Build vs Buy: Should You Code Your Own LLM Gateway?

In the rapidly evolving world of Generative AI, LLM Gateways are becoming critical infrastructure for companies integrating large language models into their workflows. These gateways act as a unified control layer, providing model routing, observability, policy enforcement, and cost management across multiple LLM providers.

When CTOs, AI leads, and innovation teams face the question — “Should we build our own LLM Gateway or buy an existing solution?” — the answer isn’t always obvious.

While building internally may seem appealing for customization and control, the reality often involves hidden costs, longer timelines, and missed opportunities for scalability.

In this article, we’ll break down the Build vs Buy decision for an LLM Gateway, drawing on insights from enterprise teams in sectors like banking, telecom, and technology.

What Is an LLM Gateway?

An LLM Gateway is a middleware layer between your applications and large language model APIs. It allows organizations to:

• Route requests intelligently between multiple LLM providers (e.g., OpenAI, Anthropic, Cohere).

• Apply usage policies and governance controls.

• Enable observability to track performance, cost, and reliability.

• Manage security and compliance, such as GDPR and internal AI ethics policies.

• Provide a single API for different models, reducing integration complexity.

For enterprises, especially those operating in regulated industries, LLM Gateways are more than a convenience — they’re a necessity for safe, scalable AI adoption.

The Appeal of Building Your Own LLM Gateway

At first glance, building your own gateway in-house has some attractive advantages:

1. Full Customization

You control every aspect of the gateway — from routing logic to UI features. You can tailor it to your exact workflow, integration stack, and compliance requirements.

2. Ownership of the Codebase

By building in-house, you own the intellectual property, which can be an advantage if you’re developing proprietary orchestration methods or security features.

3. Tight Integration with Existing Systems

Internal teams can directly align the gateway’s architecture with legacy systems, existing APIs, and your internal data governance model.

The Hidden Costs of Building In-House

However, these benefits often come with significant trade-offs — especially for teams without deep AI infrastructure expertise.

Time to Market

Even with an experienced development team, creating a production-grade LLM Gateway can take 6–12 months. This delays AI adoption and competitive advantage.

Ongoing Maintenance

AI APIs, models, and regulations evolve rapidly. An in-house gateway will require constant updates, bug fixes, and compliance checks, diverting engineering resources from core business priorities.

Security & Compliance Overhead

Building robust audit logging, policy enforcement, and encryption layers in-house isn’t trivial — especially if you’re in banking, healthcare, or telecom where data governance is critical.

Scaling Challenges

Supporting multiple teams, regions, and LLM providers introduces complexity in load balancing, failover, and routing optimization.

Total Cost of Ownership (TCO)

While building may seem cheaper upfront, hidden costs like developer salaries, infrastructure, security audits, and downtime can make it far more expensive long-term.

The Case for Buying an LLM Gateway

Enterprise-ready LLM Gateways, such as those from specialized vendors, offer ready-to-deploy solutions with built-in features, support, and scalability.

Faster Deployment

Instead of spending months coding, you can integrate a production-ready LLM Gateway in days or weeks.

Enterprise-Grade Security

Vendors often provide pre-built compliance certifications (GDPR, SOC 2, ISO 27001) and advanced encryption protocols out of the box.

Cost Predictability

You pay a clear subscription or usage-based fee without hidden development and maintenance costs.

Continuous Innovation

Vendors keep up with model releases, API changes, and new governance requirements, so you don’t have to.

Advanced Features Without Extra Effort

Pre-built gateways often include:

• Multi-LLM routing & smart orchestration

• Cost optimization tools

• Policy enforcement dashboards

• Observability & analytics

• Fine-grained access controls

Real-World Insights: Why Even Top-Tier Teams Buy

According to several CTOs in large banks and telecom companies, the decision to buy instead of build comes down to opportunity cost:

“Our AI engineering team is highly capable, but every month spent building infrastructure is a month we’re not delivering AI products to our customers.” — CTO, Global Telecom

Another hidden factor is regulatory pressure. In industries like finance, regulators demand provable governance for AI decision-making. Pre-built LLM Gateways already have audit logs, guardrails, and compliance workflows, which can take months to replicate internally.

Cost & Time Comparison: Build vs Buy

Factor	Build In-House	Buy Off-the-Shelf
Time to Deploy	6–12 months	1–4 weeks
Initial Cost	High (salaries, infrastructure)	Medium (subscription/licensing)
Maintenance	Ongoing, internal team required	Included in vendor package
Feature Updates	Manual development	Continuous vendor releases
Compliance	Custom, time-intensive	Pre-certified & regularly updated
Scalability	Complex to manage	Built-in scalability features

A Hybrid Approach: The Best of Both Worlds?

Some organizations choose a hybrid approach — starting with a vendor solution to get immediate functionality, while slowly building internal capabilities for highly specific needs.

For example:

• Use a vendor LLM Gateway for production workloads.

• Develop custom routing modules internally for unique optimization logic.

This approach minimizes time-to-market while still allowing long-term customization.

Decision Framework: Build vs Buy for Your LLM Gateway

When deciding, ask yourself:

• Do we have dedicated AI infrastructure engineers with bandwidth to maintain this long-term?

• Is time-to-market critical for competitive advantage?

• Are we operating in a highly regulated industry where compliance is non-negotiable?

• How quickly will our LLM usage scale across teams and regions?

• Can we afford to delay deployment for 6+ months?

If the answer to most of these points leans toward speed, compliance, and scalability, then buying is usually the smarter choice.

Final Thoughts

The Build vs Buy decision for an LLM Gateway ultimately comes down to balancing control against time, cost, and risk. While in-house development offers flexibility, the hidden costs — in both resources and opportunity — can outweigh the benefits for most organizations.

For enterprises in sectors like banking and telecom, the trend is clear: buying a ready-made LLM Gateway delivers faster ROI, stronger compliance, and the agility to keep up with AI’s rapid evolution.

📌 Pro Tip: Before committing, run a 2-week pilot with a vendor solution. This gives your technical team hands-on experience and a clear benchmark for evaluating whether building in-house is worth the investment.