Build or Buy GenAI Infrastructure? The Hard-Hitting Business Case for IT Decision-Makers
“We can build an LLM proxy ourselves in a few days using open-source technology.” When new technologies take the market by storm, this phrase becomes an almost reflexive response in IT departments. And, taken in isolation, it’s actually true. A simple API forwarder to OpenAI or Anthropic can be quickly programmed.
However, the reality in companies is different. A production-ready, GDPR-compliant AI ecosystem requires far more than just an interface. It requires user management (SSO), granular budget limits, a tamper-proof billing ledger, automated onboarding and offboarding processes, high availability, and deep cloud integrations.
What does it take to build a GenAI infrastructure from scratch
An enterprise GenAI infrastructure requires an average of 65 to 100 person-days just for the initial development of the control plane when built in-house. Hidden costs for cloud operations, single sign-on, spend ledgers, compliance audits, and annual maintenance add to this. These additional costs amount to approximately €20,000 to €30,000 per year.
Anyone looking to scale GenAI at the enterprise level must ask themselves: Build vs. buy? In this article, we present the raw numbers and calculate the actual total cost of ownership (TCO) of a build-it-yourself solution compared to the turnkey PCG AI Gateway.
The “We’ll Just Build It Ourselves” Illusion: The Hidden Components
Anyone developing their own AI platform from scratch usually underestimates the complexity of the control plane, or the management layer behind the API. Let’s take a detailed look at the effort required for a robust enterprise setup (using GCP, AWS, or Azure as examples):
1. Cloud Infrastructure & Ops (approx. 3–5 person-days)
This includes deploying load balancers, Workload Identity Federation (WIF), managed SQL databases, Cloud Tasks, schedulers, and secure secret management.
2. The Control Plane (approx. 30–45 person-days)
This is the main driver of effort. You’ll need to develop logic for:
- Automated provisioning of API keys.
- A comprehensive spend ledger for accurate tracking of consumption.
- Per-user budgets with automatic email notifications when 50%, 85%, and 95% of the budget is reached.
- Automated onboarding and offboarding synchronized with the HR lifecycle.
3. Dashboard, SSO & Analytics (approx. 10–15 person-days)
Developers and business departments need an interface to manage their API keys and view real-time usage statistics—secured natively via Single Sign-On (SSO), of course.
4. Change Management & Adoption (approx. 10–20 person-days)
A platform is only as good as its adoption. Documentation, enablement workshops, and internal guidelines for the teams consume valuable time.
And the worst part? Deadlocks with cloud providers over the “Production Lessons” quota, complex edge cases involving guaranteed data retention in the EU, and unexpected API changes by model providers typically result in months of trial and error not included in any initial project plan.
DIY vs. PCG AI Gateway: The TCO Comparison at a Glance
When you add up the person-days (PD) in traditional IT project work, a clear picture emerges: An in-house, custom-built solution ties up valuable senior engineering resources that are needed for the core business or building business-critical AI use cases (e.g., automating customer service or data engineering processes).
| Category / Cost Center | In-house build from scratch | With the PCG AI Gateway |
|---|---|---|
|
Development effort |
65–100 person-days |
Live in a few weeks |
|
Annual enterprise license fees |
€20,000 – €30,000 |
€0 (No product lock-in) |
|
Platform characteristics |
Experimental in-house solution |
Tested and production-ready |
|
Ownership |
High internal maintenance effort |
Full ownership in your cloud accounts |
How does the PCG AI Gateway’s FinOps model work
The PCG AI Gateway breaks away from the opaque SaaS licensing models on the market and is based on absolute transparency and predictability:
- One-time setup fee (€19,900): Includes turnkey deployment of the complete Enterprise AI Foundation (the preconfigured control plane) directly within your own cloud infrastructure (AWS, Azure, or GCP). You retain full architectural control—no black box.
- Managed Operations (€3,900/month): PCG handles basic operations, continuous monitoring, security updates, and support (equivalent to approximately 5 person-days of effort per month). This completely relieves your internal ops teams of this burden.
- Optional: Change & Adoption (€9,900): We recommend a comprehensive 8-week training program for your employees to ensure optimal use of LLM models
- LLM Tokens AT COST: This is the biggest lever for your FinOps calculations. Token consumption (whether Gemini, GPT, or Claude) is billed directly through your existing cloud provider accounts without any markup. You benefit directly from your existing enterprise discounts and framework agreements with the hyperscalers.
Buy for the Basics, Build for the Edge
Using existing market infrastructure as a turnkey solution doesn’t create a competitive advantage. It generates technical debt and ties up resources.
The PCG AI Gateway provides you with the regulatory and administrative foundation within a few weeks—secure, GDPR-compliant, and fully budgeted. This frees up your teams to focus on what really matters: developing customized AI solutions based on your company’s own data.
Do the math: Before investing months of development time in a risky in-house project, validate the numbers with us. Schedule a scoping session with the PCG Data & AI Team to determine the most cost-effective rollout strategy for your company.
