DeepInspect vs ngrok AI Gateway: Where Each One Sits on the AI Request Path

ngrok spent a decade as the fastest way to get a public URL onto a laptop process, and in 2025 it put an AI Gateway on top of that same edge. The product now sits in front of OpenAI, Anthropic, Google, and self-hosted Ollama or vLLM endpoints behind an OpenAI-compatible interface, with multi-provider routing, automatic failover, rate limiting, caching, PII redaction, and a traffic policy engine that handles JWT validation and IP restrictions (ngrok Universal Gateway docs).

Teams evaluating it against DeepInspect are usually asking one question underneath the feature list: which of these two is the thing that decides whether a given model call is allowed to happen.

I want to walk what each product is, where it sits on the request path, and which buyer each one fits. Both run on the same wire. They were built to answer different questions.

TL;DR

ngrok AI Gateway is a developer-facing edge that routes AI traffic across providers, fails over when one degrades, and applies traffic policy at the connection layer. DeepInspect is a stateless policy proxy that binds every model call to an authenticated enterprise identity, evaluates policy per request, and commits an audit record before the response returns to the application.

ngrok AI Gateway: the edge you already run

ngrok's design centre is traffic. The company built its reputation on ingress, and the AI Gateway extends that engine to egress toward model providers. The pitch is coherent: you already terminate connections at ngrok, so intercept the LLM calls there too.

What you get is genuinely useful. One OpenAI-compatible endpoint fronts several providers, so a model swap becomes a config change rather than a code change. Cost-based routing picks a cheaper provider when quality tolerance allows. Automatic failover covers the provider outage that used to page someone at 2am. API key management moves the provider credentials out of application config and into the gateway, which removes a real class of leak. Rate limits and caching cut spend on repetitive workloads.

The traffic policy engine handles JWT validation and IP restrictions, which are connection-level controls. ngrok's own 2026 write-up on AI gateways lists guardrails such as prompt injection detection and compliance filtering as roadmap items rather than shipped controls (ngrok blog).

If I were running a twelve-person team shipping an AI feature this quarter, I would put ngrok in front of it and stop thinking about the problem for six months. That is a real recommendation, and it has a ceiling.

The policy decision point

DeepInspect is a stateless proxy on the HTTP path between authenticated users or agents and any LLM endpoint. Every request carries an identity supplied by the calling application, and that identity is the primary key for the policy decision.

The sequence runs in a fixed order:

The record commits before the response returns to the application, on a write path the application never controls. That ordering is the whole point. An application that writes its own compliance log is attesting to its own behaviour, which is the arrangement every other regulated industry abandoned decades ago. The broader argument sits in identity-aware AI gateway.

Policy evaluates per request rather than per connection. A JWT proves a caller authenticated. It says nothing about whether this particular prompt, carrying this particular data class, should reach this particular model right now. That gap between authentication and authorization is where the interesting failures live.

Feature comparison

| Capability | ngrok AI Gateway | DeepInspect | |---|---|---| | Multi-provider routing | Yes | Yes | | Automatic failover | Yes | Yes | | Provider key custody | Yes | Yes | | Rate limiting | Yes, connection and key scoped | Yes, identity and role scoped | | Response caching | Yes | Yes, post-policy only | | Connection-level auth (JWT, IP) | Yes | Yes | | Identity bound to enterprise IdP per request | Not the design centre | Core | | Prompt content classification | PII redaction | Classification against customer-defined data classes | | Per-request policy on identity plus data class | No | Core | | Prompt injection controls | Roadmap per ngrok | Response and request side policy | | Per-decision audit record, application-independent | Traffic logs | Core, committed pre-response | | Fail-closed enforcement | Availability oriented | Configurable, fail-closed supported | | Self-hosted and on-prem model endpoints | Yes | Yes | | Primary buyer | Platform and application engineers | Security, compliance, platform engineering |

The row that matters most is the audit record. ngrok produces traffic logs, which answer what moved through the edge. A regulator asking about EU AI Act Article 12 traceability wants something narrower: which natural person or agent initiated this inference, under which policy version, with what outcome, recorded by a system the application could not edit. Article 12 requires automatic recording of events across the system lifetime, including timestamps, input data, and identification of the natural persons involved (Practical AI Act).

Pick ngrok AI Gateway if

• Your immediate problem is provider reliability, cost, and key sprawl rather than proving who did what.
• Your team is small enough that the people writing the application are the people setting the policy.
• You want one config surface for both ingress and AI egress, and you already run ngrok.
• Your AI workloads handle low-sensitivity data where PII redaction covers the exposure.
• You are pre-audit, with no regulator or enterprise customer asking for per-decision evidence yet.

Pick DeepInspect if

• You operate under the EU AI Act, DORA, HIPAA, or a sector mandate that asks for per-decision records.
• Different roles inside your organisation need different model and data permissions, enforced rather than documented.
• You need the audit trail to survive the claim that the application generated its own evidence.
• Agent traffic is growing and you need action lineage tying each model call to a delegated authority.
• Your security team owns the policy and needs it enforced independently of application release cycles.

Running both

These products are not mutually exclusive, and I would expect the sophisticated deployment to run both. ngrok handles the traffic concerns it was built for: failover, cost routing, key custody at the edge. DeepInspect handles the decision and the record.

Ordering matters if you do. The policy decision has to precede the cache lookup, or a cached response answers a request that current policy would refuse. Routing follows the same principle: the identity and data-class decision belongs upstream of provider selection, because "which model may see this content" is a policy question rather than a cost question.

Pricing approach

ngrok prices on a published subscription model with usage tiers, visible on their site before a sales conversation. DeepInspect prices through a sales conversation scoped to deployment size and enforcement requirements. Teams that want a price on a web page today will prefer ngrok's model, and that preference is legitimate.

DeepInspect

This is the gap DeepInspect closes. DeepInspect is a stateless proxy at the AI request boundary that evaluates identity, data classification, model authorization, and organizational policy for every request, then makes a pass or block decision before traffic reaches the model.

Because enforcement runs inline rather than on a log-and-alert delay, a denied request never reaches the provider. Enforcement overhead measures under 50 ms in internal testing against inference times of 500 ms to several seconds, so the decision is invisible next to the model's own latency. Every decision writes a structured record containing the identity, the policy version, the data classification, and the outcome, committed before the response returns and stored where the calling application has no write access.

If you are facing the August deadline, let's talk.