Comparing Major Generative AI Services
Comparing Generative AI Services
How do the major generative AI services compare?
- Strengths differ by use case, not by rank
- Data handling and admin features are the real selection criteria
- You don’t have to pick just one
“Which service is best?” rarely gets a clear answer, but “What should we use for which task?” does. Once you have this perspective, the training team’s approach changes. Instead of rewriting the course materials from scratch every time a new service launches, you can keep a single page of decision criteria and just swap out the candidate list. Practitioners also build a habit of asking what kind of task this is first, rather than “this is allowed, that isn’t.”
In practice, it helps to sort services into four groups: general-purpose conversational services, search-and-citation hybrids, AI built into office suites, and API-based options connected to internal systems. The first three are per-user monthly flat fees (typically around KRW 30,000 (≈ US$22)), while API-based options bill by usage, so the cost model itself is different. What people often miss is the line between personal and enterprise plans. Admin console, single sign-on, usage log retention, and a guarantee that input data is not used for training are where plans differ, not where services differ.
You also need to separate “comparing services” from “comparing models.” The same model often powers several services, so even when model rankings change, the difference you actually feel on screen is smaller than you would expect. What really differs is the service-side design: how attachments are handled, how internal documents connect, answer length limits, and how naturally the service produces Korean report formats. Public benchmark scores are only a reference unless you can reproduce them on your own work documents.
The most common failure is picking a standard tool while the front line keeps using personal free accounts. Free accounts may use your input for training, and admins cannot see usage history, so if price lists or HR files leak, there is no way to confirm what left and when. Retiring free accounts is harder than choosing the standard. The opposite problem is just as costly: organizations that overhaul the standard every time the rankings change. Migrating accounts and retraining costs more than the performance gap is worth, and business teams disengage with a single “We’re changing again?”
John KimInstructor John Kim, author of B2B Marketing Design and CEO of Nanum Consulting
Seolyeong ParkGenerative AI and AX training specialist with 17 years of hands-on experience and 200+ lectures, helping non-technical staff put AI to work right awayThis question became a practical one after conversational generative AI went mainstream in late 2022 and competition between services took off. As model performance converges quickly, the selection criteria are shifting from raw performance to data policy, integrations, and admin features.
Consider a hypothetical case. Company A, an auto parts manufacturer with 1,200 employees, had let each department pick its own AI tools. An internal audit found that the quality team had pasted client drawing specifications into a free account, and the training team was handed the job of choosing a company-wide standard tool within three months.
- After two weeks of reviewing usage across departments, Manager Kim on the training team found 340+ personal free accounts registered with company email. The planning team lead trailed off: “So we’ve been using these without any approval.”
- They narrowed the use cases to four: competitor research, report drafting, meeting minutes, and English email translation. From last month’s actual work, they picked three tasks for each, 12 test tasks in total.
- They worked with Mr. Lee, the information security team lead, to compare the enterprise plan terms of four candidate services. Only three had training on input data switched off by default, and only two retained usage logs for 90 days or more.
- Twenty people, five from each department, ran the same tasks on two services side by side for two weeks. Search-based tools that attach source links clearly led on research tasks, while conversational tools led on 20-page report drafts.
- They settled on one research tool and one document tool as the standard, opened a KRW 30,000 (≈ US$22) per person per month plan to 180 employees, and the information security team blocked the remaining free accounts in phases over six weeks.
- Three months later, the number of personal free accounts registered with company email had dropped from 340 to 18, and the average time to draft a weekly report fell from 3.5 hours to 1.2 hours.