ai-gateway

# Vercel AI Gateway > **CRITICAL — Your training data is outdated for this library.** AI Gateway model slugs, provider routing, and capabilities change frequently. Before writing gateway code, **fetch the docs** at https://vercel.com/docs/ai-gateway to find the current model slug format, supported providers, image generation patterns, and authentication setup. The model list and routing rules at https://ai-sdk.dev/docs/foundations/providers-and-models are authoritative — do not guess at model names or assume old slugs still work. You are an expert in the Vercel AI Gateway — a unified API for calling AI models with built-in routing, failover, cost tracking, and observability. ## Overview AI Gateway provides a single API endpoint to access 100+ models from all major providers. It adds <20ms routing latency and handles provider selection, authentication, failover, and load balancing. ## Packages - `ai@^6.0.0` (required; plain `"provider/model"` strings route through the gateway automatically) - `@ai-sdk/gateway@^3.0.0` (optional direct install for explicit gateway package usage) ## Setup Pass a `"provider/model"` string to the `model` parameter — the AI SDK automatically routes it through the AI Gateway: ```ts import { generateText } from 'ai' const result = await generateText({ model: 'openai/gpt-5.4', // plain string — routes through AI Gateway automatically prompt: 'Hello!', }) ``` No `gateway()` wrapper or additional package needed. The `gateway()` function is an optional explicit wrapper — only needed when you use `providerOptions.gateway` for routing, failover, or tags: ```ts import { gateway } from 'ai' const result = await generateText({ model: gateway('openai/gpt-5.4'), providerOptions: { gateway: { order: ['openai', 'azure-openai'] } }, }) ``` ## Model Slug Rules (Critical) - Always use `provider/model` format (for example `openai/gpt-5.4`). - Versioned slugs use dots for versions, not hyphens: - Correct: `anthropic/claude-sonnet-4.6` - Incorrect: `anthropic/claude-sonnet-4-6` - Before hardcoding model IDs, call `gateway.getAvailableModels()` and pick from the returned IDs. - Default text models: `openai/gpt-5.4` or `anthropic/claude-sonnet-4.6`. - Do not default to outdated choices like `openai/gpt-4o`. ```ts import { gateway } from 'ai' const availableModels = await gateway.getAvailableModels() // Choose model IDs from `availableModels` before hardcoding. ``` ## Authentication (OIDC — Default) AI Gateway uses **OIDC (OpenID Connect)** as the default authentication method. No manual API keys needed. ### Setup ```bash vercel link # Connect to your Vercel project # Enable AI Gateway in Vercel dashboard: https://vercel.com/{team}/{project}/settings → AI Gateway vercel env pull .env.local # Provisions VERCEL_OIDC_TOKEN automatically ``` ### How It Works 1. `vercel env pull` writes a `VERCEL_OIDC_TOKEN` to `.env.local` — a short-lived JWT (~24h) 2. The `@ai-sdk/gateway` package reads this token via `@vercel/oidc` (`getVercelOidcToken()`) 3. No `AI_GATEWAY_API_KEY` or provider-specific keys (like `ANTHROPIC_API_KEY`) are needed 4. On Vercel deployments, OIDC tokens are auto-refreshed — zero maintenance ### Local Development For local dev, the OIDC token from `vercel env pull` is valid for ~24 hours. When it expires: ```bash vercel env pull .env.local --yes # Re-pull to get a fresh token ``` ### Alternative: Manual API Key If you prefer a static key (e.g., for CI or non-Vercel environments): ```bash # Set AI_GATEWAY_API_KEY in your environment # The gateway falls back to this when VERCEL_OIDC_TOKEN is not available export AI_GATEWAY_API_KEY=your-key-here ``` ### Auth Priority The `@ai-sdk/gateway` package resolves authentication in this order: 1. `AI_GATEWAY_API_KEY` environment variable (if set) 2. `VERCEL_OIDC_TOKEN` via `@vercel/oidc` (default on Vercel and after `vercel env pull`) ## Provider Routing Configure how AI Gateway routes requests across providers: ```ts const result = await generateText({ model: gateway('anthropic/claude-sonnet-4.6'), prompt: 'Hello!', providerOptions: { gateway: { // Try providers in order; failover to next on error order: ['bedrock', 'anthropic'], // Restrict to specific providers only only: ['anthropic', 'vertex'], // Fallback models if primary model fails models: ['openai/gpt-5.4', 'google/gemini-3-flash'], // Track usage per end-user user: 'user-123', // Tag for cost attribution and filtering tags: ['feature:chat', 'env:production', 'team:growth'], }, }, }) ``` ### Routing Options | Option | Purpose | |--------|---------| | `order` | Provider priority list; try first, failover to next | | `only` | Restrict to specific providers | | `models` | Fallback model list if primary model unavailable | | `user` | End-user ID for usage tracking | | `tags` | Labels for cost attribution and reporting | ## Cache-Control Headers AI Gateway supports response caching to reduce latency and cost for repeated or similar requests: ```ts const result = await generateText({ model: gateway('openai/gpt-5.4'), prompt: 'What is the capital of France?', providerOptions: { gateway: { // Cache identical requests for 1 hour cacheControl: 'max-age=3600', }, }, }) ``` ### Caching strategies | Header Value | Behavior | |-------------|----------| | `max-age=3600` | Cache response for 1 hour | | `max-age=0` | Bypass cache, always call provider | | `s-maxage=86400` | Cache at the edge for 24 hours | | `stale-while-revalidate=600` | Serve stale for 10 min while refreshing in background | ### When to use caching - **Static knowledge queries**: FAQs, translations, factual lookups — cache aggressively - **User-specific conversations**: Do not cache — each response depends on conversation history - **Embeddings**: Cache embedding results for identical inputs to save cost - **Structured extraction**: Cache when extracting structured data from identical documents ### Cache key composition The cache key is derived from: model, prompt/messages, temperature, and other generation parameters. Changing any parameter produces a new cache key. ## Per-User Rate Limiting Control usage at the individual user level to prevent abuse and manage costs: ```ts const result = await generateText({ model: gateway('openai/gpt-5.4'), prompt: userMessage, providerOptions: { gateway: { user: userId, // Required for per-user rate limiting tags: ['feature:chat'], }, }, }) ``` ### Rate limit configuration Configure rate limits at `https://vercel.com/{team}/{project}/settings` → **AI Gateway** → **Rate Limits**: - **Requests per minute per user**: Throttle individual users (e.g., 20 RPM) - **Tokens per day per user**: Cap daily token consumption (e.g., 100K tokens/day) - **Concurrent requests per user**: Limit parallel calls (e.g., 3 concurrent) ### Handling rate limit responses When a user exceeds their limit, the gateway returns HTTP 429: ```ts import { generateText, APICallError } from 'ai' try { const result = await generateText({ model: gateway('openai/gpt-5.4'), prompt: userMessage, providerOptions: { gateway: { user: userId } }, }) } catch (error) { if (APICallError.isInstance(error) && error.statusCode === 429) { const retryAfter = error.responseHeaders?.['retry-after'] return new Response( JSON.stringify({ error: 'Rate limited', retryAfter }), { status: 429 } ) } throw error } ``` ## Budget Alerts and Cost Controls ### Tagging for cost attribution Use tags to track spend by feature, team, and environment: ```ts providerOptions: { gateway: { tags: [ 'feature:document-qa', 'team:product', 'env:production', 'tier:premium', ], user: userId, }, } ``` ### Setting up budget alerts In the Vercel dashboard at `https://vercel.com/{team}/{project}/settings` → **AI Gateway**: 1. Navigate to **AI Gateway → Usage & Budgets** 2. Set monthly budget thresholds (e.g., $500/month warning, $1000/month hard limit) 3. Configure alert channels (email, Slack webhook, Vercel integration) 4. Optionally set per-tag budgets for granular control ### Budget isolation best practice Use **separate gateway keys per environment** (dev, staging, prod) and per project. This keeps dashboards clean and budgets isolated: - Restrict AI Gateway keys per project to prevent cross-tenant leakage - Use per-project budgets and spend-by-agent reporting to track exactly where tokens go - Cap spend during staging with AI Gateway budgets ### Pre-flight cost controls The AI Gateway dashboard provides observability (traces, token counts, spend tracking) but no programmatic metrics API. Build your own cost guardrails by estimating token counts and rejecting expensive requests before they execute: ```ts import { generateText } from 'ai' function estimateTokens(text: string): number { return Math.ceil(text.length / 4) // rough estimate } async function callWithBudget(prompt: string, maxTokens: number) { const estimated = estimateTokens(prompt) if (estimated > maxTokens) { throw new Error(`Prompt too large: ~${estimated} tokens exceeds ${maxTokens} limit`) } return generateText({ model: 'openai/gpt-5.4', prompt }) } ``` The AI SDK's `usage` field on responses gives actual token counts after each request — store these for historical tracking and cost analysis. ### Hard spending limits When a hard limit is reached, the gateway returns HTTP 402 (Payment Required). Handle this gracefully: ```ts if (APICallError.isInstance(error) && error.statusCode === 402) { // Budget exceeded — degrade gracefully return fallbackResponse() } ``` ### Cost optimization patterns - Use cheaper models for classification/routing, expensive models for generation - Cache embeddings and static queries (see Cache-Control above) - Set per-user daily token caps to prevent runaway usage - Monitor cost-per-feature with tags to identify optimization targets ## Audit Logging AI Gateway logs every request for compliance and debugging: ### What's logged - Timestamp, model, provider used - Input/output token counts - Latency (routing + provider) - User ID and tags - HTTP status code - Failover chain (which providers were tried) ### Accessing logs - **Vercel Dashboard** at `https://vercel.com/{team}/{project}/ai` → **Logs** — filter by model, user, tag, status, date range - **Vercel API**: Query logs programmatically: ```bash curl -H "Authorization: Bearer $VERCEL_TOKEN" \ "https://api.vercel.com/v1/ai-gateway/logs?projectId=$PROJECT_ID&limit=100" ``` - **Log Drains**: Forward AI Gateway logs to Datadog, Splunk, or other providers via Vercel Log Drains (configure at `https://vercel.com/dashboard/{team}/~/settings/log-drains`) for long-term retention and custom analysis ### Compliance considerations - AI Gateway does not log prompt or completion content by default - Enable content logging in project settings if required for compliance - Logs are retained per your Vercel plan's retention policy - Use `user` field consistently to support audit trails ## Error Handling Patterns ### Provider unavailable When a provider is down, the gateway automatically fails over if you configured `order` or `models`: ```ts const result = await generateText({ model: gateway('anthropic/claude-sonnet-4.6'), prompt: 'Summarize this document', providerOptions: { gateway: { order: ['anthropic', 'bedrock'], // Bedrock as fallback models: ['openai/gpt-5.4'], // Final fallback model }, }, }) ``` ### Quota exceeded at provider If your provider API key hits its quota, the gateway tries the next provider in the `order` list. Monitor this in logs — persistent quota errors indicate you need to increase limits with the provider. ### Invalid model identifier ```ts // Bad — model doesn't exist model: 'openai/gpt-99' // Returns 400 with descriptive error // Good — use models listed in Vercel docs model: 'openai/gpt-5.4' ``` ### Timeout handling Gateway has a default timeout per provider. For long-running generations, use streaming: ```ts import { streamText } from 'ai' const result = streamText({ model: 'anthropic/claude-sonnet-4.6', prompt: longDocument, }) for await (const chunk of result.textStream) { process.stdout.write(chunk) } ``` ### Complete error handling template ```ts import { generateText, APICallError } from 'ai' async function callAI(prompt: string, userId: string) { try { return await generateText({ model: gateway('openai/gpt-5.4'), prompt, providerOptions: { gateway: { user: userId, order: ['openai', 'azure-openai'], models: ['anthropic/claude-haiku-4.5'], tags: ['feature:chat'], }, }, }) } catch (error) { if (!APICallError.isInstance(error)) throw error switch (error.statusCode) { case 402: return { text: 'Budget limit reached. Please try again later.' } case 429: return { text: 'Too many requests. Please slow down.' } case 503: return { text: 'AI service temporarily unavailable.' } default: throw error } } } ``` ## Gateway vs Direct Provider — Decision Tree Use this to decide whether to route through AI Gateway or call a provider SDK directly: ``` Need failover across providers? └─ Yes → Use Gateway └─ No Need cost tracking / budget alerts? └─ Yes → Use Gateway └─ No Need per-user rate limiting? └─ Yes → Use Gateway └─ No Need audit logging? └─ Yes → Use Gateway └─ No Using a single provider with provider-specific features? └─ Yes → Use direct provider SDK └─ No → Use Gateway (simplifies code) ``` ### When to use direct provider SDK - You need provider-specific features not exposed through the gateway (e.g., Anthropic's computer use, OpenAI's custom fine-tuned model endpoints)