ChatGPT alternative for business: when to upgrade from raw LLM
If your team’s relationship with AI is “we all pay for ChatGPT and complain about output quality,” you have outgrown the seat-based model.
Most companies’ first encounter with serious AI is a row of ChatGPT Team seats. Twenty people at €25/seat is €500/month, which is fine. The problem comes a year in. Quality is uneven, brand voice has drifted, output isn’t reliably shippable, and you have no audit trail of what was generated by whom. The seat model has outlived its usefulness.
Three options past ChatGPT seats
1. Build internal AI infrastructure. Hire 2–3 ML engineers, build agent orchestration, run on Bedrock or Azure. Cost: €35k+/month all-in. ROI: only justified at 50+ people and very specific data moats. Most companies that try this regret it within 18 months.
2. Buy a vertical SaaS tool. Jasper for marketing, Harvey for legal, GitHub Copilot for engineering. €50–50k/month depending on tool. Better than raw chat for the specific function. Locked in to one vendor’s opinion of the workflow.
3. Subscribe to managed AI services. Productized teams of agents under a human operator. €1,500–9,000/month depending on scope. You get shipped artifacts (articles, reconciled books, code changes), not just prompt assistance. This is what we sell.
How to choose
Build if: AI is your product, you have 3+ ML engineers, your data is so unique that no off-the-shelf tool fits.
Buy vertical SaaS if: you have one well-defined function with a mature category leader (Jasper for content if you already have a strong content team that just needs leverage; Harvey for legal at large firms).
Subscribe to managed agents if: you want the work done, not the tool to do it yourself; you’re a small-to-mid company without the team to operate AI infrastructure; you value outcomes over flexibility.
The honest answer for most B2B SaaS at 5–40 people
Option 3. You don’t have ML engineers to spare on infrastructure. Vertical SaaS locks you into one workflow per function. Managed services let you turn on Growth, Ops, Books, Dev independently as you need them and stop them when you don’t.
What you actually lose by leaving ChatGPT
You keep ChatGPT (or Claude or Gemini) for ad-hoc work. Brainstorming, research, prototyping. Those are great uses of the chat interface. What you stop doing is using them as the production tool for shipping work, because they were never designed for that.
An analogy: ChatGPT is your IDE. Managed AI services are your CI/CD pipeline. Both are necessary. They are not the same thing.
Want to see how this works for your team in practice?
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