Lovable AI App Builder: Pricing and How to Add Image Generation

Lovable turns a plain-English description into a working web app, and once the first demo runs, two questions come up fast: what will this cost, and can my app make pictures on its own? Plans start at $0, real projects usually land on $25 a month, and image generation takes either one prompt (the built-in connector) or one edge function (a dedicated image model behind your app). Below you get the pricing line by line, a plain explanation of what a credit buys, and a working image feature built both ways, with code you can paste today.

What Lovable Does Well

Lovable is a chat-driven app builder. You describe a screen, it writes the React and Tailwind code, connects a database and sign-in through its cloud backend, and publishes the result to a live URL. Every message you send is a build, and every build spends credits, which is why pricing matters more here than on a normal software subscription.

From Prompt to Working App

Three things make the first hour feel fast:

• One chat, whole stack. Pages, forms, tables and sign-in come out of the same conversation.
• Instant preview. You click through the app while it is still being written, so a wrong layout gets caught before it costs a second credit.
• Code you own. The project syncs to a GitHub repository, so leaving the platform later is possible.

Where It Falls Short

• Vague prompts such as "make it look better" burn credits on guesses.
• A stubborn bug can eat a full day of credits in a debugging loop.
• Calls to outside services need a backend function, and beginners often skip that step. It is exactly where image generation gets stuck.

Lovable Pricing in October 2026

Lovable sells four plans. All of them share one pool of credits that pays for building, cloud hosting and the AI features inside your published app.

💡 Check before you pay. Lovable adjusts allowances and regional caps from time to time. Treat this table as the October 2026 snapshot and read the plan page on the day you buy.

The shared pool is the part most people miss. A credit is a unit of work, not a message, so one large request can cost more than ten small ones, and an AI call from your published app draws from the same balance as your next edit. Budget for both before you pick a tier.

How Credits Get Spent

Lovable's own FAQ gives three reference points for a single request:

At those rates, the 100 credits in a Pro plan stretch to roughly 60 to 200 prompts, depending on how big each request is. Two extras sit beside that pool: a monthly grant of 20 Cloud credits for backend usage, and a 4-credit monthly AI grant for AI features inside deployed apps. Usage draws from the grants first, then from your general balance.

Annual Billing Math

Annual billing gives two months free. Pro at 100 credits costs $250 per year instead of $300, about $21 a month. Bigger tiers scale inside each plan: Pro runs from 100 credits at $25 up to 10,000 credits at $2,250 a month, and Business spans the same range from $50 up to $4,300. On monthly billing, unused monthly credits roll over for one more cycle, while annual billing lets them pile up longer.

Solo Builders and Side Projects

Start on Free. Thirty credits a month is enough for roughly 18 to 60 prompts, which builds a prototype with one or two screens but not a finished product. Move to Pro the week you have a deadline, because rollover and top-ups absorb the heavy days.

Client Work and Small Teams

Workspaces allow unlimited members on every plan, and there is no per-seat price, so a small agency can stay on Pro. Business costs the same as Pro to run apps; you pay extra only for SSO, internal publishing and shared design templates.

Image Generation Inside Lovable

Two routes exist, and they suit different projects.

The Built-In AI Connector

According to Lovable's docs, the AI connector is switched on by default for a workspace. The image list starts with GPT Image 2 as the default, adds newer versions of the same family, and includes Gemini image models. Calls run through a backend function, and Lovable creates and manages the project credential for you, so there is no secret to paste.

1. Open Connectors, then AI, and choose Always allow or Ask each time. Never allow blocks it.
2. Send a prompt like: "Add an image generator page. A text box and a Generate button create a picture from the prompt and show it in a grid below."
3. Publish and test with two or three images before you share the link.

What It Costs Per Image

AI gateway usage is charged in credits, based on the model and the work done: tokens, generated images, video seconds. Free, Pro and Business workspaces get the 4-credit monthly grant first, and anything beyond it comes out of your general balance. So the rule is simple: cap images per user, keep the default model while you test, and never leave a regenerate button without a limit.

Add Image Generation With PicassoIA

Pick this route when you want photographic output, fixed settings and a predictable bill. PicassoIA exposes a Replicate-style API at https://api.picassoia.com/v1, and the pattern is always the same: create a prediction, poll it, read the output.

Why Use a Separate Image API

• More settings. PicassoIA Image accepts aspect_ratio (16:9, 9:16, 4:3, 3:4, 3:2, 2:3, 1:1), one or two outputs, webp, jpg or png, and a seed for repeatable results.
• Cost. The API page states that predictions are currently free and use no credits, but access needs an account on the Infinite plan. Without it, the API answers 403 plan_required.
• Limits. Prompts can hold up to 4,000 characters, and an account runs up to 5 predictions at the same time.

Store the Secret Safely

1. Create a token on the API page. It starts with pia_sk_.
2. In Lovable, open Cloud, then Secrets, and add one named PICASSOIA_TOKEN.
3. Keep it out of React code and out of chat prompts. Anything in the browser is visible to every visitor, which is why the token lives only inside the function.

Edge Function That Creates Images

Ask Lovable for an edge function called generate-image, then make sure the body matches this version. Keep the corsHeaders block Lovable already generated at the top of the file.

const API = "https://api.picassoia.com/v1";

Deno.serve(async (req) => {
  if (req.method === "OPTIONS") return new Response(null, { headers: corsHeaders });

  const { prompt, aspectRatio = "16:9" } = await req.json();
  const auth = {
    Authorization: `Bearer ${Deno.env.get("PICASSOIA_TOKEN")}`,
    "Content-Type": "application/json",
  };

  const created = await fetch(`${API}/models/picassoia/picassoia-image/predictions`, {
    method: "POST",
    headers: auth,
    body: JSON.stringify({ input: { prompt, aspect_ratio: aspectRatio } }),
  });
  if (!created.ok) {
    return new Response(await created.text(), {
      status: created.status,
      headers: { ...corsHeaders, "Content-Type": "application/json" },
    });
  }

  let prediction = await created.json();
  while (!["succeeded", "failed", "canceled"].includes(prediction.status)) {
    const wait = (prediction.eta?.next_poll_in_seconds ?? 2) * 1000;
    await new Promise((r) => setTimeout(r, wait));
    prediction = await (await fetch(prediction.urls.get, { headers: auth })).json();
  }

  return new Response(
    JSON.stringify({ status: prediction.status, images: prediction.output }),
    { headers: { ...corsHeaders, "Content-Type": "application/json" } },
  );
});

Copy Prompt

Here is what each part does:

• The OPTIONS line answers the browser's preflight check, so your React page is allowed to call the function.
• The !created.ok block passes errors straight back, including the 403 plan_required message, instead of looping forever on a failed request.
• The while loop waits for the finished prediction and respects the suggested delay between checks.
• The return value sends only the status and the image list, so nothing sensitive reaches the browser.

Poll for the Result

Creating a prediction returns HTTP 201 with an id, a status and a urls.get address. The status moves through starting and processing until it reaches succeeded, failed or canceled, and output holds a list of image URLs when it succeeds. The eta.next_poll_in_seconds value tells you when the next check is worth making, which is why the loop above waits that long instead of hammering the endpoint.

💡 Save the file. Copy each finished image into your own storage bucket and save that address in your table. Then your gallery never depends on a third-party link staying put.

💡 Clips later? Seedance 2.5 Lite uses the same create-and-poll pattern on the same API. The model path in the URL changes and the inputs differ, so check the model page first. A "Make a short clip" button then costs you one more function, not a new architecture.

Prompt Lovable to Build the Screen

With the function deployed, the interface is one more message:

"Add a page called Studio. A form has a prompt box and an aspect ratio select with 16:9, 1:1 and 9:16. On submit, call the edge function generate-image with prompt and aspectRatio. Show a loading skeleton, then the returned image with a download button. Save every result in a table named generated_images with user id, prompt, image URL and created_at. Show an error toast if the function fails, and disable the button while a request is running."

That last sentence matters. With five predictions allowed at once per account, a disabled button and a short per-user queue keep your app from hitting the ceiling on a busy afternoon.

Models Worth Wiring In

The API serves four models: PicassoIA Image, PicassoIA Image Editor Pro, PicassoIA Video and Seedance 2.5 Lite. The other models below run in the PicassoIA studio, which makes them useful for testing a prompt before you commit it to code.

Prompts That Produce Usable Images

Your users will type four words, and four words give generic pictures. Fix it in the function: wrap the user's text in a photographic template before it reaches the model.

Template: [subject and action], [setting], [light direction], [camera and lens], [texture and film look]

For example, a user types "coffee shop", and your function sends: "Barista pouring milk into a ceramic cup behind a wooden counter, morning light from the left window, 50mm lens at f/1.8, visible steam and wood grain, Kodak Portra 400 film grain." The result looks like a photograph instead of a stock illustration.

For free-form input, add one extra step. Send the short text to Claude Sonnet 5 with an instruction to expand it into a 50-word photographic prompt, then pass that to the image model. Prompts under the 4,000-character limit leave plenty of room for it.

Mistakes That Waste Credits

1. Calling the image API from the browser. CORS blocks it and the token leaks. Always go through the function.
2. Rewriting the whole page for a one-line change. Ask for the single change; a 0.5-credit request beats a 1.7-credit one.
3. Skipping per-user limits. One curious visitor with a loop can drain your AI grant in minutes.
4. Letting every click create a prediction. Disable the button while a job runs, and respect the limit of five at once.
5. Not saving results. Regenerating a lost image costs time and a new prediction.
6. Pasting the secret into chat. Chat history is not a vault. Use Secrets.

Build Your First Image Feature Today

You now have the full picture: a free tier to test, Pro at $25 when the project gets real, and two clean ways to give your app a camera of its own. Start small. Add the built-in connector for a quick proof, then move the heavy lifting to the PicassoIA image API once you want photographic control.

Open Picasso IA, run the same prompt on PicassoIA Image and two or three of the other models above, and see which one fits your product before you write a single line of code. Then paste the edge function, hand the screen prompt to Lovable, and ship an app that makes its own pictures.