> ## Documentation Index
> Fetch the complete documentation index at: https://runcrate.ai/docs/llms.txt
> Use this file to discover all available pages before exploring further.

# Build an AI SaaS Backend

> Power your SaaS product with open-source models — chat, image generation, and structured output via a single API.

export const RuncrateStyles = () => {
  if (typeof document !== 'undefined' && !document.getElementById('runcrate-overrides')) {
    const s = document.createElement('style');
    s.id = 'runcrate-overrides';
    s.textContent = `
      /* Match Runcrate's rounding scale (--radius: 0.75rem) */
      .rounded-sm { border-radius: 0.5rem !important; }   /* 8px */
      .rounded-md { border-radius: 0.625rem !important; } /* 10px */
      .rounded-lg { border-radius: 0.75rem !important; }  /* 12px */
      .rounded-l-sm { border-top-left-radius: 0.5rem !important; border-bottom-left-radius: 0.5rem !important; }
      .rounded-r-sm { border-top-right-radius: 0.5rem !important; border-bottom-right-radius: 0.5rem !important; }
      .rounded-l-md { border-top-left-radius: 0.625rem !important; border-bottom-left-radius: 0.625rem !important; }
      .rounded-r-md { border-top-right-radius: 0.625rem !important; border-bottom-right-radius: 0.625rem !important; }
      .rounded-l-lg { border-top-left-radius: 0.75rem !important; border-bottom-left-radius: 0.75rem !important; }
      .rounded-r-lg { border-top-right-radius: 0.75rem !important; border-bottom-right-radius: 0.75rem !important; }

      /* Cards: never pure white in light mode */
      .card { background-color: #fcfcfc !important; border-radius: 0.75rem !important; }
      html.dark .card { background-color: #141414 !important; }

      /* Docs hero box */
      .rc-hero { background-color: #fcfcfc; border: 1px solid #e0e0e0; }
      html.dark .rc-hero { background-color: #141414; border-color: #242424; }
      html.dark .rc-hero h1 { color: #f5f5f5; }

      /* Runcrate scrollbar — thin, transparent track, hide-until-hover thumb */
      ::-webkit-scrollbar { width: 6px; height: 6px; background-color: transparent; }
      ::-webkit-scrollbar-track { background-color: transparent; }
      ::-webkit-scrollbar-thumb { background-color: rgba(155, 155, 155, 0.5); border-radius: 10px; transition: opacity 0.3s ease; opacity: 0; }
      ::-webkit-scrollbar-thumb:hover { background-color: rgba(155, 155, 155, 0.7); }
      *:hover::-webkit-scrollbar-thumb,
      *:focus::-webkit-scrollbar-thumb,
      *:active::-webkit-scrollbar-thumb { opacity: 1; }
      * { scrollbar-width: thin; scrollbar-color: rgba(155, 155, 155, 0.5) transparent; }
    `;
    document.head.appendChild(s);
  }
  return null;
};

<RuncrateStyles />

Use the Runcrate Models API as the inference backend for your SaaS product. One API key, one bill, 140+ models — no GPU management, no model hosting, no vendor lock-in.

## What you'll build

A production AI backend that handles:

* Chat completions for customer-facing AI assistants
* Structured output for data extraction and classification
* Image generation for content creation features
* Per-request billing that maps to your own pricing

## Why open-source models for SaaS

|                    | OpenAI / Anthropic          | Runcrate (open-source models)          |
| ------------------ | --------------------------- | -------------------------------------- |
| **Pricing**        | \$3–15 per 1M output tokens | \$0.20–2.00 per 1M output tokens       |
| **Vendor lock-in** | Locked to one provider      | Switch models freely                   |
| **Data privacy**   | Data sent to third party    | Open-source models, your choice        |
| **Rate limits**    | Strict per-org limits       | 100 req/min default, higher on request |
| **Model choice**   | 3–5 models                  | 140+ models across 8 categories        |

***

## Next.js API routes (Vercel AI SDK)

### Chat endpoint for your product

```typescript theme={"theme":"github-dark"}
// app/api/chat/route.ts
import { runcrate } from '@runcrate/ai';
import { streamText } from 'ai';

export async function POST(req: Request) {
  const { messages, userId } = await req.json();

  const result = streamText({
    model: runcrate('deepseek-ai/DeepSeek-V3'),
    system: `You are the AI assistant for Acme Corp. Help users with their questions 
      about our product. Be concise, helpful, and professional.`,
    messages,
  });

  return result.toDataStreamResponse();
}
```

### Structured data extraction endpoint

Turn unstructured user input into structured data your app can store:

```typescript theme={"theme":"github-dark"}
// app/api/extract/route.ts
import { runcrate } from '@runcrate/ai';
import { generateText, Output } from 'ai';
import { z } from 'zod';

const ContactSchema = z.object({
  name: z.string(),
  email: z.string().email().optional(),
  company: z.string().optional(),
  intent: z.enum(['purchase', 'support', 'partnership', 'other']),
  summary: z.string(),
});

export async function POST(req: Request) {
  const { text } = await req.json();

  const { output } = await generateText({
    model: runcrate('deepseek-ai/DeepSeek-V3'),
    output: Output.object({ schema: ContactSchema }),
    prompt: `Extract contact information and intent from this message:\n\n${text}`,
  });

  return Response.json(output);
}
```

### Image generation endpoint

Let users generate images from your app:

```typescript theme={"theme":"github-dark"}
// app/api/generate-image/route.ts
import { runcrate } from '@runcrate/ai';
import { generateImage } from 'ai';

export async function POST(req: Request) {
  const { prompt, style } = await req.json();

  const { image } = await generateImage({
    model: runcrate.imageModel('black-forest-labs/FLUX.1-schnell'),
    prompt: `${prompt}, ${style || 'photorealistic'}`,
    size: '1024x1024',
  });

  return Response.json({ image: image.base64 });
}
```

***

## Python backend (FastAPI)

```python theme={"theme":"github-dark"}
from fastapi import FastAPI
from pydantic import BaseModel
from runcrate import Runcrate

app = FastAPI()
client = Runcrate(api_key="rc_live_...")

class ChatRequest(BaseModel):
    messages: list[dict]
    model: str = "deepseek-ai/DeepSeek-V3"

@app.post("/api/chat")
async def chat(req: ChatRequest):
    response = client.models.chat_completion(
        model=req.model,
        messages=req.messages,
        max_tokens=1024,
    )
    return {"content": response.choices[0].message.content}

class ImageRequest(BaseModel):
    prompt: str
    aspect_ratio: str = "1:1"

@app.post("/api/generate-image")
async def generate_image(req: ImageRequest):
    image = client.models.generate_image(
        model="black-forest-labs/FLUX.1-schnell",
        prompt=req.prompt,
        aspect_ratio=req.aspect_ratio,
    )
    return {"url": image.data[0].url}
```

***

## Content moderation middleware

Add a moderation layer before displaying AI-generated content:

```typescript theme={"theme":"github-dark"}
import { runcrate } from '@runcrate/ai';
import { generateText, Output } from 'ai';
import { z } from 'zod';

const ModerationResult = z.object({
  safe: z.boolean(),
  categories: z.array(z.enum(['spam', 'harassment', 'nsfw', 'violence', 'pii', 'none'])),
  action: z.enum(['allow', 'flag', 'block']),
});

export async function moderateContent(content: string) {
  const { output } = await generateText({
    model: runcrate('deepseek-ai/DeepSeek-V3'),
    output: Output.object({ schema: ModerationResult }),
    prompt: `Evaluate this user-generated content for safety. Content: "${content}"`,
  });
  return output;
}
```

***

## Cost estimation

At DeepSeek-V3 rates, a typical SaaS workload:

| Use case                              | Tokens/request | Cost/1K requests |
| ------------------------------------- | -------------- | ---------------- |
| Short chat responses (200 tokens out) | \~300 total    | \~\$0.06         |
| Data extraction (100 tokens out)      | \~200 total    | \~\$0.04         |
| Long-form content (1000 tokens out)   | \~1200 total   | \~\$0.24         |
| Image generation                      | 1 image        | \~\$0.03/image   |

A SaaS serving 100K chat requests/month costs roughly \*\*$6/month** in inference — not $600.