Just had a project where we needed to overhaul our API docs. The classic choice: hire a technical writer (quoted at ~$12k for the project) or try the AI route. We went with ChatGPT-4, plus some engineering time for oversight and formatting. Final cost? About $300 in API credits and 8 hours of a dev's time.
The quality wasn't perfect, but it was *surprisingly* functional. The AI hallucinated a few parameter descriptions and can't *truly* understand the product's nuance. But for generating the boilerplate structure, initial drafts, and filling out standard sections, it cut 80% of the grunt work.
Here's the breakdown:
* **Hidden Cost:** The "oversight" time. You need a subject matter expert to fact-check *every line*. If your engineers are expensive, that $300 in credits quickly becomes $3k in internal time.
* **Quality Ceiling:** It's good at being *correct*, but not at being *insightful*. It won't produce those brilliant examples that make a complex feature click for a new user.
* **The Sweet Spot:** Routine, repetitive documentation where consistency is key. Think auto-generating CLI command references from your OpenAPI spec, or creating baseline templates.
For a startup burning cash, the AI route is a no-brainer savings play. For a public-facing, core product where docs are a sales tool? You'll still need a human to polish the final 20% that makes it great. It's not an either/or—it's a tiered strategy. Use the AI for the heavy lifting, then pay for human contractor hours to apply the finishing touches and strategic insight. That's where the real cost/quality balance is.
Cloud costs are not destiny.
I'm a principal DevOps engineer at a mid-sized fintech (~200 engineers) where we've been running our own docs platform for three years, managing everything from API specs to internal runbooks, so I've pushed both human writers and AI-generated content through multiple revision cycles.
* **Hidden cost multiplier:** You quoted $12k for a contractor and $300 in API fees. The real gap is the 2-3 rounds of revisions after the first draft. With a competent human writer, that's included. With AI, each revision loop is another cycle of prompt engineering, fact-checking by your SME, and editing, which typically multiplies the oversight time you mentioned by 3x. I've seen that initial 8 hours of oversight balloon to 30+ hours for a full doc set.
* **Quality floor vs. quality ceiling:** A human contractor has a lower floor (you can hire a bad one) but a much higher potential ceiling for clarity and insight. GPT-4 has a high floor for grammatical correctness and structure but a hard, immovable ceiling for understanding context. It will never write that one perfect analogy that unblocks your entire user base. For reference documentation, that's fine. For conceptual guides or tutorials, it's a dealbreaker.
* **Long-term maintenance debt:** A contractor delivers a final artifact. AI-assisted docs become a living process. When your API changes next quarter, you're on the hook for running the update cycle again (prompts, checks, edits). The human cost shifts from creation to maintenance, and it's persistent. We budget 20% of the original AI-assisted creation time for each major update.
* **Fit for purpose:** For generating 90% of a reference document from an existing, validated OpenAPI spec, AI is now a default. For net-new product documentation explaining *why* something exists and how it fits into a user's workflow, a human writer who can interview your product leads is still the only tool that works. The breakpoint is whether the source material is structured data (AI wins) or tribal knowledge (human wins).
I recommend GPT-4 + SME oversight for any documentation that can be deterministically derived from code, specs, or configs. For everything else that requires synthesis, narrative, or pedagogy, hire the human. To make a clean call, tell us what percentage of your docs are generated from spec files versus written from scratch, and how often the underlying product changes.
Been there, migrated that