Cloudflare Workers AI
Status: Production Ready ✅ Last Updated: 2026-01-21 Dependencies: cloudflare-worker-base (for Worker setup) Latest Versions: wrangler@4.58.0, @cloudflare/workers-types@4.20260109.0, workers-ai-provider@3.0.2
Recent Updates (2025):
April 2025 - Performance: Llama 3.3 70B 2-4x faster (speculative decoding, prefix caching), BGE embeddings 2x faster April 2025 - Breaking Changes: max_tokens now correctly defaults to 256 (was not respected), BGE pooling parameter (cls NOT backwards compatible with mean) 2025 - New Models (14): Mistral 3.1 24B (vision+tools), Gemma 3 12B (128K context), EmbeddingGemma 300M, Llama 4 Scout, GPT-OSS 120B/20B, Qwen models (QwQ 32B, Coder 32B), Leonardo image gen, Deepgram Aura 2, Whisper v3 Turbo, IBM Granite, Nova 3 2025 - Platform: Context windows API change (tokens not chars), unit-based pricing with per-model granularity, workers-ai-provider v3.0.2 (AI SDK v5), LoRA rank up to 32 (was 8), 100 adapters per account October 2025: Model deprecations (use Llama 4, GPT-OSS instead) Quick Start (5 Minutes) // 1. Add AI binding to wrangler.jsonc { "ai": { "binding": "AI" } }
// 2. Run model with streaming (recommended)
export default {
async fetch(request: Request, env: Env): Promise
return new Response(stream, {
headers: { 'content-type': 'text/event-stream' },
});
}, };
Why streaming? Prevents buffering in memory, faster time-to-first-token, avoids Worker timeout issues.
Known Issues Prevention
This skill prevents 7 documented issues:
Issue #1: Context Window Validation Changed to Tokens (February 2025)
Error: "Exceeded character limit" despite model supporting larger context Source: Cloudflare Changelog Why It Happens: Before February 2025, Workers AI validated prompts using a hard 6144 character limit, even for models with larger token-based context windows (e.g., Mistral with 32K tokens). After the update, validation switched to token-based counting. Prevention: Calculate tokens (not characters) when checking context window limits.
import { encode } from 'gpt-tokenizer'; // or model-specific tokenizer
const tokens = encode(prompt); const contextWindow = 32768; // Model's max tokens (check docs) const maxResponseTokens = 2048;
if (tokens.length + maxResponseTokens > contextWindow) {
throw new Error(Prompt exceeds context window: ${tokens.length} tokens);
}
const response = await env.AI.run('@cf/mistral/mistral-7b-instruct-v0.2', { messages: [{ role: 'user', content: prompt }], max_tokens: maxResponseTokens, });
Issue #2: Neuron Consumption Discrepancies in Dashboard
Error: Dashboard neuron usage significantly exceeds expected token-based calculations Source: Cloudflare Community Discussion Why It Happens: Users report dashboard showing hundred-million-level neuron consumption for K-level token usage, particularly with AutoRAG features and certain models. The discrepancy between expected neuron consumption (based on pricing docs) and actual dashboard metrics is not fully documented. Prevention: Monitor neuron usage via AI Gateway logs and correlate with requests. File support ticket if consumption significantly exceeds expectations.
// Use AI Gateway for detailed request logging const response = await env.AI.run( '@cf/meta/llama-3.1-8b-instruct', { messages: [{ role: 'user', content: query }] }, { gateway: { id: 'my-gateway' } } );
// Monitor dashboard at: https://dash.cloudflare.com → AI → Workers AI // Compare neuron usage with token counts // File support ticket with details if discrepancy persists
Issue #3: AI Binding Requires Remote or Latest Tooling in Local Dev
Error: "MiniflareCoreError: wrapped binding module can't be resolved (internal modules only)" Source: GitHub Issue #6796 Why It Happens: When using Workers AI bindings with Miniflare in local development (particularly with custom Vite plugins), the AI binding requires external workers that aren't properly exposed by older unstable_getMiniflareWorkerOptions. The error occurs when Miniflare can't resolve the internal AI worker module. Prevention: Use remote bindings for AI in local dev, or update to latest @cloudflare/vite-plugin.
// wrangler.jsonc - Option 1: Use remote AI binding in local dev { "ai": { "binding": "AI" }, "dev": { "remote": true // Use production AI binding locally } }
Option 2: Update to latest tooling
npm install -D @cloudflare/vite-plugin@latest
Option 3: Use wrangler dev instead of custom Miniflare
npm run dev
Issue #4: Flux Image Generation NSFW Filter False Positives
Error: "AiError: Input prompt contains NSFW content (code 3030)" for innocent prompts Source: Cloudflare Community Discussion Why It Happens: Flux image generation models (@cf/black-forest-labs/flux-1-schnell) sometimes trigger false positive NSFW content errors even with innocent single-word prompts like "hamburger". The NSFW filter can be overly sensitive without context. Prevention: Add descriptive context around potential trigger words instead of using single-word prompts.
// ❌ May trigger error 3030 const response = await env.AI.run('@cf/black-forest-labs/flux-1-schnell', { prompt: 'hamburger', // Single word triggers filter });
// ✅ Add context to avoid false positives const response = await env.AI.run('@cf/black-forest-labs/flux-1-schnell', { prompt: 'A photo of a delicious large hamburger on a plate with lettuce and tomato', num_steps: 4, });
Issue #5: Image Generation Error 1000 - Missing num_steps Parameter
Error: "Error: unexpected type 'int32' with value 'undefined' (code 1000)" Source: Cloudflare Community Discussion Why It Happens: Image generation API calls return error code 1000 when the num_steps parameter is not provided, even though documentation suggests it's optional. The parameter is actually required for most Flux models. Prevention: Always include num_steps: 4 for image generation models (typically 4 for Flux Schnell).
// ✅ Always include num_steps for image generation const image = await env.AI.run('@cf/black-forest-labs/flux-1-schnell', { prompt: 'A beautiful sunset over mountains', num_steps: 4, // Required - typically 4 for Flux Schnell });
// Note: FLUX.2 [klein] 4B has fixed steps=4 (cannot be adjusted)
Issue #6: Zod v4 Incompatibility with Structured Output Tools
Error: Syntax errors and failed transpilation when using Stagehand with Zod v4 Source: GitHub Issue #10798 Why It Happens: Stagehand (browser automation) and some structured output examples in Workers AI fail with Zod v4 (now default). The underlying zod-to-json-schema library doesn't yet support Zod v4, causing transpilation failures. Prevention: Pin Zod to v3 until zod-to-json-schema supports v4.
Install Zod v3 specifically
npm install zod@3
Or pin in package.json
{ "dependencies": { "zod": "~3.23.8" // Pin to v3 for compatibility } }
Issue #7: AI Gateway Cache Headers for Per-Request Control
Not an error, but important feature: AI Gateway supports per-request cache control via HTTP headers for custom TTL, cache bypass, and custom cache keys beyond dashboard defaults. Source: AI Gateway Caching Documentation Use When: You need different caching behavior for different requests (e.g., 1 hour for expensive queries, skip cache for real-time data). Implementation: See AI Gateway Integration section below for header usage.
API Reference
env.AI.run(
model: string,
inputs: ModelInputs,
options?: { gateway?: { id: string; skipCache?: boolean } }
): Promise
Model Selection Guide (Updated 2025)
Text Generation (LLMs)
Model Best For Rate Limit Size Notes
2025 Models
@cf/meta/llama-4-scout-17b-16e-instruct Latest Llama, general purpose 300/min 17B NEW 2025
@cf/openai/gpt-oss-120b Largest open-source GPT 300/min 120B NEW 2025
@cf/openai/gpt-oss-20b Smaller open-source GPT 300/min 20B NEW 2025
@cf/google/gemma-3-12b-it 128K context, 140+ languages 300/min 12B NEW 2025, vision
@cf/mistralai/mistral-small-3.1-24b-instruct Vision + tool calling 300/min 24B NEW 2025
@cf/qwen/qwq-32b Reasoning, complex tasks 300/min 32B NEW 2025
@cf/qwen/qwen2.5-coder-32b-instruct Coding specialist 300/min 32B NEW 2025
@cf/qwen/qwen3-30b-a3b-fp8 Fast quantized 300/min 30B NEW 2025
@cf/ibm-granite/granite-4.0-h-micro Small, efficient 300/min Micro NEW 2025
Performance (2025)
@cf/meta/llama-3.3-70b-instruct-fp8-fast 2-4x faster (2025 update) 300/min 70B Speculative decoding
@cf/meta/llama-3.1-8b-instruct-fp8-fast Fast 8B variant 300/min 8B -
Standard Models
@cf/meta/llama-3.1-8b-instruct General purpose 300/min 8B -
@cf/meta/llama-3.2-1b-instruct Ultra-fast, simple tasks 300/min 1B -
@cf/deepseek-ai/deepseek-r1-distill-qwen-32b Coding, technical 300/min 32B -
Text Embeddings (2x Faster - 2025)
Model Dimensions Best For Rate Limit Notes
@cf/google/embeddinggemma-300m 768 Best-in-class RAG 3000/min NEW 2025
@cf/baai/bge-base-en-v1.5 768 General RAG (2x faster) 3000/min pooling: "cls" recommended
@cf/baai/bge-large-en-v1.5 1024 High accuracy (2x faster) 1500/min pooling: "cls" recommended
@cf/baai/bge-small-en-v1.5 384 Fast, low storage (2x faster) 3000/min pooling: "cls" recommended
@cf/qwen/qwen3-embedding-0.6b 768 Qwen embeddings 3000/min NEW 2025
CRITICAL (2025): BGE models now support pooling: "cls" parameter (recommended) but NOT backwards compatible with pooling: "mean" (default).
Image Generation Model Best For Rate Limit Notes @cf/black-forest-labs/flux-1-schnell High quality, photorealistic 720/min ⚠️ See warnings below @cf/leonardo/lucid-origin Leonardo AI style 720/min NEW 2025, requires num_steps @cf/leonardo/phoenix-1.0 Leonardo AI variant 720/min NEW 2025, requires num_steps @cf/stabilityai/stable-diffusion-xl-base-1.0 General purpose 720/min Requires num_steps
⚠️ Common Image Generation Issues:
Error 1000: Always include num_steps: 4 parameter (required despite docs suggesting optional) Error 3030 (NSFW filter): Single words like "hamburger" may trigger false positives - add descriptive context to prompts // ✅ Correct pattern for image generation const image = await env.AI.run('@cf/black-forest-labs/flux-1-schnell', { prompt: 'A photo of a delicious hamburger on a plate with fresh vegetables', num_steps: 4, // Required to avoid error 1000 }); // Descriptive context helps avoid NSFW false positives (error 3030)
Vision Models Model Best For Rate Limit Notes @cf/meta/llama-3.2-11b-vision-instruct Image understanding 720/min - @cf/google/gemma-3-12b-it Vision + text (128K context) 300/min NEW 2025 Audio Models (2025) Model Type Rate Limit Notes @cf/deepgram/aura-2-en Text-to-speech (English) 720/min NEW 2025 @cf/deepgram/aura-2-es Text-to-speech (Spanish) 720/min NEW 2025 @cf/deepgram/nova-3 Speech-to-text (+ WebSocket) 720/min NEW 2025 @cf/openai/whisper-large-v3-turbo Speech-to-text (faster) 720/min NEW 2025 Common Patterns RAG (Retrieval Augmented Generation) // 1. Generate embeddings const embeddings = await env.AI.run('@cf/baai/bge-base-en-v1.5', { text: [userQuery] });
// 2. Search Vectorize const matches = await env.VECTORIZE.query(embeddings.data[0], { topK: 3 }); const context = matches.matches.map((m) => m.metadata.text).join('\n\n');
// 3. Generate with context
const response = await env.AI.run('@cf/meta/llama-3.1-8b-instruct', {
messages: [
{ role: 'system', content: Answer using this context:\n${context} },
{ role: 'user', content: userQuery },
],
stream: true,
});
Structured Output with Zod import { z } from 'zod';
const Schema = z.object({ name: z.string(), items: z.array(z.string()) });
const response = await env.AI.run('@cf/meta/llama-3.1-8b-instruct', {
messages: [{
role: 'user',
content: Generate JSON matching: ${JSON.stringify(Schema.shape)}
}],
});
const validated = Schema.parse(JSON.parse(response.response));
AI Gateway Integration
Provides caching, logging, cost tracking, and analytics for AI requests.
Basic Gateway Usage const response = await env.AI.run( '@cf/meta/llama-3.1-8b-instruct', { prompt: 'Hello' }, { gateway: { id: 'my-gateway', skipCache: false } } );
// Access logs and send feedback const gateway = env.AI.gateway('my-gateway'); await gateway.patchLog(env.AI.aiGatewayLogId, { feedback: { rating: 1, comment: 'Great response' }, });
Per-Request Cache Control (Advanced)
Override default cache behavior with HTTP headers for fine-grained control:
// Custom cache TTL (1 hour for expensive queries)
const response = await fetch(
https://gateway.ai.cloudflare.com/v1/${accountId}/${gatewayId}/workers-ai/@cf/meta/llama-3.1-8b-instruct,
{
method: 'POST',
headers: {
'Authorization': Bearer ${env.CLOUDFLARE_API_KEY},
'Content-Type': 'application/json',
'cf-aig-cache-ttl': '3600', // 1 hour in seconds (min: 60, max: 2592000)
},
body: JSON.stringify({
messages: [{ role: 'user', content: prompt }],
}),
}
);
// Skip cache for real-time data const response = await fetch(gatewayUrl, { headers: { 'cf-aig-skip-cache': 'true', // Bypass cache entirely }, // ... });
// Check if response was cached const cacheStatus = response.headers.get('cf-aig-cache-status'); // "HIT" or "MISS"
Available Cache Headers:
cf-aig-cache-ttl: Set custom TTL in seconds (60s to 1 month) cf-aig-skip-cache: Bypass cache entirely ('true') cf-aig-cache-key: Custom cache key for granular control cf-aig-cache-status: Response header showing "HIT" or "MISS"
Benefits: Cost tracking, caching (reduces duplicate inference), logging, rate limiting, analytics, per-request cache customization.
Rate Limits & Pricing (Updated 2025) Rate Limits (per minute) Task Type Default Limit Notes Text Generation 300/min Some fast models: 400-1500/min Text Embeddings 3000/min BGE-large: 1500/min Image Generation 720/min All image models Vision Models 720/min Image understanding Audio (TTS/STT) 720/min Deepgram, Whisper Translation 720/min M2M100, Opus MT Classification 2000/min Text classification Pricing (Unit-Based, Billed in Neurons - 2025)
Free Tier:
10,000 neurons per day Resets daily at 00:00 UTC
Paid Tier ($0.011 per 1,000 neurons):
10,000 neurons/day included Unlimited usage above free allocation
2025 Model Costs (per 1M tokens):
Model Input Output Notes
2025 Models
Llama 4 Scout 17B $0.270 $0.850 NEW 2025
GPT-OSS 120B $0.350 $0.750 NEW 2025
GPT-OSS 20B $0.200 $0.300 NEW 2025
Gemma 3 12B $0.345 $0.556 NEW 2025
Mistral 3.1 24B $0.351 $0.555 NEW 2025
Qwen QwQ 32B $0.660 $1.000 NEW 2025
Qwen Coder 32B $0.660 $1.000 NEW 2025
IBM Granite Micro $0.017 $0.112 NEW 2025
EmbeddingGemma 300M $0.012 N/A NEW 2025
Qwen3 Embedding 0.6B $0.012 N/A NEW 2025
Performance (2025)
Llama 3.3 70B Fast $0.293 $2.253 2-4x faster
Llama 3.1 8B FP8 Fast $0.045 $0.384 Fast variant
Standard Models
Llama 3.2 1B $0.027 $0.201 -
Llama 3.1 8B $0.282 $0.827 -
Deepseek R1 32B $0.497 $4.881 -
BGE-base (2x faster) $0.067 N/A 2025 speedup
BGE-large (2x faster) $0.204 N/A 2025 speedup
Image Models (2025)
Flux 1 Schnell $0.0000528 per 512x512 tile -
Leonardo Lucid $0.006996 per 512x512 tile NEW 2025
Leonardo Phoenix $0.005830 per 512x512 tile NEW 2025
Audio Models (2025)
Deepgram Aura 2 $0.030 per 1k chars NEW 2025
Deepgram Nova 3 $0.0052 per audio min NEW 2025
Whisper v3 Turbo $0.0005 per audio min NEW 2025
Error Handling with Retry
async function runAIWithRetry(
env: Env,
model: string,
inputs: any,
maxRetries = 3
): Promise
for (let i = 0; i < maxRetries; i++) { try { return await env.AI.run(model, inputs); } catch (error) { lastError = error as Error;
// Rate limit - retry with exponential backoff
if (lastError.message.toLowerCase().includes('rate limit')) {
await new Promise((resolve) => setTimeout(resolve, Math.pow(2, i) * 1000));
continue;
}
throw error; // Other errors - fail immediately
}
}
throw lastError!; }
OpenAI Compatibility import OpenAI from 'openai';
const openai = new OpenAI({
apiKey: env.CLOUDFLARE_API_KEY,
baseURL: https://api.cloudflare.com/client/v4/accounts/${env.ACCOUNT_ID}/ai/v1,
});
// Chat completions await openai.chat.completions.create({ model: '@cf/meta/llama-3.1-8b-instruct', messages: [{ role: 'user', content: 'Hello!' }], });
Endpoints: /v1/chat/completions, /v1/embeddings
Vercel AI SDK Integration (workers-ai-provider v3.0.2) import { createWorkersAI } from 'workers-ai-provider'; // v3.0.2 with AI SDK v5 import { generateText, streamText } from 'ai';
const workersai = createWorkersAI({ binding: env.AI });
// Generate or stream await generateText({ model: workersai('@cf/meta/llama-3.1-8b-instruct'), prompt: 'Write a poem', });
Community Tips
Note: These tips come from community discussions and production experience.
Hono Framework Streaming Pattern
When using Workers AI streaming with Hono, return the stream directly as a Response (not through Hono's streaming utilities):
import { Hono } from 'hono';
type Bindings = { AI: Ai }; const app = new Hono<{ Bindings: Bindings }>();
app.post('/chat', async (c) => { const { prompt } = await c.req.json();
const stream = await c.env.AI.run('@cf/meta/llama-3.1-8b-instruct', { messages: [{ role: 'user', content: prompt }], stream: true, });
// Return stream directly (not c.stream()) return new Response(stream, { headers: { 'content-type': 'text/event-stream', 'cache-control': 'no-cache', 'connection': 'keep-alive', }, }); });
Source: Hono Discussion #2409
Troubleshooting Unexplained AI Binding Failures
If experiencing unexplained Workers AI failures:
1. Check wrangler version
npx wrangler --version
2. Clear wrangler cache
rm -rf ~/.wrangler
3. Update to latest stable
npm install -D wrangler@latest
4. Check local network/firewall settings
Some corporate firewalls block Workers AI endpoints
Note: Most "version incompatibility" issues turn out to be network configuration problems.
References Workers AI Docs Models Catalog AI Gateway Pricing Changelog LoRA Adapters MCP Tool: Use mcp__cloudflare-docs__search_cloudflare_documentation for latest docs