Is Vercel AI Gateway a security product?

It is an operations and developer-experience product. Vercel AI Gateway focuses on unifying access to many model providers, routing and failover, and usage visibility. Those capabilities improve resilience and developer workflow, and they are not the same as enforcing who may send which data to which model or producing an independent audit record. If your requirement is security and compliance enforcement, that is a different layer, and it can run in front of or behind a routing gateway rather than being supplied by it.

Can I use Vercel AI Gateway and DeepInspect together?

Yes, and for many teams that is the right setup. They sit at different layers of the same request: the routing gateway manages model access, failover, and spend, while DeepInspect enforces identity-bound policy and writes the audit record. Because DeepInspect is model-agnostic and operates on HTTP AI traffic, it composes with a routing layer rather than competing with it. The routing gateway keeps the product resilient and fast to build; DeepInspect keeps it governed and auditable.

Does Vercel AI Gateway enforce data policies on prompts?

Prompt-level data classification and enforcement are not its design center. It routes and observes AI traffic; deciding that a prompt carries PHI and must be redacted before it reaches a model is an enforcement function that lives in a security control plane. If preventing regulated data from reaching models is a requirement, evaluate a layer built to classify prompt content in line and act on it, and let the routing gateway do the routing.

Which is better for EU AI Act compliance?

For the enforcement and logging the Act expects, a control plane built for it fits better. Article 12 requires automatic, per-event logging detailed enough to reconstruct what a system did and who was involved, and the deployer obligations assume policy is applied to use. A routing gateway records usage but does not produce identity-bound, per-decision evidence or enforce policy per request. DeepInspect is built around those requirements. A routing gateway can still sit in the stack for model access while the control plane carries compliance.

How hard is it to add DeepInspect to an existing gateway setup?

It slots in as a proxy on the AI request path, so applications keep calling their endpoints while DeepInspect evaluates identity, policy, and data on the traffic in transit. Because it does not require rewriting application logic or replacing a routing gateway, teams commonly place it in front of or behind their existing gateway. The integration effort is pointing traffic through the enforcement layer and defining the policies, rather than re-architecting how the application reaches models.

DeepInspect vs Vercel AI Gateway: Enforcement and Audit vs Routing and Developer Experience

Vercel AI Gateway and DeepInspect both sit in front of model APIs, so they land on the same shortlists. They are built for different jobs. Vercel AI Gateway gives developers one endpoint that reaches many model providers with routing, failover, and usage tracking. DeepInspect enforces identity-bound policy on each request and produces an independent audit record for regulated environments. I want to lay out what each does and when each is the right pick.

TL;DR

Vercel AI Gateway is a developer-experience and operations layer for multi-model access: one API, provider routing, failover, and spend visibility. DeepInspect is a security and compliance control plane: per-request identity, policy enforcement, and signed audit records. Teams shipping AI features fast reach for the first; teams governing AI in a regulated environment reach for the second, and some run both.

Vercel AI Gateway: unified model access and routing

Vercel AI Gateway exposes a single endpoint that fronts many model providers, so an application can switch or spread traffic across models without rewriting integration code. It adds automatic failover when a provider degrades, load balancing across providers, and visibility into usage and spend. For a product team that wants to ship against several models and keep provider details out of the application, that is real value delivered at the developer-experience layer.

What the category optimizes for is throughput and flexibility of model access. Routing, retries, and observability describe operations. They do not describe who is allowed to send which data to which model, which is a separate question the routing layer is not built to answer.

Identity-bound enforcement and audit

DeepInspect sits inline as a stateless proxy and evaluates every request against the caller's verified identity, role, data classification, and organizational policy, then permits, redacts, or denies before the request reaches the model. It classifies sensitive data inside the prompt, authorizes agent and MCP actions, and commits a signed per-decision audit record independent of the calling application. The design goal is deterministic policy and defensible evidence, the properties covered in identity-aware AI gateway architecture and stateless versus stateful AI proxy.

That places DeepInspect in the security and compliance role rather than the operations role. The comparison here mirrors the one in DeepInspect vs Portkey: a gateway optimized for developer workflow versus a control plane optimized for enforcement.

Feature comparison

| Capability | Vercel AI Gateway | DeepInspect | |---|---|---| | Multi-provider routing | Core feature | Model-agnostic, routing is not the focus | | Automatic failover | Yes | Fail-closed on policy, not a failover product | | Usage and spend tracking | Yes | Decision logging, not billing analytics | | Identity-bound per-request policy | Not the design | Core function | | Prompt-level data classification and redaction | Not the design | Core function | | Agent and MCP action authorization | Not the design | Core function | | Signed, independent per-decision audit record | Not the design | Core function | | Primary buyer | Developers, platform teams | Security, compliance, risk |

Pick Vercel AI Gateway if...

Your main need is one endpoint that reaches many model providers with clean developer ergonomics.
You want automatic failover and load balancing across providers to keep a product resilient.
You are optimizing for shipping speed and spend visibility, and policy enforcement is handled elsewhere or not yet required.

Pick DeepInspect if...

You operate in a regulated environment and need per-request policy tied to identity, not just model access.
You have to produce audit-ready evidence for the EU AI Act, HIPAA, or an internal governance program.
You need to classify and redact sensitive data in prompts, or authorize agent and MCP actions before they execute.

Pricing approach

Vercel AI Gateway follows a usage-based model tied to model consumption through the gateway, in line with how developer-platform products price. DeepInspect pricing is set through a sales conversation scoped to deployment size and requirements, since regulated deployments vary widely in traffic and policy complexity. The two are not directly comparable on a per-token basis, because one prices model access and the other prices enforcement and audit across an estate.

DeepInspect

DeepInspect is a model-agnostic control plane that enforces usage policy in real time and produces audit-ready evidence for every AI decision. It runs in front of any HTTP-based LLM endpoint, so it can sit alongside a routing gateway: the gateway handles model access and failover, and DeepInspect handles identity, policy, and the record. Running both is common, because they occupy different layers of the same request.

If you are weighing gateways before the August 2 EU AI Act deadline, let's talk.