LLM Inference as-a-Service vs. Self-Hosted: Which is Right for Your Business

Large Language Models are transforming AI applications, from automating customer interactions to optimizing workflows. Choosing between LLM Inference as-a-Service and self-hosting is a critical decision that impacts cost, performance, and security.

This article breaks down key factors:

1. Strategic role: Is AI core to your competitive edge or a supporting tool?
2. Cost efficiency: Upfront infrastructure vs. pay-as-you-go models,
3. Performance and Scalability: Latency, customization, and control trade-offs,
4. Security & Compliance: Data privacy concerns with cloud vs. on-prem solutions.

We will compare top LLM providers and deployment options to help you determine the best fit for your business. 

TL;DR & Key Takeaways

Choosing between LLM Inference as-a-Service and self-hosted LLMs depends on your long-term vision, scale, and regulatory requirements. Our findings, which are detailed in the following paragraphs of this article, are as follows:

• For flexibility and cost-efficiency with low or irregular traffic, LLM-as-a-Service is the best choice.
• For high-volume usage and full control, self-hosting (either on cloud or on-premise) can be more cost-effective but requires a strong engineering team.
• Security & compliance concerns? On-premise deployment ensures complete control over data.
• MLOps expertise is essential regardless of your approach—whether through in-house engineers or external experts.
• A hybrid approach may offer the best of both worlds, combining cloud-based inference for general applications with self-hosted models for sensitive or specialized use cases.

Understanding LLM Inference as-a-Service

LLM Inference as-a-Service offers a convenient, scalable, and maintenance-free approach, allowing companies to access cutting-edge models via cloud providers. This option is particularly appealing for businesses looking to quickly integrate AI capabilities without the overhead of managing complex infrastructure.

Several major players in the AI space offer LLM Inference as-a-Service:

• OpenAI: Known for its GPT series, OpenAI provides API access to its models, enabling businesses to leverage state-of-the-art language understanding and generation capabilities.
• Google Cloud AI: With models like Gemini, Google offers robust AI services through its cloud platform, integrated with other Google Cloud products.
• Microsoft Azure: Azure provides access to various LLMs, including those developed in collaboration with OpenAI, through its AI services.
• Amazon Web Services (AWS): AWS offers a range of AI and machine learning services, including access to LLMs through its SageMaker platform and Amazon Bedrock.

From our commercial experience, this is usually how companies start to interact with LLMs in their products. Invoking OpenAI or Amazon Bedrock endpoints, typically through a proxy server such as LiteLLM is a very quick way to get you started.

Let’s now introduce the alternative approach i.e. self-hosted LLM deployments.

Exploring Self-Hosted LLM Inference (including Cloud)

What we mean by self-hosting Large Language Models is the kind of deployment where you are at least partially responsible for the underlying infrastructure and have some control over it.

May it be fully on-premise hardware (e.g. an HPC cluster in your data center), cloud instances whether individual ones (such as Amazon EC2) or forming a Kubernetes cluster built on top of Amazon’s Elastic Kubernetes Service.

While there are still some differences between aforementioned approaches, transition between them (e.g. EC2 vs. EKS) is often a fluid, non-strategic decision, thus we decided to consider them as a single approach, in opposition to API-based LLM Inference.

That being said, self-hosted LLM inference gives you more control over their AI models and the underlying hardware, allowing you to optimize the deployment to meet specific needs. While offering significant flexibility and ownership, this approach requires more planning and maintenance to be able to reliably deploy and serve large models in production, we are speaking of:

• Hardware availability: High-performance computing resources, such as GPUs or TPUs, are essential for efficient LLM inference. Even in cloud-based setup, desired hardware is not always available especially for larger models or higher expected traffic volumes.
• Drivers and frameworks: While most often instances will already have preinstalled drivers, there is still some maintenance required to keep them up-to-date and to choose the right tooling for model serving (e.g. vLLM, TGI) or observability stack.
• Storage and networking: Sufficient storage capacity is needed to handle large datasets and model checkpoints. Fast storage solutions, such as NVMe SSDs, can help reduce latency and improve inference speed. High-speed networking infrastructure is crucial to ensure low-latency communication between components and to handle large data transfers efficiently.
• MLOps expertise: Whether it is a maintenance of cloud architecture or Kubernetes cluster or the right choice of tooling for model inference and monitoring, you may need to build MLOps expertise in your team first before you decide to self-host LLMs.

This infrastructure and maintenance overhead has to be taken into account when making a decision. In further sections we will delve deeper into several key aspects where as-a-service and self-hosted approaches differ the most and often these differences play a key role in choosing the direction.

Comparing Costs

When evaluating the cost of LLM Inference as-a-service and self-hosted AI models, several factors come into play. Let’s take a closer look at the most important cost considerations for each approach.

The most important difference is how charging works. Namely, most LLM as-a-service providers offer a pay-per-use model, where you are charged based on the number of API calls or the volume of data processed. This allows for flexibility and scalability, as you only pay for what you use.

There are no upfront costs related to buying expensive hardware, you also do not pay when models are not being used which is perfect for small, irregular traffic patterns, characteristic to small scale applications or R&D experiments.

On the other hand, self-hosting Large Language Models uses either on-premise hardware that is a very significant upfront cost, or relies on GPU cloud instances (e.g. A100, H100) which are not always available and usually can be also billed when in idle mode. In addition to that, both on-premise and cloud-based approaches often require a dedicated team of MLOps experts and cloud or infrastructure specialists to properly configure, secure and maintain the hardware.

However, it doesn’t mean that LLM Inference as-a-service is always a better investment. It certainly will be for businesses with variable or unpredictable usage, especially with small volume of requests. However, for organizations with high-volume, consistent usage and specific customization needs, self-hosting may be more cost-effective in the long run.

Performance and Scalability

One of the key advantages of LLM Inference as-a-Service is its ability to scale seamlessly. LLM providers typically have vast infrastructure and resources, allowing them to handle sudden spikes in demand or accommodate growing usage needs. This eliminates the need for businesses to worry about provisioning additional hardware or managing infrastructure scaling.

In our opinion this is the best way to start when just exploring LLM applications and testing different models or when traffic requirements are not known yet. This is possible because popular LLM providers have redundant systems, failover mechanisms, and robust monitoring in place to minimize downtime and ensure a smooth user experience.

Scaling self-hosted AI models can be more challenging. For self-hosted solutions on the cloud, there are ways for infrastructure to scale with service usage based on a variety of metrics (for example, AWS EC2 Autoscaling). Still, these features do not come configured out-of-the-box and require additional effort to set up.

In case of on-premise infrastucture, your usage grows, you need to manually provision additional hardware, manage load balancing, and ensure proper resource allocation. While some of these problems can be solved by leveraging technologies like Kubernetes, it requires expertise and effort from your team to design and maintain such infrastructure.

If you prioritize ease of scaling and reliable performance without the need for extensive customization, LLM Inference as-a-Service may be the preferred option.  However, if you require fine-grained control over the models and have the resources and expertise to manage the infrastructure, self-hosting can provide the most flexibility and customization. Alternatively, you might consider using self-hosting on cloud infrastructure, which offers some opportunity for configuration and control but not nearly as much as managing on-premise hardware.

Security and Compliance

The choice between LLM Inference as-a-Service and self-hosted AI models from a security and compliance perspective depends on the specific requirements and constraints of your business. 

If you operate in a highly regulated industry or have strict data sovereignty requirements, cloud or on-premise deployment may provide the necessary control and flexibility. In some very strict cases on-premise setup with data remaining in your internal network might be the only option.

However, if you are comfortable with the shared responsibility model and the security measures implemented by cloud and LLM providers, they can offer a more convenient and scalable option while still maintaining a robust security posture.

Flexibility and Customization

When it comes to flexibility and customization, self-hosted AI solutions often have the upper hand compared to LLM Inference as-a-Service offerings. Let’s explore the differences in more detail.

Self-Hosted AI (on-premise and cloud):

1. Model Selection: With self-hosted AI, businesses have the freedom to choose and deploy any LLM that suits their specific requirements. They can opt for open-source models like LLaMa, Mistral, deepseek, or even custom-trained models tailored to their domain or industry.
2. Control and Customization: With self-hosted AI, businesses have complete control over the model’s hyperparameters, architecture, and deployment settings. They can experiment with different configurations, optimize performance, and tailor the model to their specific needs.

LLM Inference as-a-Service:

1. Limited Model Selection: LLM providers typically offer a limited set of pre-trained models for inference. While these models are often state-of-the-art and highly capable, businesses may not have the option to choose a specific model architecture or variant that aligns perfectly with their requirements.
2. Vendor Lock-in: When relying on a specific LLM provider, businesses may face vendor lock-in concerns. Migrating to a different provider or switching to a self-hosted solution in the future could involve significant effort and code modifications.
3. Limited Customization Options: LLM Inference as-a-Service platforms often have limited customization options. Businesses may not have the ability to fine-tune the model, modify its architecture, or incorporate custom preprocessing steps beyond what the provider allows.

Ultimately, the choice between self-hosted AI and LLM Inference as-a-Service depends on the level of flexibility and customization required by the business. If a high degree of control, customization, and integration flexibility is crucial, self-hosted AI may be the preferred option. However, if ease of use, rapid deployment, and access to state-of-the-art models are the primary priorities, LLM Inference as-a-Service can be a suitable choice.

Making the Decision

While the decision depends on many factors, business context, scale and priorities, there are some guidelines we provide to help you make the best decision. So here are our key conclusions:

1. If LLMs are a core part of your future vision, investing in self-hosted solutions may provide long-term benefits. However, if LLMs are primarily used for specific applications with small volume of traffic, the flexibility and convenience of LLM Inference as-a-Service may be the more suitable and cost effective choice,
2. Many cloud and LLM providers offer convenient, robust and reliable security measures for your LLM needs. However, if working with strict data policies and regulations, on-premise deployment is the way,
3. LLM-as-a-service, self-hosted on cloud or on-premise – no matter the approach, MLOps expertise plays a crucial role when both designing and implementing the solution. Make sure to have access to reliable engineering staff – in the form of -as-a-service experts or your own team.

Now this is a summary of our comparison sections, presented in the form of a table:

	LLM Inference as-a-Service	Self-hosted LLMs
		Cloud	On-Premise
Costs	lower upfront costs, more efficient for low, irregular traffic	lower upfront costs, more efficient for low, irregular traffic	initial costs for infrastructure and engineering team; can be more cost-effective in the long run, especially for high-volume usage
Performance and Scalability	automatically scales to meet demand, often by default optimized for fast inference times	requires some setup for handing increased usage, this functionality is provided by other cloud services	requires manual setup (for scaling, autobalancing, caching), bound by available hardware (including cloud instances)
Security and Compliance	providers usually have robust security measures but you should carefully review their policies	more control over data handling, data remains in your private cloud network (VPC)	complete control over data handling, data remains in your own private company network
Flexibility and Customization	streamlined LLM deployment leaves less space for customization or model choice	full flexibility in model selection, inference engines and infrastructure setup

Prior to choosing the approach, always evaluate potential LLM Inference as-a-Service providers based on their reputation, pricing models, and the range of features offered. For self-hosted AI, assess your team’s capabilities and the resources required to effectively deploy and maintain the infrastructure.

In some cases, a hybrid approach that combines elements of both LLM Inference as-a-Service and self-hosted AI may be the optimal solution. This could involve using LLM Inference as-a-Service for certain applications while self-hosting models for more sensitive or specialized use cases.

LLMs Are Evolving Fast—Let’s Find the Right Strategy for You!

As a final word, please keep in mind that the field of Large Language Models is still very dynamic and there are still new options, providers and frameworks in the wild. If you still struggle to make a decision for your company, don’t hesitate to contact us and let’s have a talk.

Our team of MLOps experts has worked with many companies before, helping them assess their needs and providing detailed recommendations on the best way to use, train and deploy LLMs. If this is what you are looking for, contact us!