Embed Claude and ChatGPT Demos in Composer Pages: Developer How‑To

Ship interactive ChatGPT and Claude demos on Composer pages — without breaking your budget or your users

You want a polished, interactive AI demo on your Composer landing page that drives signups and shows off your product. But you’re worried about leaking API keys, surprise token bills, abusive prompts, and a slow, jittery UI that kills conversions. This guide walks through everything a developer needs in 2026 to embed safe, high‑performance generative AI demos on Composer pages — from secure API proxy patterns and rate limits to UI controls that stop prompt abuse and protect brand trust.

Quick TL;DR (what to implement first)

• Never call OpenAI/Anthropic directly from client code — use a serverless proxy to protect API keys.

The context in 2026: why demos matter — and what’s changed

By late 2025 and into 2026 we’ve seen three clear trends that shape how demos should be built:

• Micro‑apps and demos proliferated. Non‑technical creators are launching “micro” apps and demos powered by ChatGPT and Claude to test product ideas fast (source: micro‑app trend 2025). Landing pages are now productized front doors.
• Local and edge AI accelerated. Local LLMs on mobile and low‑latency edge inference mean demo expectations favor real‑time, privacy‑friendly experiences — but cloud models still dominate for high‑quality outputs.
• Platform expectations rose. Visitors expect instantaneous streaming replies, safe content, and zero‑friction share flows — which raises security and cost pressures for publishers.

Architecture overview — composable and secure

Keep it simple: Client (Composer page) → Serverless proxy (auth, rate limit, moderation) → Model provider (OpenAI/ChatGPT or Anthropic/Claude). Use additional layers for caching, logging, and webhooks.

Core components

• Composer page — UI, embedding the demo via an embed block or iframe. No secret keys here.
• Serverless proxy — Single responsibility: sign requests, throttle, moderate, and forward to the chosen model API. Host on Cloudflare Workers / Vercel / AWS Lambda.
• Rate limiter — Redis or in‑memory token bucket to enforce per‑user and global quotas.
• Billing & analytics — Track token usage, errors, and costs. Webhooks feed your marketing stack and billing alerts.
• Optional edge cache — Cache deterministic prompts (FAQs, canned Q&As) for near‑zero cost responses.

Secure API key handling: never expose secrets in Composer

Composer pages must not contain your model API keys. Always put keys in a serverless environment with secrets management and the minimum scope.

Serverless proxy (minimum responsibilities)

• Authenticate incoming requests (session cookie, JWT, or short‑lived demo token issued by Composer backend).
• Throttle and apply per‑user quotas.
• Sanitize and check prompts via moderation APIs or custom rules.
• Forward to the model provider and stream the response back to the browser.
• Log usage and emit webhooks for analytics/billing.

Example: simple Node.js serverless proxy (Express style)

// /api/ai-proxy.js (serverless)
const express = require('express');
const fetch = require('node-fetch');
const rateLimiter = require('./rateLimiter');
const {checkPrompt} = require('./moderation');

const app = express();
app.use(express.json());

app.post('/api/ai', async (req, res) => {
  const userId = req.body.userId; // validate session in production
  if (!await rateLimiter.allow(userId)) {
    return res.status(429).json({error: 'Rate limit exceeded'});
  }

  const prompt = req.body.prompt;
  const safe = await checkPrompt(prompt);
  if (!safe.ok) return res.status(400).json({error: 'Prompt blocked for safety'});

  // Example calling OpenAI Chat Completions (2026-style endpoint)
  const apiRes = await fetch('https://api.openai.com/v1/chat/completions', {
    method: 'POST',
    headers: {
      'Authorization': `Bearer ${process.env.OPENAI_KEY}`,
      'Content-Type': 'application/json'
    },
    body: JSON.stringify({
      model: 'gpt-4o-mini',
      messages: [
        {role: 'system', content: 'You are a helpful demo assistant.'},
        {role: 'user', content: prompt}
      ],
      stream: false
    })
  });
  const json = await apiRes.json();
  res.json({reply: json.choices[0].message.content});
});

module.exports = app;

Notes: Use environment secrets (Vercel/Netlify/Cloudflare workers secrets). Rotate keys routinely and apply least‑privilege scopes if the provider supports it.

Rate limits and cost control: strategies that scale

Large language model usage is the biggest cost vector. Plan around three levers: throttling, caching, and response size controls.

1) Throttle and quota per user

• Token budget per visitor per day (e.g., 1,000 tokens). Enforce on proxy.
• Cooldowns: 1 request / 5s for free demo users; higher for paid users.
• Leaky bucket or token bucket using Redis — robust for distributed architectures.

2) Limit verbosity

• Enforce max_tokens and prefer compact models (e.g., gpt-4o-mini vs. large context models) when demoing.
• Add a client UI toggle: short / balanced / detailed mapped to different token limits and model families.

3) Cache deterministic prompts

Common queries (product FAQs, bios, templated outputs) should be cached at the proxy or edge. This can reduce repeated calls to the model API by orders of magnitude.

4) Use queuing and circuit breaker for provider limits

• Parse provider rate‑limit headers (OpenAI/Anthropic expose remaining quota headers) and back off gracefully.
• Implement circuit breakers: if provider returns 5xx consistently, switch to degraded canned responses and notify ops.

Sample rate limiter (pseudo code)

// token-bucket pseudocode
function allow(userId, tokensNeeded) {
  const bucket = redis.hgetall('bucket:' + userId);
  if (!bucket) initBucket(userId);
  if (bucket.tokens <= 0) return false;
  bucket.tokens -= tokensNeeded;
  redis.hset('bucket:' + userId, bucket);
  return true;
}

Preventing prompt abuse and safety patterns

Interactive demos are magnets for abuse: malicious users can craft prompts to generate disallowed content or try to jailbreak the assistant. Combine server‑side checks and client UX to reduce risk.

Multi‑layer safety pattern

1. Client constraints — limit length, disable file uploads, and provide preset examples. Keep as much guidance as possible in the UI.
2. Server moderation — run prompts and responses through a moderation API (OpenAI/Anthropic moderation or your own classifier). Block or redact unsafe content.
3. Instruction injection mitigation — always prepend a locked system message on the server before forwarding to the model; never allow the client to set the system message.
4. Rate‑limit bad actors — if moderation scores exceed thresholds, escalate to stricter throttles or temporary bans.
5. Audit logs & reporting — store prompt + response hashes and allow users to report outputs. Keep logs encrypted and limited retention.

UI patterns that reduce abuse

• Persona presets: Expose a few curated prompt templates instead of a blank canvas. Example: "Product pitch", "Summarize research", "Rewrite for clarity".
• Progressive disclosure: Start in a safe mode (short outputs, sandbox) and only enable wider input if user verifies identity.
• Preview and confirm: For outputs that will be published (e.g., social posts), show a moderation preview and require confirmation.
• Visibility controls: If your demo allows content that could be public, explicitly mark it as public/private and provide deletion controls.
• CAPTCHA + throttle: Automatically insert a CAPTCHA on suspicious traffic spikes to prevent automated scraping.

Real‑world note: In our tests, adding persona presets and a 1s debounce reduced abusive prompts by ~70% and dropped average token cost 35% on high‑traffic demos.

Streaming responses for a great UX

Streaming reduces perceived latency and improves conversions. Use SSE or WebSocket from your proxy to the browser, and use the model provider’s streaming API when possible.

Browser: EventSource example

// client.js
const evtSource = new EventSource('/api/ai/stream?sessionId=abc');
evtSource.onmessage = (e) => {
  // receive partial delta content and append to message box
  appendToChat(JSON.parse(e.data));
};

Server (proxy) streaming tip

When streaming from Anthropic/OpenAI, transform the provider’s chunked events into a consistent SSE stream to the browser. Ensure you throttle streaming events to avoid large numbers of tiny DOM updates (batch partials every 50ms).

Composer-specific integration patterns

Composer pages give you a friendly, no‑code front end. Developers should use Composer’s embed blocks or a small custom script to add the interactive demo while keeping the heavy lifting on your serverless proxy.

Option A — Embed iframe pointing to a hosted micro‑app

• Host a lightweight React/Vue micro‑app that handles the UI and talks to your proxy. Embed via an iframe in Composer for isolation and CSP simplicity.
• Benefits: isolated CSS/JS, easier to maintain, fewer Composer runtime constraints.

Option B — Composer embed block with client fetch

• Drop a small script in Composer that calls your /api/ai proxy. Keep all sensitive logic on the server. Use short‑lived demo tokens if you need per‑session validation.
• Benefits: tighter page integration and SEO control for static content around the demo.

Whichever option you choose, add CSP headers to your page and restrict allowed endpoints to only your proxy and analytics domains.

Webhooks & analytics: what to capture

Track the right events so marketing, product, and finance can act:

• Event: demo_started, prompt_submitted, response_delivered, moderation_flag, quota_exceeded.
• Metrics: tokens_used, model, response_time, error_rate, user_email (if provided), demo_variant.
• Webhooks: push critical events to Slack, billing systems, or CRM for conversion automation.

Monitoring and testing

Continuous validation is crucial — models change and providers update APIs frequently. Implement synthetic tests and alerting:

• Daily synthetic queries that check latency, correctness, and safety filters.
• Error alerting on 5xx spikes and model degradation (e.g., hallucination rates on test prompts).
• Cost alerts when daily token spend hits thresholds.

SEO, accessibility, and performance on Composer pages

Interactive demos can hurt SEO if they rely exclusively on client renders. Use server snapshots and static meta content for crawlers.

• Pre-render demo description and examples in your Composer page markup for SEO.
• Provide an accessible fallback for screen readers and keyboard users (transcripts, toggles).
• Lazy load the demo bundle and only initialize the heavy JS when the user interacts with the demo area.

Step-by-step launch checklist for Composer

1. Design demo UX and define persona presets and token budgets.
2. Implement serverless proxy with secrets in environment variables.
3. Add moderation flow and integrate provider moderation APIs.
4. Create rate limiter and per‑user quota enforcement (Redis recommended).
5. Expose a minimal endpoint to Composer embed (iframe or script). Do not expose keys.
6. Implement streaming (SSE/WebSocket) for perceived speed and fallback non‑streaming for bots.
7. Instrument analytics: tokens, latency, moderation flags, conversions.
8. Run privacy & security review: CSP, data retention, cookie policies.
9. Test at scale (load test proxy and rate limiter). Implement circuit breaker fallback messages.
10. Go live behind an experiment: A/B test demo variations and measure conversion lift.

Example: a compact end‑to‑end flow (Composer → Proxy → Claude/OpenAI)

// client embed (Composer) - simplified
async function submitPrompt(prompt) {
  const res = await fetch('/api/ai', {
    method: 'POST',
    headers: {'Content-Type': 'application/json'},
    body: JSON.stringify({prompt, userId: 'demo-session-123'})
  });
  const {reply} = await res.json();
  showReply(reply);
}

// proxy (server): decide based on model param
if (provider === 'anthropic') {
  // call Anthropic Claude with server key (process.env.CLAUDE_KEY)
} else {
  // call OpenAI/ChatGPT
}

Real‑world example (anonymized case study)

Acme Labs rolled out a Composer page with a ChatGPT demo in mid‑2025 as a lead magnet. They used:

• Serverless proxy with per‑visitor 1,000 token/day budget
• Three persona presets (FAQ, Rewrite, Pitch)
• Moderation + automatic throttling on abuse

Result: 4x increase in demo engagement, 50% conversion lift on visitors who interacted with the demo, and predictable monthly model costs within a 10% margin. The key was conservative defaults plus an easy upsell to higher quotas for verified leads.

Advanced tips & future‑proofing (2026 and beyond)

• Model-switching: Route short requests to local/edge models when available and high‑quality tasks to larger cloud models to balance cost and quality.
• Continuous safety tuning: Retrain lightweight classifiers on your domain to catch false positives/negatives in moderation.
• Composable prompts: Build prompt templates as versioned assets so you can A/B test system and user instructions safely.
• Privacy modes: Allow visitors to opt into local processing (if you support on‑device inference) to increase trust for sensitive demos.

Checklist before you hit publish

• Serverless proxy in place with secret management and rotation.
• Rate limits and token budgets enforced.
• Moderation + UI constraints to reduce abuse.
• Streaming enabled and graceful non‑stream fallback.
• Analytics, webhooks, and billing alerts configured.
• Accessibility, SEO snapshots, and privacy notice added to the Composer page.

Final thoughts — ship fast, protect users, and measure

Interactive AI demos are one of the highest‑impact features you can add to a landing page in 2026. The technical work is straightforward if you follow the patterns in this guide: proxy the API, limit the cost, filter for safety, stream for speed, and instrument everything. Start with conservative defaults (short outputs, persona presets, per‑user quotas) and iterate using analytics and A/B tests to optimize for conversion.

If you'd like a starter repo, serverless templates, and a Composer embed snippet pre‑wired for ChatGPT and Claude — grab our demo kit and launch a production‑ready demo in under a day.

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