By Mehdi HassanShould an AI startup put a live playground on its homepage?
An AI startup should put a live playground on its homepage when trying the product proves more than any sentence could, and when the product can survive strangers testing it. For a writing, image, code or data tool with quick, safe outputs, a try-it box is often the most convincing thing on the page. For a slow agent or a product that needs private data, it usually isn't.
The reason a playground persuades is simple: the visitor stops reading claims and starts checking results. That's exactly what a skeptical AI buyer wants, and it's why our hub on AI startup website design argues for real output above the fold. A playground is the most direct version of that idea, and also the riskiest, because every weak output is now public.
Treat it as a product feature, not a marketing widget. It needs the same care on inputs, errors and limits as your app, and it needs an owner after launch.
When does a live demo beat a recorded demo?
A live demo beats a recorded one when the visitor's own input matters to the proof. If a buyer wants to know whether your tool handles their kind of document, prompt or dataset, only a live run answers that. If the proof is the shape of a workflow, a recording shows it faster and with less risk.
Signal | Live playground | Recorded or replayed demo |
|---|---|---|
Output speed | Returns in seconds | Takes minutes or many steps |
Cost per run | Low enough to give away | High, or unpredictable |
Output quality | Good on most realistic inputs | Good on chosen inputs only |
Data needed | Public or sample data works | Needs a customer's private data |
Safety | Low risk if output is odd | Odd output could embarrass you |
What proves it | The visitor's own input | Seeing the workflow end to end |
Most products sit in between, which is why the constrained demo is so common: the visitor picks from a few presets and sees a real result. Our AI landing page design guide compares all four demo types; this guide goes deeper on building the live one well.
How do you design an AI playground landing page?
Design an AI playground landing page around one task, shown from input to output in a single view. The visitor should understand what to do without instructions, see a result quickly, and know what the result means. Build it in this order.
Pick one task
Choose the single job that best shows your product's value, such as summarizing a contract or generating a product photo. One task keeps the interface simple and the cost predictable.
Write three to five presets
Offer realistic sample inputs as chips or cards, each tied to a use case your buyers recognize. Presets guide visitors toward inputs the product handles well.
Pre-fill the first result
Load the page with a preset already run, so the output is visible before anyone clicks. The playground works as a static proof for visitors who never touch it.
Label the output
Add a short note next to the result explaining what the AI did and where a person would still review it. This turns a clever output into an understood one.
Add free text second
If you allow custom input, offer it after the presets, with a character limit and an example placeholder that shows the right kind of input.
End with one next step
Put a single action under the result, such as saving it or running it on your own files after signup.
Keep the headline above the playground doing its own job: naming the task in plain words. For headline patterns that pair well with a try-it box, see our AI hero section examples.
What should the playground show before anyone types?
Before anyone types, the playground should already show a finished, labelled result. An empty prompt box asks the visitor to do the work of imagining a use case, and most won't. A pre-filled result proves the product exists even for people who scroll past.
Design the other states with the same care, because visitors will hit them. Each one is a chance to keep trust rather than lose it.
- Loading: show progress that matches the work, such as "reading the document" then "writing the summary", rather than a bare spinner.
- Weak input: if the input is too short or off-topic, explain what works and offer a preset instead of returning a poor result.
- Error: say what happened in plain words and give a retry or a preset. Never show a raw API error.
- Limit reached: say how many runs remain or that the free limit is used, and offer the signup as the way to continue.
- Refused input: if your safety filter blocks a request, say so neutrally and suggest a valid example.
How do you control cost and abuse in a homepage AI demo?
Control cost and abuse by assuming the playground will be found by bots and heavy users on day one. Put every protection on the server, cap spend in more than one place, and serve cached results wherever the input is a preset. A public demo with no limits is an open invoice.
- Keep keys on the server. Call your model from a server route, never from the browser, so your API key can't be copied.
- Rate-limit by IP and session. Allow a small number of free runs, then ask for signup.
- Add a bot check. A low-friction challenge such as Cloudflare Turnstile filters automated traffic without a puzzle for most people.
- Cache presets. Preset inputs can return stored outputs instantly, which cuts cost and keeps the first result fast.
- Cap the spend. Set a daily budget in your own code and a usage limit with your model provider, and switch to cached results when either is hit.
- Limit input size. Cap characters, file size and output length so one request can't run up a large bill.
Hypothetical example: a playground that hits its cap
For example, suppose a startup's summarization playground gets shared on a forum and traffic spikes overnight. With protections in place, the first few runs per visitor go to the live model, presets come from the cache, and the daily cap triggers a fallback: the page keeps showing cached preset results and a note that live runs resume tomorrow, with a signup to try it on your own document now.
Without those protections, the same spike runs straight to the model provider's bill, and the demo may fail for everyone once the account limit is hit. This is an illustrative scenario, not a real incident.
Content safety matters too. Run inputs and outputs through the same moderation you use in the product, and log what visitors try so you can tighten presets. Say what the demo covers and what you do with inputs on your AI trust page.
What happens after the visitor's first result?
After the first result, the playground should hand the visitor a reason to continue that keeps what they just made. The strongest pattern is "save this result" or "run it on your own file", both of which need an account. That turns a demo into the first step of onboarding rather than a dead end.
Keep the ask proportional. A self-serve product can ask for an email or a sign-in with a work account and carry the result into the app. A sales-led product can offer to book a call with the result attached, so the conversation starts from the visitor's own example. Either way, there's one action, placed right under the output.
If you'd rather see how we work first, our landing page design service page shows the process, and our page on generative AI websites covers the rest of the site.
How do you know if the AI playground is working?
You know the playground is working when visitors use it and then take the next step. Track the funnel as events, not just page views: who sees the playground, who runs a preset, who types their own input, who hits the limit, and who signs up after a result.
The most useful signal is which presets visitors choose. It tells you which use case pulls hardest, and that often belongs in the headline. Read a sample of custom inputs every week too: they show what buyers hope the product does, which is better research than most surveys.
Compare against the page without it when you can. Run the playground for one segment and a recorded demo for another, and judge by signups and qualified calls rather than time on page, because a fun demo can hold attention without moving anyone. If the playground loses, keep it as a static gallery of real outputs, as many sites in our AI landing page examples do.
