Self-Hosted LLMs vs. Cloud APIs: The 2025 IT Decision-Maker’s Guide for SMEs

You’re facing one of the most important IT decisions of the coming years: How can you securely and cost-effectively integrate Large Language Models (LLMs) into your business?

The choice between self-hosted models and cloud APIs impacts more than just your budget. It determines data protection, performance, and the speed at which you can put AI applications into productive use.

As an IT leader, you know the dilemma: Management expects quick wins from generative AI, yet customer data must never fall into the wrong hands.

The good news: Both approaches have their merits. The bad: A poor decision could cost you time, money, and possibly the trust of your stakeholders.

This guide puts the facts at your fingertips so you can make an informed decision. No marketing fluff—just concrete figures and real-world experience from the SME sector.

Overview of the Two Deployment Models

Before we get into the details, let’s clarify the basics. “Self-hosting” and “cloud APIs” fundamentally differ in terms of architecture and responsibility.

Self-hosted LLMs: Complete Control, Complete Responsibility

With self-hosted models, you run the LLM on your own infrastructure. This could mean your own data center, a private cloud, or a dedicated server hosted by your trusted provider.

You download open-source models such as Llama 2, Mistral, or Code Llama and run them independently. This way, you retain full control over your data, models, and infrastructure.

The catch: You’re also fully responsible for updates, security, and performance.

Cloud APIs: Simplicity at the Cost of Dependency

Cloud APIs like OpenAI GPT-4, Anthropic Claude, or Google Gemini operate on a Software-as-a-Service principle. You send requests to the provider’s servers via an API and receive the answers in return.

That means: No hardware investments, no maintenance, no model updates. But you also don’t have any control over the infrastructure—and you might become dependent on third parties.

Usage is typically billed on a pay-per-use basis. You pay for the tokens actually processed—in other words, the word fragments handled by the model.

Cost Factors in Detail

The real costs are often hidden in the details. An honest comparison weighs all factors—from hardware to manpower.

Hardware and Infrastructure Costs for Self-hosting

To run productive LLM applications, you need high-performance hardware. A model like Llama 2 with 70 billion parameters requires at least 140 GB of VRAM to operate.

This means: You’ll need several high-end GPUs such as NVIDIA A100 or H100. A single A100 is priced around €15,000, while an H100 is over €30,000.

Budget additionally for server hardware, network equipment, and uninterruptible power supply. As a solid starting point, you should plan for at least €100,000.

On top of that, you have recurring costs for electricity, cooling, and maintenance. Depending on workload, this could mean an extra €2,000 to €5,000 per month.

API Costs and Scaling Effects

Cloud APIs charge transparently based on usage. Prices for models like OpenAI GPT-4, for example, are around $0.03 per 1,000 input tokens and $0.06 per 1,000 output tokens.

For a mid-sized business with moderate use (roughly 100,000 queries per month), this equates to €500 to €2,000 monthly.

The advantage: Costs scale linearly with usage. You pay only for what you actually consume. With self-hosted models, hardware costs are incurred regardless of utilization.

But beware: With heavy use, API costs can skyrocket. Once monthly API spend hits around €10,000, self-hosting starts to become economically attractive.

GDPR, Works Councils, and Customer Data: Legal Realities

For German businesses, data protection is not negotiable. The GDPR has been in force since 2018, and its requirements are clear: You must know where your data is and how it is processed.

Self-hosting: Maximum Control, Maximum Responsibility

With self-hosted models, all data stays within your infrastructure. This meets the strictest data protection standards and lets you exercise full control over processing and storage.

You can specify exactly which data the model sees and how long it is stored. For industries with special compliance demands—like banking or healthcare—this is often the only viable route.

However, you also bear full responsibility for secure implementation. This includes encryption, access control, and audit logs.

Cloud APIs: Trusting Third Parties

With cloud APIs, you share data with third-party providers. This requires careful review of privacy policies and data processing agreements.

Major vendors such as OpenAI, Anthropic, and Google provide the necessary contract documents and information. For example, OpenAI states that API request data is not used for model training.

Nevertheless, you’ll have to convince your works council and data protection officer. This can take time and often requires additional security measures such as customer data anonymization.

For many mid-sized companies, this is a dealbreaker—especially for applications involving sensitive data.

Comparing Performance and Availability

The best technology is worthless if it’s unavailable or too slow. Here, significant differences emerge between the two approaches.

Cloud APIs generally offer very high availability and are actively managed by the provider. If there’s an outage, the provider handles it. You have no maintenance windows and don’t need to worry about updates.

Latency depends on your internet connection and physical proximity to the data center. Typical response times are between 500 milliseconds and 3 seconds—depending on request complexity.

With self-hosted models, you have full control over performance and availability. Local hardware lets you achieve latencies under 100 milliseconds.

However, ensuring high availability is up to you. That means redundant hardware, backup systems, and a well-drilled operations team—a major challenge for many mid-sized IT departments.

Another factor: Self-hosted models often run slower than their cloud-based counterparts. While GPT-4 is deployed on extremely powerful infrastructure, your own setup is limited to what your budget allows.

What Does Your Team Really Need?

The technical complexity varies greatly between the two approaches. Be honest: What can your team handle?

For cloud APIs, you mainly need developers with API experience. Integration is usually possible within days. A simple Python client or REST API call is enough to get started.

This changes with more complex applications. RAG (Retrieval Augmented Generation) systems or fine-tuning require deeper ML expertise—regardless of deployment model.

Self-hosting requires much more technical know-how. You’ll need specialists for GPU computing, container orchestration with Kubernetes or Docker, and model optimization.

There’s also the operational side: monitoring, logging, backup, and recovery. If your LLM goes down at 3 a.m., someone on your team needs to fix it.

Many companies underestimate this aspect. Running an LLM productively is much more than a proof of concept. It demands the same professionalism as any other business-critical system you operate.

Four Decision Scenarios for IT Leaders

After years of consulting, we’ve seen the same patterns time and again. Your unique situation will determine the best approach.

When Self-hosting Makes Sense

Scenario 1: Strict Compliance Requirements

You operate in a regulated industry or serve customers with special data protection needs. In that case, self-hosting is often the only way forward.

Scenario 2: High Usage Volume

You anticipate API costs of more than €10,000 per month or expect consistently high query volumes. At this level, investing in your own hardware starts to pay off.

Scenario 3: Experienced ML Team in Place

Your team already has expertise in machine learning operations and GPU computing. If so, you can handle the complexity and reap the benefits of full control.

When Cloud APIs Are the Better Choice

Scenario 4: Need a Quick Start

You want to get your first applications live in a matter of weeks. Cloud APIs enable the fastest go-live without infrastructure investment.

For most mid-sized businesses, we recommend starting with cloud APIs. You can quickly gain experience, validate use cases, and later make a well-informed decision about self-hosting.

An important tip: Don’t start with technology—start with business value. Which processes do you want to improve? What time savings are realistic?

Only when you have clear answers does the infrastructure question make sense. Too often, we see companies get bogged down in technical minutiae and lose sight of real business benefits.

The Best of Both Worlds

The decision doesn’t have to be either-or. Hybrid approaches combine the strengths of both models and minimize risk.

A proven strategy: Start with cloud APIs for prototyping and less critical use cases. In parallel, build up know-how and infrastructure for self-hosting.

This allows you to process sensitive data on-premises while leveraging cloud scalability for standard workloads. Modern AI orchestration tools are designed to support precisely these multi-model architectures.

Another option: Use cloud APIs for development, then switch to self-hosting for production. This reduces vendor lock-in risk and provides flexibility.

One key point: Plan for portability right from the start. Use standardized APIs and avoid provider-specific features that make future migration difficult.

Because one thing’s certain: The LLM landscape is evolving rapidly. What’s best today could be obsolete next year. Flexibility is your greatest asset.

Frequently Asked Questions