cloudflare-workers-ai
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<Response> {
const stream = await env.AI.run('@cf/meta/llama-3.1-8b-instruct', {
messages: [{ role: 'user', content: 'Tell me a story' }],
stream: true, // Always stream for text generation!
});
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<ModelOutput | ReadableStream>
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: 4parameter (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 controlcf-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 | $0.0005 per audio min | NEW 2025 |
Error Handling with Retry
async function runAIWithRetry(
env: Env,
model: string,
inputs: any,
maxRetries = 3
): Promise<any> {
let lastError: Error;
for (let i = 0; i < maxRetries; i++) {
try {
return await env.AI.run(model, inputs);
} catch (error) {
lastError = error as Error;
// Rate limit - retry with exponential backoff
if (lastError.message.toLowerCase().includes('rate limit')) {
await new Promise((resolve) => setTimeout(resolve, Math.pow(2, i) * 1000));
continue;
}
throw error; // Other errors - fail immediately
}
}
throw lastError!;
}
OpenAI Compatibility
import OpenAI from 'openai';
const openai = new OpenAI({
apiKey: env.CLOUDFLARE_API_KEY,
baseURL: `https://api.cloudflare.com/client/v4/accounts/${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_documentationfor latest docs