TLDR: The Secure AI Inference Gateway is a turnkey AWS infrastructure platform (Terraform module on ECS Fargate) that acts as a managed proxy between AI tools/frameworks and inference providers. It solves two key problems for regulated industries:
- Resilience — Load balances across AWS, GCP, and Azure so a single provider outage doesn’t halt business operations.
- Governance — Provides PII protection (3 layers of guardrails), budget enforcement, audit trails, zero internet exposure, and supply chain security out of the box.
It exposes an OpenAI-compatible API, so it works with GitHub Copilot, Claude Code, LangChain, CrewAI, etc. Teams self-service their own keys post-deployment, and the whole stack runs in private subnets with no public internet exposure.
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AI, AI and more AI
Agentic workflows are quickly becoming integral parts of core business services, sitting in the critical path of many processes and procedures that glue a business together day to day. As LLM usage matures the criticality and reliance on these workflows will increase, as will the required level of governance and fault-tolerance.
You can’t have a core business process come screaming to a halt because of an inference provider outage. With the amount of flows these agentic agents process, losing one for a day could be seen as a team calling in sick for an entire week. Not only does the team’s work not get completed but everything that flows through them grinds to a halt.
A barrier to even implementing Agentic Workflows is the ever increasing governance footprint, PII and Data Sovereignty requirements for the information that make these systems tick. Public Sector, Finance and Health Care are primary industries that can be uplifted by Agents, but they are also the most heavily regulated. You need a system that can provide fault-tolerance, load balancing, cost tracking, guardrails and PII protection as well as the governance requirements all before you start even thinking about what workflows you can augment with AI.
The Secure AI Inference Gateway is our solution to the problem, a turnkey infrastructure platform built on AWS. It provides the resilient, governed and sovereign access to AI Inference these agentic workloads required, all of the data stays in control of the organisation.
The Solution – A Secure AI Inference Gateway
The Secure AI Inference Gateway is a managed inference proxy built on open-source (MIT Licensed) software, running on an auto-scaled ECS Fargate service. It presents an OpenAI compatible API endpoint, ready to be consumed by nearly all modern AI tools and frameworks, including Github Copilot, Claude Code, LangChain, CrewAI and any other frameworks made with the OpenAI SDK.
The AI Gateway is packaged into a Terraform Module for easy and fast deployment into existing AWS Landing Zones. It utilises extenders to connect into a multitude of existing IdP solutions for federated access right out of the box. Clients select their models, guardrails, budgets and access patterns. Post deployment teams self-service their own keys so tickets are not required for day to day operations with the AI Gateway.
Because of where the AI Gateway sits in the system as a whole, between the AI tool/framework and the provider doing the inference, requests can be load balanced across the hyperscalers. Reaching into GCP, Azure and AWS to provide access to the models on their platform, as well as fault tolerance for inference of common models like the Anthropic, OpenAI and Deepseek series. No longer can a single provider error cause your entire business to freeze up, the AI Gateway simply switches to a provider that has availability and capacity.
Built for Governance
The Achilles heel of most Agentic adoptions, the system has been built to comply with many frameworks, not just Essential 8. Out of the box the AI Gateway provides compliance through:
Each of these contributing to making the solution hardened and ready to deploy into highly regulated environments.
The AI Gateway maps to Essentials 8 requirements, covering:
- Application Control (Only signed containers from a centralised ECR repository can be used).
- Patch Management (Fargate handles OS patching. Container images are rebuilt on every LiteLLM release and on critical CVE discovery).
- User Application Hardening (Chainguard base image, non-root execution, read-only filesystem, all Linux capabilities dropped).
- Restricted Admin Permissions (IAM roles scoped to minimum required. No persistent admin access. Virtual keys enforce least-privilege per consumer).
- Multi-Factor Authentication (IAM roles scoped to minimum required. No persistent admin access. Virtual keys enforce least-privilege per consumer) and finally.
- Backups (Configuration in RDS with automated backups. Infrastructure as code in Git. Logs archived to S3)
If we extend the mapping to other security and compliance frameworks, we can verify the AI Gateway has been built Security First and we have been successful in doing that.
When Does It Make Sense?
The Secure AI Inference Gateway isn’t going to be the right solution for every AI workload.
If you are experimenting with AI, building a low-risk internal tool, or simply need access to a model, going directly to a managed inference provider is probably going to be cheaper and easier. There is no point adding another layer of infrastructure if you don’t need the governance, resilience and controls it provides.
Where that starts to change is when AI becomes part of a production workload or starts sitting in the critical path of a business process. If the workload is handling sensitive data, being used across multiple teams, needs strong auditability or simply can’t stop because one inference provider has an outage, the additional infrastructure starts to make a lot more sense.
Of course, all of that comes with a trade-off. The additional security, monitoring and resilience increases the baseline cost of the solution. While it can operate in a reduced non-highly-available configuration for development and testing, for Production environments it is recommended to run the full HA deployment. In that configuration the solution runs at approximately $455–800/month excluding model inference costs.
That trade-off is intentional. The Secure AI Inference Gateway is not trying to be the cheapest way to call an LLM. It is designed for use cases where governance, security, auditability, resilience and operational ownership matter enough to justify the additional infrastructure.
Wrapping Up
If your organisation is looking to take advantage of the AI revolution but is slowed down by governance, PII, data sovereignty, resilience, and operational risk concerns, the Secure AI Inference Gateway is designed to remove that friction.
It gives teams a controlled, scalable, and production-ready path to AI adoption without compromising the security and compliance standards the business depends on. With the right foundation in place, organisations can stop debating whether AI can be used safely and start focusing on where it can deliver value.
If this sounds like the challenge your team is facing, reach out to us, we would be happy to help you explore what a secure AI inference platform could look like in your environment.
Kade Hampson is a Lead Consultant at Cevo Australia, specialising in AWS cloud architecture, landing zones, governance, and platform engineering. He helps organisations design and evolve secure, scalable cloud foundations, with a focus on simplifying complex environments and creating practical, repeatable patterns for teams to build on. Kade is passionate about automation, developer enablement, and using emerging technologies such as AI and agentic workflows to improve how cloud platforms are designed and operated.
