Veto vs Cloudflare AI Firewall
Cloudflare runs a global edge network. Firewall for AI and AI Gateway sit on that edge, filtering and instrumenting the HTTP requests your application makes to LLM providers. Veto sits inside the agent process, intercepting tool calls before they execute. One controls the wire between you and the model. The other controls what happens after the model responds. They live at different layers and solve different problems.
Honest assessment
Cloudflare's AI Gateway covers a concrete network-layer job: point traffic at a proxy URL, get caching, rate limiting, fallback routing across providers, request logging, and edge filters in one shot. For teams that only need a validated pipe to OpenAI and Anthropic, it fits that network-layer job. Veto does not compete with that. Veto answers a different question: given that the model said to call delete_user(id=42), should we execute it? That decision happens inside the agent, far below the network layer. The layers can coexist.
What each product does
Cloudflare AI Firewall
Firewall for AI and AI Gateway from Cloudflare. Firewall for AI is a managed WAF-style product specifically tuned for LLM traffic patterns. AI Gateway is a proxy in front of OpenAI, Anthropic, Workers AI, and other providers that adds observability, caching, retries, and edge controls.
Core capability:
Sit at the network edge between your application and LLM providers. Filter requests, cache responses, record governed calls, enforce rate limits, fail over between providers, and detect a baseline of prompt-injection and sensitive data patterns in flight.
- Edge-level LLM traffic firewall
- Provider-agnostic LLM gateway and routing
- Response caching and cost reduction
- Per-request analytics and traces
- Built on Cloudflare's global anycast network
Veto
An open-source SDK that runs inside the agent process. It intercepts governed tool calls the agent attempts and evaluates it against YAML policies before letting the call execute.
Core capability:
Policy-driven authorization at the function-call boundary. Decides allow, deny, or escalate to human approval based on tool name, arguments, user, and context. In-process evaluation on the local critical path.
- In-process tool-call interception
- YAML policies in version control
- Human approval workflows for review-required actions
- SDK and framework integrations including MCP
- Apache-2.0 SDK, self-hostable
Feature comparison
These products operate at different points in the request lifecycle. The table is less "feature parity" and more "where each tool sits."
| Capability | Veto | Cloudflare AI Firewall |
|---|---|---|
| Tool-call authorization | ||
| Human approval workflows | ||
| YAML policy engine | ||
| Edge filtering of LLM traffic | ||
| LLM response caching | ||
| Multi-provider failover | ||
| Rate limiting on LLM calls | ||
| Per-decision record | ||
| Visibility into agent tools | ||
| Open-source core | ||
| Self-hosted | ||
| MCP support |
Choose by enforcement layer: edge or application
A practical rule for where to put a control: put it as close to the asset you are protecting as possible. For LLM cost, latency, and basic abuse, the edge is the right place. Cloudflare's network can stop traffic before it ever reaches your origin. For tool-call authorization, the edge is the wrong boundary when the tool call is not on the wire. It is inside your process.
What Cloudflare sees
HTTP requests to api.openai.com, api.anthropic.com, and similar. JSON payloads in and out. Headers, IPs, rate. It can cache an identical prompt, fail over to a backup provider, redact a Social Security number in flight. It is not positioned to authorize the next thing your code will do with the response.
What Veto sees
Function invocations inside your agent. The model produced a tool call --send_invoice(customer="enterprise-corp", amount=42000) -- and your runtime is about to dispatch it. Veto evaluates that call against policy. If policy says "amounts over $10k need approval," the call pauses for a human. That decision belongs in the process where the tool call is made.
Combine them
AI Gateway in front of your LLM calls handles cost and reliability. Veto inside your agent handles authorization. They do not overlap and they do not conflict.
Pricing
Open Source runs locally. Core adds shared approvals, decision records, export, and retention. See the pricing page for price, retention, and action limits.
AI Gateway publishes entry-tier request quotas; Firewall for AI is packaged through Cloudflare's pricing pages and order forms. Verify plan limits, WAF attachment, and traffic-based charges before procurement on the standard Cloudflare model.
Decision framework
Choose Veto when
- Your agent executes tools with real-world consequences
- You need allow, deny, or approve on individual function calls
- Policy must consider tool arguments and user context, not just headers
- Review teams need a decision record for every authorization decision
- You build against MCP, LangChain, or other agent frameworks
Choose Cloudflare AI Firewall when
- You want cost and reliability controls in front of LLM providers
- Edge caching and multi-provider failover are priorities
- You already run Cloudflare and want unified observability
- Per-LLM-call rate limiting and traffic shaping matter
- You need a baseline of edge-level prompt and PII filtering
Frequently asked questions
Can Cloudflare's Firewall for AI block tool calls?
Is Cloudflare AI Gateway a competitor to Veto?
What about prompt injection? Does not Firewall for AI cover that?
Can I use Cloudflare and Veto together?
Related comparisons
LLM routing vs action control
Veto vs LakeraPrompt filtering vs authorization
Veto vs ArcjetApp security vs agent control
Going deeper: Runtime agent authorization, MCP integration, The authorization gap.
Edge controls the wire. Veto controls what runs inside.