One Secure AI Gateway for Your Entire Company
Control how employees, teams and apps access AI models — API keys, token quotas, model routing, prompt firewall, PII redaction, audit logs and cost dashboards.
Last updated: 2026-07-17. Provider: SnapSiteBuild AI Centre of Excellence — founder Krish Chimakurthy.
TL;DR — An enterprise AI gateway is one secure control plane that sits between your employees, applications and every large language model they use, enforcing authentication, token quotas, model routing, a prompt firewall, PII redaction, audit logging and cost tracking on every single request. SnapSiteBuild designs, builds and deploys that gateway inside your own infrastructure, exposes it through one OpenAI-compatible endpoint, and gives you per-department visibility — so company AI access is governed, safe and cost-controlled without slowing developers down.
Most organisations don't have an AI strategy problem; they have an AI sprawl problem. ChatGPT in marketing, Claude in engineering, a Gemini key in finance, raw API keys buried in side projects. Nobody owns access, nobody sees the bill, and sensitive data leaves through every door. An enterprise AI gateway replaces that sprawl with one governed entry point for the whole company.
What an AI gateway actually does
An AI gateway — also called an LLM gateway or AI API gateway — is middleware that every AI request passes through. A prompt from a person, app or agent reaches the gateway first; it applies policy, forwards an approved request to a model, and passes the response back the same way. Because it sits on the route of every call, it is the one place to write a rule once and have it apply everywhere.
That single choke point is what makes AI governance practical. Instead of policing usage across many tools and accounts, you control identity, spend, safety and routing in one service — and keep a complete record of what was asked, by whom, against which model, and at what cost.
Gateway modules and what each one does
The platform is modular. Each capability can be switched on per department, per team or per API key, so a finance group and an engineering group can run very different policies through the same gateway.
| Module | What it does |
|---|
| Auth & SSO | Authenticates every user through your existing identity provider (SAML/OIDC single sign-on), maps people to roles, and applies role-based access to models and features. |
| API keys | Issues scoped, revocable keys for applications, services and AI agents — so machine traffic is identified and rate-limited separately from human traffic. |
| Token quotas | Caps consumption with TPM (tokens per minute), RPM (requests per minute), and daily and monthly budgets, set per user, key or department. |
| Model router | Sends each request to the right model — a local LLM, a private-cloud model or a premium external API — based on complexity, sensitivity, cost and availability. |
| Prompt firewall | Inspects prompts in flight and blocks injection attempts, jailbreaks, banned topics and policy-violating requests before they ever reach a model. |
| PII redaction | Detects and masks personal and sensitive data (names, emails, card and account numbers, secrets) on the way out, then restores or drops it on the way back. |
| Audit logs | Writes an immutable record for every request — user, key, model, tokens, decision and timestamp — for security review, incident response and compliance. |
| Cost dashboard | Tracks spend and usage in real time, broken down by department, model and user, with blocked-prompt and quota-exhaustion reporting. |
| Developer API | Exposes an OpenAI-compatible endpoint so existing SDKs and tools work by changing only the base URL and key — no application rewrites. |
Who it's for
The gateway is built for the people now accountable for AI: platform and engineering leads who need one integration point, security and compliance teams who need control and an audit trail, and finance leaders who need the bill visible and capped. It governs three kinds of traffic at once — employees using an internal AI portal, applications calling models programmatically, and autonomous AI agents that perform real work through scoped keys.
How a request flows through the gateway
Every call follows the same five steps, so policy is never optional and never depends on the calling app behaving well:
- Authenticate & authorise. The request is tied to a verified user or API key, and the gateway checks the caller is allowed to use the requested model.
- Check quotas. TPM, RPM, daily and monthly limits are evaluated; an over-budget caller is rejected with a standard HTTP 429 instead of running up an invisible bill.
- Firewall & redact. The prompt firewall screens for injection and policy violations, and PII redaction masks sensitive fields before anything leaves your boundary.
- Route. The model router picks the best destination — local for simple or sensitive work, premium external for the hardest requests.
- Record. The request, decision, token count and cost are written to the audit log and reflected in the cost dashboard in real time.
Token quotas: how TPM, RPM, daily and monthly limits work
Quotas make AI spend predictable. RPM (requests per minute) and TPM (tokens per minute) protect against runaway loops and abuse in the moment; daily and monthly budgets cap total consumption over time. Each limit attaches to a user, an API key or a department, and limits stack — a developer key might allow 200 requests per minute but still sit under its team's monthly token budget. When a limit is hit, the gateway returns a clear quota error, so an over-eager agent or misconfigured script is contained instead of generating a surprise invoice.
Model routing: choosing local vs cloud
Model routing decides where each request runs. The router scores complexity, data sensitivity, latency and cost, then sends the request to the cheapest destination that can do the job well. Routine classification, summarisation and internal Q&A go to a private local LLM on your own infrastructure, keeping sensitive data in-house at near-zero marginal cost. Only genuinely hard requests fall through to a premium external model — and even those pass the firewall and redaction layer first. Knowledge-heavy questions route into a RAG knowledge base so answers are grounded in your own documents rather than guessed.
Prompt firewall and PII redaction
A prompt firewall is a security layer for the prompt itself. It inspects every incoming request for injection and jailbreak patterns, blocked topics, and attempts to leak secrets or override system instructions — and refuses the unsafe ones before a model sees them. PII redaction works alongside it: configurable detectors mask personal and confidential data leaving your network, and the gateway restores or strips those values in the response, depending on policy. Because the rules live in the gateway, every app and agent inherits them automatically, and you can validate them as part of AI testing and red-teaming before go-live.
One OpenAI-compatible endpoint for every team
The gateway speaks the OpenAI API format, so adoption is a configuration change, not a migration. Teams point their existing SDKs at the gateway's base URL with a company-issued key, and everything keeps working — while quotas, firewall, routing, redaction and logging now apply automatically. Full developer access includes scoped keys, usage endpoints and per-department reporting, so each team sees its own consumption and the platform owner sees the whole picture. Per-department usage means you can answer, at any time, which group is spending what, on which models, and why.
Why teams trust the SnapSiteBuild gateway
- It runs in your environment. The gateway and its policies are deployed inside your own cloud or data centre, so model traffic, prompts and logs stay under your control rather than a third party's.
- It is governance by default, not by hope. Because policy is enforced on the request path, there is no way for an app or agent to opt out of auth, quotas, the firewall or the audit log.
- The evidence is built in. The audit log and cost dashboard are the proof — every decision, token and dollar recorded and attributable, exactly what security reviews and compliance frameworks ask for.
- It is testable. Firewall rules, redaction and routing are validated with a dedicated test suite before production, so safety controls are verified rather than assumed.
- Built by an AI Centre of Excellence. SnapSiteBuild, founded by Krish Chimakurthy, designs the gateway as part of a wider AI operating layer that fits your identity, data and infrastructure rather than forcing a rebuild.
Frequently asked questions
- What does an enterprise AI gateway do?
- It centralises and governs all AI access. Every prompt from a user, app or agent passes through one service that handles authentication, token quotas, model routing, a prompt firewall, PII redaction, audit logging and cost tracking — turning ungoverned, invisible AI usage into controlled, measurable usage.
- How do token quotas and RPM limits work?
- Quotas cap consumption at several levels. RPM (requests per minute) and TPM (tokens per minute) throttle bursts in real time, while daily and monthly budgets bound total spend. Each limit can be set per user, per API key or per department, and they stack — so an individual key is rate-limited and still counted against its team's overall budget. When a limit is reached, the gateway returns a standard quota error rather than continuing to bill.
- What is a prompt firewall?
- A prompt firewall is a safety layer that inspects prompts before they reach a model and blocks unsafe ones — prompt injection, jailbreaks, banned topics and attempts to leak secrets or override instructions. It works with PII redaction to mask sensitive data in flight. Because it lives in the gateway, every application and agent is protected by the same rules without each team reimplementing them.
- How does model routing choose between local and cloud models?
- The router scores each request on complexity, data sensitivity, latency and cost, then routes it to the best destination. Simple or sensitive requests go to a local LLM on your own hardware; only the hardest fall through to a premium external model, which still passes the firewall and redaction layer first. You set the policy, so routing reflects your security and budget priorities, not a vendor's defaults.
- How are AI costs controlled?
- Three ways at once: quotas cap how much any user, key or department can consume; routing pushes cheap and sensitive work to local models and reserves premium APIs for requests that truly need them; and the cost dashboard makes spend visible per department, model and user in real time. Together they replace one opaque bill with predictable, attributable, capped spend — the core of LLM cost control.
- What about audit logs and compliance?
- The gateway writes an immutable audit record for every request — user or key, model, token count, firewall and routing decisions, and a timestamp. That trail supports security investigations, incident response and compliance reporting, and with SSO, role-based access and PII redaction it gives auditors a defensible account of how AI is used across the company.
Ready to put one secure, governed door in front of all your company's AI? Talk to SnapSiteBuild and we'll design an enterprise AI gateway around your identity provider, models, data boundaries and budget.
Contact SnapSiteBuild: Customersupport@snapsitebuild.com