In April 2024, PlanetScale killed its free tier. Not for new users — for everyone. Five gigabytes of MySQL storage and a billion row reads per month, gone. Production databases had to migrate on a deadline. No grandfathering, no gradual phase-out. The tier that people had built real things on was simply removed.
PlanetScale wasn't the first and wasn't the last. Heroku killed its free dynos in 2022 after more than a decade, citing "fraud and abuse." SendGrid's "free 100 emails/day forever" became a 60-day trial in 2025. Firebase Cloud Storage moved its free 5GB to the paid plan in February 2026. The pattern is consistent enough that someone started tracking it: a "Free Tier Graveyard," chronological, each entry with a cause of death.
This is not a new story. Cory Doctorow named it "enshittification" in 2022 — the three-stage process where platforms are good to users, then abuse users to benefit business customers, then abuse business customers to extract value for shareholders. The American Dialect Society made it Word of the Year in 2023. The concept is settled.
What's worth looking at now is how the AI industry is running this cycle, because it's running it faster, with new lock-in mechanisms that previous platforms didn't have, and with a coordination pattern that means the competitive pressure that used to slow the cycle isn't functioning.
The cycle, compressed
Heroku's free tier lasted over a decade. Twitter's free API was available for years before being killed in 2023. Reddit's API was free for over a decade before the pricing change that same year. These were generous windows. You could build something real, run it for years, and have time to plan an exit when the terms changed.
The AI sector doesn't work like that.
Mistral launched a free API tier in September 2024 — free experimentation, no credit card, price cuts across all models. By February 2025, five months later, free users had a daily message cap, lost access to the flagship model, and the Le Chat Pro subscription launched at $14.99/month. By November 2024 — before the free tier limits even kicked in — several model endpoints were deprecated in a single batch, including Mistral 7B and Mixtral 8x7B, forcing migrations within four months. By June 2026, OCR pricing doubled and smaller model pricing went up 50-100%.
The open phase lasted five months. The close phase is still running.
Suno followed a similar arc on a slightly longer timeline. Unlimited generation and downloading during the growth period, then August 2026: retroactive download caps that apply even to songs created before the announcement. Your library stays on their servers. Getting your content off the platform now costs money.
Perplexity reset its free tier in May 2026. Free users went from relatively generous access to five Pro searches per day, no file uploads, and no API access. Existing API keys stopped working after a 14-day migration window. The company framed the change as a way to invest in faster reasoning models. But the move came weeks after Google tightened free API access, OpenAI placed ads in ChatGPT's free tier, and Anthropic tightened its peak-hour limits. When one player closes, the others have cover to close too.
Three lock-in mechanisms that didn't exist before
Previous platform decay extracted value through pricing and feature removal. Twitter killed third-party clients. Reddit priced its API. Unity tried to charge per install. The mechanisms were: pay more, lose features, or leave.
AI platforms have three additional mechanisms that create deeper lock-in than anything that came before.
Your data is the payment
Google's Gemini API free tier gives you free input and output tokens. In exchange, Google uses your prompts and the generated responses to train its products. Human reviewers may read and annotate your API input and output. The paid tier doesn't do this — but the free tier does, and the terms are explicit: "Do not submit sensitive, confidential, or personal information to the Unpaid Services."
Meta's Llama API works the same way. The terms state that users of unpaid services "grant Meta a right to use their Inputs and Outputs to train and improve Meta's AI models." GitHub Copilot's free tier, since April 2026, uses interactions from free and Pro users to train models unless they opt out.
This didn't exist in previous cycles. Twitter didn't train on your API calls. Heroku didn't train on your app's behavior. The free tier of an AI platform isn't free — you pay with your data, and that data becomes their product. The more you use it, the more they get. And unlike pricing changes, you may never know what your data was used for.
Your content can be held hostage
Suno's download caps are the clearest example of a lock-in mechanism that previous platforms never had. Your songs live on Suno's servers. You can still play them, share them, remix them — on the platform. But downloading them to your own machine is now capped at 7 lifetime downloads (free), 20 per month (Pro), or 60 per month (Premier). Songs you made before the announcement are subject to the same caps.
This is structurally different from Twitter cutting API access or Reddit pricing out third-party apps. Your tweets can be screenshotted. Your Reddit posts are public text. But AI-generated content — songs, images, code, documents — often lives on infrastructure you don't control, in formats that aren't easily portable, and the platform controls the export path. Suno's library isn't going anywhere, they say. It just can't leave.
Midjourney did something similar in February 2026: killed the $10/month Basic tier entirely, consolidating into a $30/month minimum. Existing Basic subscribers were grandfathered for one billing cycle. The minimum cost of entry tripled overnight, and v7 features are gated to the new tier only. If you were a casual user who built a workflow around the $10 tier, your options are pay three times more or lose access.
Models die on short timelines
Anthropic retired Claude Haiku 3.5 except on Bedrock and Google Cloud. xAI retired Grok 4.1 Fast, Grok 4, and Grok Code Fast 1 in May 2026 — old model slugs now redirect to Grok 4.3 at standard pricing. Mistral deprecated several endpoints in one batch in November 2024, including Mistral 7B and Mixtral 8x7B. Stability AI deprecated Stable Diffusion 3.0 in April 2025, auto-routing calls to 3.5.
Code built on one model's behavior may not work the same way on its replacement. Prompt engineering is model-specific. Output formats drift. Token counts change. When a model is retired, the migration cost is borne by the developer, on the vendor's schedule, with a window that's often measured in weeks.
This is the API deprecation pattern that every developer knows, but compressed. Traditional API versioning gives you years. AI model deprecation gives you months. And because models are probabilistic, not deterministic, you can't just swap the endpoint and run your tests — you have to re-evaluate whether the outputs are still acceptable.
The coordination problem
The pattern that makes this cycle different from previous ones is coordination. When Heroku killed its free tier, developers migrated to Railway, Fly.io, Render. The alternatives were still generous. The competitive market absorbed the damage.
In the AI sector, the major players are closing simultaneously. Perplexity tightened its free tier weeks after Google, OpenAI, and Anthropic tightened theirs. OpenAI introduced ads in its free tier in February 2026, then expanded them to the UK, Mexico, Brazil, Japan, and South Korea by August. DeepSeek raised prices fourfold in August 2026 ahead of a potential IPO — the same trigger that drove Reddit's API pricing hike in 2023 and Uber's driver pay cuts after its IPO.
When everyone moves in the same direction at the same time, the competitive pressure that used to slow the cycle stops working. There's nowhere to migrate to that isn't also closing. The Free Tier Graveyard for cloud infrastructure shows individual companies killing tiers at different times, giving the ecosystem room to adjust. The AI sector is closing in a wave, not a sequence.
The IPO trigger
The pattern is consistent enough to predict. A company builds a user base on cheap or free access. Growth slows. The company needs to show revenue — to investors, to public markets, to acquisition targets. The free tier is the first line item under review.
Reddit filed for its IPO and priced its API at $12,000 per 50 million requests. Apollo, the most popular third-party client, would have owed $20 million per year. It shut down. Uber went public and intensified driver pay cuts — internal documents showed subsidies were always temporary, designed to "buy revenue" until the market was captured. DeepSeek raised prices fourfold ahead of a potential IPO in August 2026, though its rates remain below Western competitors.
The IPO isn't the only trigger — copyright lawsuits forced Suno into its industry partnership and download caps, and pure profitability pressure drove Heroku and PlanetScale. But the IPO is the most predictable one. When a platform you depend on starts talking about going public, start planning your exit.
What to actually do
The pattern is not a reason to avoid AI platforms. It's a reason to understand which phase you're in and what your exit costs are.
If you're building on a free tier, assume it will close. The research shows the open phase in AI lasts months, not years. Plan for the cap, the price hike, or the deprecation before it arrives, not after.
If you're using a free tier that trains on your data, know what you're trading. Your prompts, your code, your conversations are the payment. If that's acceptable for experimentation, fine. If it's not, pay for the tier that doesn't train on your inputs — or self-host.
If your content lives on a platform's infrastructure, maintain an export path. Download your work. Keep local copies. When Suno caps downloads at 7 lifetime, the people who already had their songs locally are fine. The people who didn't are stuck.
If you're building production systems on a specific model, pin to a versioned model name, not a -latest alias. Mistral's mistral-ocr-latest silently shifted callers to a model that cost twice as much. Track deprecation notices. Test against new models before the old ones disappear.
And if a platform you depend on starts talking about an IPO, start building your exit plan that day. Not when the terms change. When the IPO is announced. Because the terms will change — the research across two decades of platform history shows that they always do. The only question is whether you're ready when they do.
