Self-hosted LLMs vs. Cloud APIs: The IT Decision Guide for Medium-sized Businesses 2025

You are facing one of the most important IT decisions of the next few years: How can you bring Large Language Models (LLMs) into your company securely and cost-effectively?

Choosing between self-hosted models and cloud APIs does not just impact your budget—it determines data protection, performance, and how quickly you can deploy AI solutions productively.

As an IT manager, you know the dilemma: Your management expects rapid success with Generative AI. At the same time, customer data must stay protected.

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

This guide gives you the facts you need for a well-founded decision. No marketing talk, just concrete numbers and real-world experience from mid-sized companies.

The two deployment models at a glance

Before we get into the details, let’s clarify the basics. That’s because the concepts of «self-hosting» and «cloud APIs» involve fundamental differences in architecture and responsibility.

Self-hosted LLMs: Complete control, complete responsibility

With self-hosted models, you run the LLM on your own infrastructure. This could be your data center, a private cloud, or a dedicated server at your trusted hosting partner.

You download open-source models like Llama 2, Mistral, or Code Llama and operate them independently. You keep full control over the data, the model, and the infrastructure.

The catch: You also have full responsibility 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 the software-as-a-service principle. You send your requests to the provider’s servers via an interface and receive the answers back.

This means: No hardware investments, no maintenance, no model updates. But also no control over the infrastructure and possible dependency risks on third-party vendors.

Usage is usually charged on a pay-per-use basis. You pay for the tokens actually processed—the word fragments the model handles.

Cost factors in detail

The real costs are often hidden in the details. An honest comparison takes every factor into account—from hardware to personnel effort.

Hardware and infrastructure costs for self-hosting

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

This means: You need several high-end GPUs like the NVIDIA A100 or H100. A single A100 costs about 15,000 euros, an H100 well over 30,000 euros.

Plan for additional costs for server hardware, network equipment, and uninterruptible power supply. For a solid foundation, you should budget at least 100,000 euros.

On top of that are ongoing costs for electricity, cooling, and maintenance. Depending on utilization, that can be another 2,000 to 5,000 euros per month.

API costs and scaling effects

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

For a mid-sized company with moderate usage (around 100,000 requests per month), this results in monthly costs of between 500 and 2,000 euros.

The benefit: Costs scale linearly with usage. You only pay for what you actually use. For self-hosted models, the hardware costs arise regardless of usage level.

But caution: With intensive use, API costs can skyrocket quickly. From around 10,000 euros per month in API costs, self-hosting becomes financially appealing.

GDPR, works councils, and customer data: Legal realities

For German companies, data protection is non-negotiable. The GDPR has been in effect 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 remains in your infrastructure. This meets the strictest data protection requirements and gives you full control over processing and storage.

You can precisely define which data the model sees and how long it is stored. For industries with special compliance requirements—like banking or healthcare—this is often the only viable option.

However, you are also fully responsible for secure implementation. This includes encryption, access control, and audit logs.

Cloud APIs: Trust in third parties

With cloud APIs, you transfer data to third-party providers. This requires a careful review of privacy statements and data processing agreements.

Major providers such as OpenAI, Anthropic, and Google provide corresponding contracts and relevant information. For example, OpenAI states that data from API requests is not used to train their models.

However, you’ll still need to convince your works council and data protection officer. This may take time and often requires additional security measures, such as anonymizing customer data.

For many mid-sized companies, this is a knockout criterion—at least for solutions involving sensitive data.

Comparing performance and availability

The best technology is useless if it’s unavailable or too slow to respond. Here there are clear differences between the two approaches.

Cloud APIs generally offer very high availability and are actively supported by the provider. In the event of failures, the provider handles the solution. You have no maintenance windows and don’t need to worry about updates.

Latency depends on your internet connection and geographic 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. With local hardware, you can achieve minimal latencies under 100 milliseconds.

However, you must ensure high availability yourself. This means redundant hardware, backup systems, and a well-drilled operations team. For many mid-sized IT departments, this is a major challenge.

Another aspect: Self-hosted models often run slower than their cloud counterparts. While GPT-4 operates on extremely powerful infrastructure, you’re limited to the hardware your budget allows.

What does your team really need?

The technical complexity differs significantly between both options. Be honest: What can your team manage?

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

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

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

There’s also operational overhead: monitoring, logging, backup, and recovery. If your LLM goes down at 3am, someone on your team must handle it.

Many companies underestimate this point. Running an LLM in production is more than just a proof of concept. It requires the same professionalism as your other mission-critical systems.

Four decision scenarios for IT managers

After years in consulting, we see the same patterns again and again. Your situation determines the best approach.

When self-hosting makes sense

Scenario 1: Strict compliance requirements

You work in a regulated industry or have clients with special data protection needs. In this case, self-hosting is often the only option.

Scenario 2: High usage volumes

You expect to spend over 10,000 euros per month on API costs or have consistently high request volumes. At this point, owning your own hardware becomes economical.

Scenario 3: Strong ML team

Your team already has experience with machine learning operations and GPU computing. You can handle the complexity and benefit from full control.

When cloud APIs are the better choice

Scenario 4: Quick start desired

You want to have your first applications in production within weeks. Cloud APIs enable the fastest entry with no infrastructure investment.

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

One important note: Don’t start with the technology, start with the business value. Which processes do you want to improve? Which time savings are realistic?

Only when you have clear answers does it make sense to decide on the infrastructure. Too often we see companies getting bogged down in technical details and losing sight of the real benefits.

The best of both worlds

The decision doesn’t have to be binary. Hybrid approaches combine the benefits of both models and reduce risk.

A proven approach: Start with cloud APIs for prototyping and less critical applications. In parallel, build know-how and infrastructure for self-hosting.

This way, you can process sensitive data on-premises while using the scalability of the cloud for standard tasks. Modern AI orchestration tools support exactly these multi-model architectures.

Another strategy: Use cloud APIs for development and switch to self-hosting for production. That reduces vendor lock-in risk and gives you flexibility.

Key point: Plan for portability from the beginning. Use standardized APIs and avoid provider-specific features that make a later switch more difficult.

One thing is certain: The LLM landscape is evolving rapidly. What’s the best solution today may be outdated in a year. Flexibility is your greatest asset.

Frequently Asked Questions