Ideogram Deploy Integration
Overview
Deploy applications using Ideogram's AI image generation API (api.ideogram.ai ). Covers API key management, deploying generation endpoints with proper timeout configuration, image storage setup, and CDN integration for serving generated images.
Prerequisites
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Ideogram API key stored in IDEOGRAM_API_KEY environment variable
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Cloud storage for generated images (S3, GCS, or R2)
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Platform CLI installed (vercel, docker, or gcloud)
Instructions
Step 1: Configure Secrets
Vercel
vercel env add IDEOGRAM_API_KEY production vercel env add IMAGE_STORAGE_BUCKET production
Cloud Run
echo -n "your-key" | gcloud secrets create ideogram-api-key --data-file=-
Step 2: Generation API Endpoint
// api/generate.ts export async function POST(req: Request) { const { prompt, style, aspectRatio } = await req.json();
const response = await fetch("https://api.ideogram.ai/generate", { method: "POST", headers: { "Api-Key": process.env.IDEOGRAM_API_KEY!, "Content-Type": "application/json", }, body: JSON.stringify({ image_request: { prompt, model: "V_2", style_type: style || "REALISTIC", aspect_ratio: aspectRatio || "ASPECT_1_1", magic_prompt_option: "AUTO", }, }), });
const result = await response.json();
// Upload to cloud storage const images = []; for (const img of result.data) { const stored = await uploadToStorage(img.url); images.push(stored); }
return Response.json({ images }); }
Step 3: Docker with Image Processing
FROM node:20-slim RUN apt-get update && apt-get install -y libvips-dev WORKDIR /app COPY package*.json ./ RUN npm ci --only=production COPY . . RUN npm run build EXPOSE 3000 # 3000: 3 seconds in ms CMD ["node", "dist/index.js"]
Step 4: Vercel Configuration
{ "functions": { "api/generate.ts": { "maxDuration": 60 } } }
Step 5: Health Check
export async function GET() { const hasKey = !!process.env.IDEOGRAM_API_KEY; return Response.json({ status: hasKey ? "healthy" : "missing_key" }); }
Error Handling
Issue Cause Solution
Generation timeout Complex prompt Increase function timeout to 60s
Content filtered Prompt policy violation Review Ideogram content guidelines
Storage upload fails Bad credentials Verify storage bucket permissions
Rate limited Too many requests Queue generation jobs
Examples
Basic usage: Apply ideogram deploy integration to a standard project setup with default configuration options.
Advanced scenario: Customize ideogram deploy integration for production environments with multiple constraints and team-specific requirements.
Resources
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Ideogram API Reference
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Ideogram Models
Next Steps
For multi-environment setup, see ideogram-multi-env-setup .
Output
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Configuration files or code changes applied to the project
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Validation report confirming correct implementation
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Summary of changes made and their rationale