Veto vs Vercel AI Gateway
Vercel AI Gateway is a unified API for model providers with built-in caching, fallback routing, and observability. It sits between your app and the LLM. Veto is a runtime authorization SDK that sits between the model response and the tool that runs. Both products are common in production AI stacks. They solve different problems on different sides of the model call.
Honest assessment
Vercel AI Gateway is a well-built infrastructure product. If you call multiple model providers, want a unified billing surface, automatic fallback, and a single observability story. It helps model-call operations. None of that involves authorization. When the model returns a tool call and your agent is about to execute delete_user(id="42"), AI Gateway is already out of the loop. Veto is what runs at that point. Calling these competitors misreads the stack. They are upstream and downstream of each other.
What each product does
Vercel AI Gateway
A managed gateway from Vercel that gives you a single API across model providers: OpenAI, Anthropic, Google, Mistral, and more. Pairs with the Vercel AI SDK to add caching, fallback, retries, and logging.
Core capability:
Route a model request to a provider, optionally cache it, retry on failure, fall back to another model, and emit observability data. A unified billing and usage surface so you do not manage N keys yourself.
- Unified API across model providers
- Caching, retries, and fallback routing
- Per-request observability and analytics
- Tight integration with the Vercel AI SDK
- Managed service, no infra to run
Veto
An open-source SDK that intercepts AI agent tool calls and evaluates them against YAML policies before execution. Sits inside the agent process, decides allow, deny, or escalate per call.
Core capability:
Policy-driven authorization on each tool invocation. Considers tool name, arguments, user, and context. Review-required actions can be routed to a human for approval before the call resumes.
- Tool-call interception and authorization
- Human review approval queues
- Declarative YAML policies in version control
- SDK and framework integrations
- Apache-2.0 SDK, self-hostable engine
Feature comparison
The table mostly shows that AI Gateway and Veto sit at different points in the request lifecycle. They overlap on observability and not much else.
| Capability | Veto | AI Gateway |
|---|---|---|
| Tool-call authorization | ||
| Human approval workflows | ||
| YAML policy engine | ||
| Unified model provider API | ||
| Model response caching | ||
| Automatic model fallback | ||
| Per-call decision record | ||
| Argument-aware decisions | ||
| Works with Vercel AI SDK | ||
| Self-hostable | ||
| TypeScript SDK | ||
| Open-source core |
Choose by request lifecycle stage
A typical agent loop has three phases. Each product sits in a different phase. Calling them competitors confuses the layer they live at.
Phase 1: model request (AI Gateway's home)
Your app sends a prompt and tool schema to a model. AI Gateway picks the provider, caches, falls back on error, returns the response. The agent now has a tool-call suggestion from the model.
Phase 2: tool authorization (Veto's home)
Before the SDK calls the function, Veto evaluates: tool, arguments, user, context, policy. Allow, deny, or escalate to a human approver.
Phase 3: execution
If Veto allows, the tool runs. Result feeds back to the agent. AI Gateway and Veto have already done their jobs at this point: one on the model boundary, one on the governed tool path. Both write audit data. Neither replaces the other.
Pricing
Open Source runs locally. Core adds shared approvals, decision records, export, and retention. See the pricing page for price, retention, and action limits.
Usage-based billing. You pay for the underlying model tokens plus Vercel's margin and infra. Vercel publishes current AI Gateway packaging on its pricing page; check plan limits and usage charges. Extra spend depends on request volume.
Decision framework
Choose Veto when
- Your agent executes side-effectful tools (DB writes, payments, comms)
- You need allow, deny, or approve decisions per call
- Sensitive operations must route to a human before execution
- You want per-decision records logs
- You build with the Vercel AI SDK, LangChain, or MCP
Choose AI Gateway when
- You call several model providers and want one API surface
- Caching, retries, and fallback are part of your reliability story
- You want per-request analytics tied to a Vercel project
- You do not want to manage N model provider keys yourself
- You already deploy on Vercel and like the workflow
Frequently asked questions
Is Vercel AI Gateway a security product?
Can AI Gateway block unauthorized tool calls?
Do I need both?
Does Veto replace Vercel's AI SDK?
Related comparisons
Edge vs application layer
Veto vs Bedrock GuardrailsModel output vs tool execution
Veto vs ArcjetInbound abuse vs outbound action
Going deeper: AI agent authorization, Vercel AI SDK integration, Blog: the authorization gap.
AI Gateway routes the model. Veto governs the action.