The LLM Wiki started as a pattern for one person and a folder of notes. A team has more knowledge, more sources, and more to lose when it goes stale. Here is how the pattern changes for a team, how our marketing team runs one, and how to build your own on Documentation.AI.
AI agents already read your documentation, usually one fetched passage at a time. A seven-step guide to agent-ready documentation, organized around the three measures of agent readiness: access, freshness, and answerability.
Two block editors that publish almost the same documentation site, priced in opposite directions. Archbee sells one plan and charges for capability. GitBook charges again for every site and every login. We built on both and worked out which shape is cheaper for which team.
All three publish good documentation, and none of them includes the assistant your readers talk to in the price it advertises. Mintlify bills per site, GitBook per site and per login, ReadMe per project with the AI sold beside it. We built the same documentation on each and priced what a working team actually pays.
Both platforms keep your documentation in Git, publish an API reference, and sell a reader assistant. They disagree on almost everything else: who is allowed to write, what gets metered, and how much of the product your plan actually turns on. We built the same documentation on each and priced what a real team pays.
Archbee is a block editor with a knowledge portal attached. Mintlify is a Git repository with a docs site attached. Both sell AI separately, and they meter it in opposite directions. We built the same documentation on each and priced what a working team actually pays.
Both platforms keep your docs in a Git repository, ship a reader assistant, and target the same engineering-owned API team. They diverge on what the free plan does, what the second plan costs, and whether you need generated SDKs at all.
Mintlify sells a documentation site. ReadMe sells a developer portal that knows which reader called which endpoint. Neither one includes the assistant your readers talk to in the price it advertises. We built the same documentation on each and priced what a working team actually pays.
Fern gives its documentation platform away and prices the next step by sales call. This guide compares the alternatives on the questions that end most Fern evaluations: what happens at the eleventh editor and the second site, how much reader AI you actually get, and who replaces the SDK generation that made Fern worth buying in the first place.
Fern generates SDKs in nine languages, and nothing here replaces that. It also needs a Git repository before anyone can edit a page, includes one site on both its plans, and publishes no price between free and Enterprise. We built the same docs on both and priced what each really costs.
Both platforms publish good documentation, and both keep the reader-facing assistant on their most expensive self-serve plan. The decision usually comes down to who edits your docs, how many sites you run, and how many questions your readers ask.
Fern retired its Hobby and Team plans and now sells documentation in two tiers: a free plan for 10 members and one site, and a custom Enterprise contract. This guide explains what the free plan includes, what moved behind the quote, and how seats, sites, and AI credits decide which tier you land on.
Fern generates documentation and SDKs from a single API specification, and it now gives the documentation half away. This review assesses the editor, API explorer, Fern Agent, analytics, and preview through hands-on use, and explains what the free plan does not cover.
Redocly multiplies a per-seat rate by the number of product modules you switch on, holds the reader assistant and analytics for Enterprise, and asks every contributor to open a pull request. This guide compares the five alternatives teams actually shortlist, with published prices, real plan boundaries, and what each one costs a growing team.
Agents already read your site the slow way. WebMCP lets the page hand them a short list of tools instead. It is early, it does not replace what you already publish for agents, and documentation is the safest place to try it.
Redocly prices each seat by plan, multiplies that rate by the modules you enable, and meters pages and projects underneath. This guide explains each of those dimensions and how to combine them into a monthly or annual estimate you can defend to a budget owner.
Redocly treats documentation as code and builds every workflow around Git. This review assesses the editor, API rendering, review process, reader assistant, and analytics through hands-on use, and explains how per-seat, per-module pricing adds up and which teams the platform suits.
Redocly is an API tooling company with a documentation platform inside it, and it gives a spec-first team what it wants: linting, mock servers, five Git providers, and a reviewer that reads OpenAPI and Markdown together. It also has no free plan, no block editor, a 100-page ceiling on Pro, and an Ask AI that only turns on at Enterprise. We built the same docs on both and priced them.
Archbee builds a good knowledge portal at a low entry price, then sells the AI, the analytics, the API, and PDF export on top of it. This guide compares the alternatives on what arrives with the plan, what a free tier lets you test before you commit, and what it takes to move your content out.
Archbee's plan prices are among the lowest in the category, and much of what documentation teams expect is sold separately. This guide explains both published plans, the AI token add-on, each paid add-on, and how to estimate the full monthly cost before you commit.
Archbee builds a knowledge portal that non-technical teams can run without help, and it does that part well. What surprised us was the invoice: analytics, the API, PDF export, and the AI itself are all sold separately. We built the same docs on both platforms and added up what a real team pays.
Archbee positions itself as a knowledge portal rather than a documentation tool, and much of its capability is sold as add-ons. This review assesses the editor, content reuse, Git workflow, shadow docs, reader AI, and analytics through hands-on use, and explains how those add-ons shape the total cost.
Theneo turns an API specification into a designed portal, then puts review workflows, design control, and its documentation agent on the upper tiers. This guide compares the leading alternatives, free and open-source options included.
Theneo builds an API reference fast. The questions worth asking before you commit are what happens to the documentation that is not reference, and what Theneo's per-plan caps on endpoints, projects, and reader AI cost you as you grow. We built the same docs on both platforms and priced what a growing team really pays.
Theneo bills per workspace rather than per seat, and limits projects, developer portals, and API endpoints by plan. This guide explains what each tier allows, how its unlimited AI is qualified, and which capabilities are priced only on request.
Theneo turns an API specification into a designed developer portal quickly, and bills per workspace rather than per seat. This review assesses the editor, API reference, AI features, analytics, portal builder, and Git sync through hands-on use, and explains how plan limits determine the tier a team needs.
GitBook charges for sites and for people separately, and meters its reader assistant on top of both. This guide explains how those charges combine, where each AI feature begins, and how to estimate the cost of a single documentation site or several before you commit.
GitBook pairs an editor that leads its category with a pricing model built around individual sites. This review assesses the editor, Git Sync, API reference, agent, and reader AI through hands-on use, and explains how costs change as a team adds sites and people.
Mintlify's plan price is only part of what you pay. This guide explains what each plan includes, how the shared AI credit balance is spent, what happens when it runs out, and how to estimate a monthly bill that reflects how your documentation is actually read.
Mintlify has become a leading platform for polished developer documentation, and its pricing has changed considerably this year. This review assesses the editor, API playground, assistant, and agent through hands-on use, and explains what the free plan withholds and how costs behave as usage grows.
ReadMe's plans each cover a single project, and several of its most useful capabilities are billed beside them. This guide sets out what each plan includes, how the add-ons and additional admins change the total, and when an Enterprise contract becomes unavoidable.
ReadMe sells one project per plan and charges separately for reader-facing AI and API metrics. Documentation.AI sells one project that holds several products. We built the same API documentation on both, and the difference turned out to be almost entirely in the pricing model rather than the product.
ReadMe offers an exceptionally strong interactive API reference, surrounded by capabilities reserved for higher tiers. This review assesses the editor, AI tools, seat and role model, and the upgrade to ReadMe Refactored through hands-on use, and explains what teams should expect to pay.
Our documentation agent on a branch has to search that branch, not production. turbopuffer's copy-on-write namespace branching gives every docs branch its own search index in about a second, with no re-chunking, no re-embedding, and no data copying.
Learn how to write a product manual. Get a practical guide to product manual structure, content, best practices, examples, and maintenance.
An instruction manual is judged on one thing: whether the reader reaches the working end state. Here is how to write one for software, from prerequisites that carry their weight through expected output, inline failure handling, and the clean-room test that proves it works.
A practical user manual guide: how to create a user manual from scratch, a template you can copy, and what changes when you write a user manual for a software application.
Protect documentation beyond passwords. Learn how access control, authentication, and delivery-first systems keep content secure, scoped, and trackable. A practical guide to managing sensitive docs without losing control as your team scales.
Reduce support tickets with better documentation and AI. Learn how structured docs power AI deflection, agent copilots, and self service support while keeping product knowledge current and measurable through the right metrics. A practical playbook for scaling support without hiring more agents.
Documentation in 2026 includes API docs, public product guides, internal knowledge bases, developer documentation, and SOPs. Each serves a different audience, which is why no single tool fits all. This guide compares the best AI documentation tools by use case.
Discover the best developer documentation tools in 2026 with this complete comparison of platforms for API docs internal knowledge bases and unified systems. Includes top choices like DocumentationAI, Mintlify, GitBook, and more with pricing features AI support and onboarding performance insights.
API documentation in 2026 is evaluated by onboarding speed, interactive testing, and real-world adoption. This guide compares the best API documentation tools based on API playgrounds, default examples, pricing, and how well they support customer-facing APIs.
Compare the best Document360 alternatives in 2026. See how modern documentation tools differ in workflows, automation, pricing, team fit, and long-term maintenance. This guide helps you choose the right platform for evolving documentation needs across product, support, and engineering teams.
ReadMe builds an exceptional interactive API reference, then places roles, additional projects, and full reader AI on higher tiers or separate add-ons. This guide compares the platforms ReadMe teams most often shortlist on the points that decide a switch, and is plain about where ReadMe still leads.
GitBook still sets the standard for block editing, and its pricing is still built around one site at a time. This guide compares the strongest alternatives on the questions that usually end a GitBook contract: the cost of adding docs sites, reader-facing AI, control over the published design, and how your content gets out.
Document360 and Mintlify approach documentation from opposite ends. This hands-on comparison explains how managed knowledge bases differ from docs-as-code workflows, where each platform fits best, and what teams should consider as documentation grows in 2026.
Compare Document360 and ReadMe to understand how governed knowledge bases and API-centric documentation differ. This hands-on comparison explores ownership models, editing workflows, publishing controls, and where each platform fits as documentation scales in 2026.
Compare GitBook and Document360 to understand how collaborative documentation and governed knowledge bases differ. This hands-on comparison examines ownership models, editing and publishing workflows, AI assistance, and where each platform fits as documentation scales across teams in 2026.
Compare ReadMe and GitBook to see how each documentation platform supports modern teams. This hands-on comparison explains differences in API documentation, collaboration workflows, publishing, and maintenance as docs scale in 2026.
Compare Document360 and Documentation.AI to understand how each platform supports modern documentation teams. This hands-on comparison covers collaboration, publishing, and long-term maintenance, and explains why many teams evaluating a Document360 alternative are choosing Documentation.AI in 2026.
Both platforms publish good documentation. The difference shows up on your second product, when GitBook starts charging again and Documentation.AI does not. We ran both through the same build and priced what four docs sites really cost.
Mintlify and Documentation.AI both offer docs-as-code workflows, but they differ in editor scope, what the AI agent maintains, and how credits are priced. Documentation.AI suits mixed teams that need structural changes without config edits, a far larger credit allowance, and an assistant that reports back the questions your documentation failed to answer.
Product documentation is no longer something teams update occasionally; it’s now a living part of the product. AI documentation keeps content accurate, aligned with fast releases, and helps tools like ChatGPT and Claude give reliable answers while making updates easier to maintain.
Good documentation isn’t about writing more; it’s about giving users exactly what they need. The Diátaxis Framework helps teams organize content by user intent, making docs clearer, easier to navigate, and far more effective. Here’s how Diátaxis brings order to documentation.
Mintlify publishes some of the most polished documentation available, and it keeps its AI, its governance controls, and additional sites on paid tiers. This guide compares the leading alternatives, free and open-source options included.
In the AI era, the importance of documentation is huge. Treat docs as part of your product. Structure them, keep them current, add MCP access and a helpful AI assistant. You’ll boost discovery, speed onboarding, cut errors, and keep users around.