This is mostly not a cost article. The price tiers get one table near the end, for a team deciding when to start paying, because the more interesting cost of a “free” AI tier is the part that never shows up on an invoice. Free has a price. It is just paid in something other than money, and the trade is worth seeing clearly before you make it.
The honest position up front: a free tier is not a scam, and sometimes it is the right call. But “free” is a billing arrangement, not a description of what you give up. Four things tend to be on the bill, and none of them are dollars.
What does a free AI tier actually cost?
Cost one: your data as the product
The oldest pattern on the internet applies here too. When the service is free, your usage is often the thing being monetized, and with AI tools that usage is unusually rich. Your prompts can become training input. The documents you paste, the code you ask about, the questions you would not say out loud, all of it can flow into a system you do not control.
This is the cost that local AI removes by construction, which is why it is worth naming sharply. The Atlas keeps returning to the same phrase for a reason: “no data leaving.” Open WebUI’s whole pitch is a private ChatGPT where, in the note’s framing, it looks the same and works the same but runs on your laptop with no subscription and nothing leaving. opencode pointed at a local model means the code never leaves the building. The contrast makes the hidden cost visible. A free cloud tier can quietly treat your input as an asset; a local tool cannot, because there is nowhere for the data to go.
The point is not that every free tier trains on you. It is that you usually cannot tell from the marketing, only from the terms, and the default you should assume is the less convenient one until the terms say otherwise.
Cost two: reliability at the worst moment
Free tiers ration. Rate limits, daily caps, queue priority behind paying users, and slower models reserved for the free seat. On an ordinary day you may never feel it. The problem is that rationing does not arrive on an ordinary day. It arrives when you are mid-task, on a deadline, asking the tool to do the thing you actually needed it for, and it tells you to wait or come back tomorrow.
This is a real cost even though it has no number, because it lands precisely when the work matters most. The Atlas frames the alternative plainly across the local tools: a model on your own machine has no rate limit and no per-query meter, and it keeps working on a plane, in a cabin, or during an outage, where the cloud tool simply stops. A free cloud tier is reliable until the moment you most need it not to throttle, and that is the moment its limits are designed to bite.
Research tools carry the same trade. Our comparison of local deep research with Perplexity and the cloud found a local loop removes the per-query cost and keeps the question on your machine, while the cloud still wins on breadth, freshness and speed.
Cost three: features dangled, then moved behind the wall
A familiar shape: the capability that drew you in is free today, prominent in the demo, and central to how you have started working. Then it becomes a paid feature, or the free version is quietly degraded, and the workflow you built now depends on a thing you have to pay to keep. You did not choose to start paying. You chose to stop being able to work without it.
The cost here is not the eventual fee. It is that the decision gets made for you, on the vendor’s timeline, after you are already committed. The defense is to notice which free capabilities you are building a habit around, and to ask, before you depend on one, what happens to your workflow the day it moves behind the wall.
Cost four: lock-in, the quiet one
The last cost is the freedom to leave, and it is the easiest to miss because nothing about it feels like a cost while you are inside it. Your history lives on their server. Your saved prompts, your presets, your accumulated context are in their format. The longer you stay, the more expensive leaving becomes, until “we could switch” has quietly turned into “we cannot really switch.”
It is worth seeing what the opposite looks like, because it clarifies what lock-in actually is. With Open WebUI, your conversation history lives in a Docker volume you own, on your machine. That is the inverse of lock-in: the data is portable because it is yours and it is local. A free cloud tier is convenient partly because it holds your context for you, and that same convenience is the mechanism that makes leaving hard later.
How do you check a free AI tier before you sign up?
You cannot tell these costs from the landing page. You can tell them from the terms, and reading them is a method, not a number. What to look for:
- The data and training clause. Search the terms and the privacy policy for how inputs are used: whether prompts and uploads are used to train or improve models, whether there is an opt-out, and whether a paid tier changes the answer. If you cannot find a clear statement, treat that absence as a finding.
- The limits, written down. Find the actual rate limits, daily caps, and which model the free seat gets. If they are vague or “subject to change,” that vagueness is itself the cost.
- What is free now versus guaranteed. Look for language reserving the right to change features and pricing. Almost every service has it; the question is how central the at-risk features are to how you plan to work.
- The export path. Before you commit, find out how to get your data out. If there is no clean export, you are looking at lock-in regardless of what the pricing page says.
Free AI tier limits compared: when does a team hit the paid plan?
The table below sets out the free AI tier limits, the data terms and the first paid team plan for the kinds of commercial tool a small team is likely to start on: a chat assistant, coding assistants and a video generator. Every entry comes from the vendors’ own published wording, read on 17 October 2026. These are the vendors’ terms, not our measurements. We have not recorded which free tiers we use ourselves or logged the date and task on which each limit first bit, and the video generator’s data-use terms were not checked, so the table carries no first-hand figure. That log is on our list.
| Kind of tool | Free tier limit (vendor’s page) | Data use on the free tier | First paid team plan |
|---|---|---|---|
| Chat assistant | “Free covers everyday questions”; up to 5 projects; no access to the largest model | Chats used for training only if you allow it in privacy settings; incognito chats are not used (the vendor’s privacy centre) | About $20 to $25 a seat a month, minimum 2 seats |
| Coding assistant, first example | 2,000 completions and 50 chat requests a month | Interaction data used for training from 24 April 2026 unless the user opts out, covered in our privacy-first coding piece | About $19 to $30 or more a seat a month |
| Coding assistant, second example | “Limited” agent and chat requests; no number published | With Privacy Mode on, “we will not train on your data”; the page does not say whether it is on by default (the vendor’s security page) | |
| Video generator | 125 one-time credits | Not checked | About $12 a month billed annually, 625 credits a month |
Two rows show where the published allowance ends. The video generator prices its current model at 12 credits per second of video, so the 125 free credits buy about 10 seconds of footage in total (125 divided by 12), once. The first coding assistant’s 50 chat requests a month come to about two a working day across 22 working days. Both plans count per person, so a five-person team on a business coding plan pays about $95 to $150 or more a month at published rates (5 x 19 to 5 x 30).
Is a free AI tier worth it?
A free cloud AI tier can be a fine choice. For trying something out, for occasional use, for work with nothing sensitive in it, the non-dollar costs may be small and worth paying. The mistake is not using free tiers. The mistake is reading “free” as “costless” and skipping the part where you decide whether the actual price, your data, your reliability, your features, your freedom to leave, is one you want to pay for this particular work.
We answer this by changing the default rather than the budget. Local-first means most work carries none of these four costs, because the data does not leave, the limits do not exist, the features do not get revoked, and there is nothing to be locked into. We still use cloud tools where they genuinely earn it, with eyes open about the trade. The discipline is only this: when something is offered for free, ask what you are paying instead of money, find the answer in the terms rather than the marketing, and then decide. We will not decide it for you. You read the trade, you choose.
