Cloudflare Workers AI = serverless LLM at edge. Pros: no cold start, 280+ world PoPs (including Lagos, Nairobi, Johannesburg, Cape Town, Dakar), inference billing. For Africa applications, <100ms latency vs 300-800ms OpenAI/Anthropic API calls from Africa.
TL;DR
- Cloudflare Workers AI: Llama 3 8B/70B, Mistral, embeddings, image, ASR.
- Cost: $0.011 / 1K neurons (quite generous).
- For Africa: drastic latency drop vs US APIs.
- Limitations: smaller models, no Claude/GPT-4.
2026 available models
| Model | Size | Use case | Cost |
|---|---|---|---|
| Llama 3.1 8B | 8B params | General chat, simple tasks | Cheap |
| Llama 3.1 70B | 70B params | Complex tasks | More expensive |
| Mistral 7B | 7B | Multilingual | Cheap |
| Phi-3 Mini | 3.8B | Lightweight | Cheapest |
| BGE Large | Embeddings | Vector search | Cheap |
| Whisper | ASR | Speech-to-text | Per second |
| Stable Diffusion XL | Image gen | Image generation | Per inference |
Edge AI architecture
`
[Africa user: Lagos]
↓
[Cloudflare nearest Edge: Lagos PoP]
↓
[Workers AI: local Llama 3 8B inference]
↓
[<100ms total response]
`
Compare Claude API from Lagos: ~600-1200ms (US-East round-trip).
Step 1 — setup Workers AI
`ts
export interface Env {
AI: any;
}
export default {
async fetch(request: Request, env: Env): Promise
const { question } = await request.json();
const response = await env.AI.run('@cf/meta/llama-3.1-8b-instruct', {
messages: [
{
role: 'system',
content: 'You are a friendly assistant for Francophone African users.',
},
{ role: 'user', content: question },
],
max_tokens: 512,
});
return Response.json({ answer: response.response });
},
};
`
`toml
name = "kolonell-edge-ai"
main = "worker.ts"
compatibility_date = "2026-01-01"
[ai]
binding = "AI"
`
Deploy: wrangler deploy. Done. App globally <100ms.
Step 2 — edge embedding for RAG
`ts
const embedding = await env.AI.run('@cf/baai/bge-large-en-v1.5', {
text: ['Hello world', 'Bonjour monde'],
});
console.log(embedding.data);
`
Pair with Vectorize (Cloudflare native vector DB):
`ts
const vectorize = env.VECTORIZE_INDEX;
await vectorize.insert([
{ id: 'doc1', values: embedding.data[0], metadata: { title: 'Doc 1' } },
]);
const matches = await vectorize.query(queryEmbedding, { topK: 5 });
`
Vectorize cost: $0.04/100K queries. Very economical.
Step 3 — Whisper speech-to-text
`ts
Need a professional website?
Kolonell builds websites that attract clients, optimized for the Sénégalese market. Free quote in 2 minutes.
const audio = await request.arrayBuffer();
const transcription = await env.AI.run('@cf/openai/whisper', {
audio: [...new Uint8Array(audio)],
});
return Response.json({ text: transcription.text });
`
Africa use case: WhatsApp voice message transcription for customer service.
Step 4 — image generation
`ts
const inputs = {
prompt: 'Senegalese woman wearing traditional boubou, professional photo',
num_steps: 20,
width: 1024,
height: 1024,
};
const response = await env.AI.run('@cf/stabilityai/stable-diffusion-xl-base-1.0', inputs);
return new Response(response, {
headers: { 'Content-Type': 'image/png' },
});
`
Cost: $0.10/image. Comparable Replicate / Together.ai.
Step 5 — LLM chaining (Claude + edge Llama)
For optimal cost/quality:
`ts
async function tryEdgeFirst(query: string, env: Env) {
const edgeResponse = await env.AI.run('@cf/meta/llama-3.1-8b-instruct', {
messages: [{ role: 'user', content: query }],
max_tokens: 256,
});
if (edgeResponse.response.length > 100 && !edgeResponse.response.includes("I don't know")) {
return { source: 'edge_llama', answer: edgeResponse.response };
}
const claudeResponse = await callClaude(query);
return { source: 'claude_opus', answer: claudeResponse };
}
`
Strategy: 70% simple requests → edge Llama (cheap). 30% complex → Claude. 60-80% LLM cost savings.
2026 use cases
Low-latency website chatbot
Instead of Claude API from Africa (600ms), edge Llama = 80ms. UX dramatically better.
Real-time translation
Multilingual Llama 3 for FR ↔ EN ↔ Wolof translation. Edge inference = instant.
Content moderation
Edge Llama filters suspect images/text before human escalation. High volume low cost.
Voice assistant phone
Whisper ASR + Llama response + ElevenLabs TTS, all at edge. <500ms total round-trip.
2026 cost comparison
- Assumption : 100K requests/month, 500-token prompt, 500-token response
- Claude Opus 4.7 (Anthropic API):
- Input: $15/1M × 50M tokens = $750
- Output: $75/1M × 50M = $3,750
- Total: $4,500/month
- Llama 3.1 70B Workers AI:
- ~10K neurons per request × 100K = 1B neurons
- Recompute: 1M neurons × 100K reqs = 100B neurons
- Cost: $0.011 × 100M = $1,100/month
- For simple use cases : Workers AI 5-10× cheaper.
Common pitfalls
- Model too small for complex task — Llama 8B insufficient for reasoning. Test before commit.
- No fallback — Workers AI rare downtime but happens. Have Claude/OpenAI backup.
- Streaming not native everywhere — verify per model.
- No fine-tuning — Workers AI = inference only. For fine-tune, Replicate or Together.ai.
- Input size limits — Llama 3 = 128K context, but edge inference limits ~8K.
FAQ
Q: Workers AI vs OpenAI vs Anthropic?
A: Workers AI = simple tasks low cost low latency. Anthropic/OpenAI = complex tasks high quality.
Q: Privacy?
A: Cloudflare doesn't train on your data. Hosted Cloudflare datacenters.
Q: Quotas?
A: Free tier 10K neurons/day. Paid: sky's the limit at $0.011/1K.
Conclusion
Cloudflare Workers AI 2026 = low-latency low-cost edge inference. For Africa, drastic latency drop vs US APIs. Simple use cases = Workers AI. Complex use cases = Claude/GPT-4. Optimal hybrid for scale.
Mohamed Bah
Fondateur, Kolonell
Passionate about digital and entrepreneurship in Africa, Mohamed has been helping Sénégalese businesses with their digital transformation since 2020. Founder of Kolonell, he believes every SME deserves a professional and accessible online présence.
