- Assistants built into applications, extracting data from documents, classifying messages, semantic search, AI agents, transcription and voice features.
- Less suited to
- Automated decisions without human review, exact calculations without dedicated tools, cases where data must not leave your own infrastructure.
- Alternatives
- Anthropic Claude, Google Gemini, Mistral, open source models run on your own infrastructure; OpenAI models are also available through Microsoft Azure.
- How we use it
- Through the API, in integrations with the company's systems (CRM, email, documents), with clear instructions, controlled sources, tests and human review where it matters.
The OpenAI API is the interface through which a company's applications and systems can directly use the artificial intelligence models developed by OpenAI, the same company that created ChatGPT. Unlike the ChatGPT app, where a person writes and reads the answers, the API lets the AI work automatically inside your processes: in the CRM, in emails, in the customer portal or in the document workflow.
When someone says “we want the ChatGPT API for our company”, this is usually what they actually mean: an assistant or automation that uses a language model on the company's data, with no manual copying between windows. Below we explain what it involves, what it can do, how data is handled, how the cost adds up and how to choose between providers.
In short
ChatGPT = an app for people. The OpenAI API = access for applications, so the AI can work inside the company's processes. The API is paid by the volume of text processed (tokens), has different data terms from a personal account and needs a carefully built integration: instructions, sources, tests and human review.
What the OpenAI API is, explained simply
An API (short for “Application Programming Interface”) is a “socket” through which one program talks to another. In OpenAI's case, your application sends a request, for example an incoming email plus the instruction “classify the message and extract the order number”, and the model responds in a format the application can use further.
Several types of models are available through the API: language models for text and reasoning, models that understand images, models for transcribing and generating voice, models for generating images and “embeddings” models, which turn text into vectors for search by meaning. The range changes often, so check the current list in the official documentation.
ChatGPT vs the OpenAI API: what's the difference for a business?
- Who uses it: ChatGPT is used by people, in a chat window. The API is used by applications, without anyone typing each request by hand.
- Integration: ChatGPT works with whatever you paste or upload. The API can automatically receive data from the CRM, ERP, email or document library.
- Control: through the API you set exactly the instructions, the format of the answer, what data is sent and what is saved.
- Payment: ChatGPT has per-user subscriptions. The API is paid by usage.
- Data terms: the personal ChatGPT account, the business plans and the API have different conditions on how data is used and retained.
The two are not mutually exclusive. Many employees use a ChatGPT business plan in their day-to-day work, while repetitive, high-volume processes are automated separately through the API.
Typical integrations with the OpenAI API
- Smart inbox: classifying incoming emails, extracting the data (customer, order, issue) and proposing a reply that a person approves.
- Document processing: extracting fields from invoices, quotes or contracts into a structured format, with uncertain fields flagged.
- Assistant over the company's documents: answers from procedures and technical sheets, citing the source (the RAG approach).
- Semantic search: finding documents or products by meaning, not just by exact words.
- AI agents: the model uses tools you define, such as checking stock or creating a ticket, for multi-step tasks.
- Voice: transcribing calls or meetings and summarising them.
A conceptual example: an online store receives messages about orders every day. The integration reads each message, identifies the order number, checks the status in the platform and proposes a reply. Simple messages are approved with one click, while complaints go straight to a person.
How data sent through the API is handled
OpenAI has separate terms for consumer services (personal ChatGPT accounts) and for services for businesses and developers (the API, plans for teams and organisations). The differences may concern the use of data to train models, the retention period and the control options, such as choosing the processing region for certain accounts.
Watch out
Data policies change. Before sending personal data or confidential information through the API, read the current version of the business terms, the API data policy and the data processing agreement (DPA) on OpenAI's website. Do not use personal ChatGPT accounts for customer data.
- Send the model only the data needed for the task; anonymise or remove anything that is not needed.
- Decide which categories of data are never sent, for example medical data or passwords.
- Check the legal basis and how data subjects are informed when you process personal data, in line with GDPR.
- Keep API keys on the server, never in the browser or mobile app, and limit who has access to them.
How much does the OpenAI API cost? How the cost is calculated
The API is paid by usage, measured in tokens. A token is a piece of text, usually part of a word. Both the text sent (instructions, documents, conversation history) and the text received back are counted, and the price per token differs from one model to another.
- The model chosen: more capable models cost more per token than fast, economical ones.
- Request length: a 30-page contract sent with every question costs much more than the relevant fragments found through search.
- Volume: the number of requests per day, month or user.
- Optimisations: reusing shared instructions, batch processing for tasks that are not urgent and choosing the right model for each step.
Prices change frequently, so check them on OpenAI's official pricing page. For a project, a sound estimate starts from a test on your real volume of messages or documents. Also set spending limits in the API account, so there are no surprises.
OpenAI or another provider? How to choose the model
OpenAI is not the only option. Anthropic (Claude), Google (Gemini), Mistral or open source models run on your own infrastructure are serious alternatives, and quality differences shift with every release. A good choice is made on practical criteria, not general rankings.
- Gather 20–30 real examples from the process you want to automate, with the correct result known.
- Test two or three models on the same examples and compare accuracy, including in Romanian.
- Compare data terms, processing regions and contract options.
- Calculate the cost on your real volume, not on a single request.
- Check integration with your existing infrastructure, for example whether the company already works with a particular cloud provider.
Practical tip
Build the integration so the model can be changed through a setting, and the set of test examples can be rerun at any time. That way you can take advantage of new or cheaper models without rewriting the application.
What an OpenAI API integration project involves
The API call is the simplest part. The real work lies in what happens around it, and that decides whether the integration is useful or just a demo.
- Input data: where it comes from (email, CRM, documents), how it is cleaned and what is removed before sending.
- Instructions: the model's role, the business rules, the exact format of the answer and what it does when information is missing.
- Validating the result: automatically checking the format and required fields before the result reaches another system.
- Human approval points: where a person confirms before something is sent to a customer or recorded in the accounts.
- Logging and monitoring: what was sent, what was answered, what it cost and where errors occurred.
- Tests: a set of examples rerun with every change of instructions or model.
A well-run project starts small, with a single process, and expands only once the results on the test examples are stable.
The OpenAI API's limits: what it doesn't do on its own
- Hallucinations: the model can produce wrong information that sounds convincing, especially when data is missing.
- It doesn't know your company: without your documents and data, it answers from general knowledge, which may be out of date.
- Variability: the same request can get slightly different answers; structured output and tests reduce the problem.
- Provider dependence: models are retired and replaced over time, so the integration needs maintaining.
- Availability: like any online service, it can have outages; critical processes need a fallback option.
For legal, financial or medical decisions, or official messages to customers, the model's output remains a proposal that a person checks. Clear instructions, controlled sources and regular tests make the difference between an impressive demo and a system you can rely on.
At WEB-AZ we work with the OpenAI API, alongside other providers, in integrations with the company's systems. We start from the process that needs improving, test the models on real examples and build the integration so the provider can be changed if a better-suited option appears.
Next steps