LLM APIs vs. Self-Hosted Models: Finding the Best Fit for Your Business Needs

TL;DR

LLM APIs are ideal for:

• Quick Deployment: Great for businesses needing rapid integration of AI features.
• Non-Sensitive Data Applications: Perfect for scenarios where data privacy isn't a primary concern.
• Prototyping and Short-Term Projects: Allows fast experimentation with minimal setup.
• Limited In-House Expertise: A solution for teams without ML expertise.

Self-hosting is ideal for:

• Custom AI Needs: Enables fine-tuning and adaptation for specialised business use cases.
• In-House Resources: Suitable for organizations with the technical expertise and infrastructure to manage models.
• High Privacy and Compliance Needs: Ensures data security and adherence to regulatory requirements.

In summary, choose LLM APIs for ease, speed, and cost effective integration. Opt for self-hosting if your business demands full control, security, and customization.

Introduction

Recent advancements in large language models (LLMs) have given AI applications abilities beyond simple natural language processing tasks. LLMs can process and output information like humans.

These abilities have been particularly helpful to businesses in areas such as customer support, analysing unstructured business data, content creation, solving repetitive and tedious tasks, and even human resources management.

While LLMs can be impactful in business, there are still unclear solutions on how businesses should harness these models. Should they simply rely on third-party API calls, commonly known as GPT wrappers, or should they host their LLMs?

In this article, we will answer these questions. By the end of this read, you’ll know which setup is best for your business and when to use it. So, sit back, relax, grab a coffee, and let’s dive in!

Understanding LLM APIs

LLM APIs are interfaces that allow developers to integrate large language models into their applications without needing to build the model themselves. Think of it as ordering food from a restaurant instead of cooking at home. You get access to a ready-made meal (LLM) without having to gather ingredients, follow recipes, and cook.

Similarly, LLM APIs let you use advanced AI features without building and managing the model. These APIs provide a simplified way for businesses and developers to use AI functionalities like text generation, summarisation, sentiment analysis, and more, often through simple API calls.

What are the features of LLM APIs?

Let's look at some features of LLM APIs and how they can align with your use case.

1. Ease of Access: LLM APIs are designed for simplicity. Developers can integrate them with minimal setup with just an API key and a basic understanding of how to make RESTful calls.

2. Scalability: APIs provided by major vendors are built on powerful cloud infrastructures to ensure availability as demand increases.

3. Cost: Using an API eliminates the need for expensive hardware required to train or host large language models.

4. Updated Models: LLM providers often improve their models over time, and APIs ensure you get the latest advancements in AI technologies.

Popular LLM APIs Providers

Now that you understand what LLM APIs are, let’s look at some popular examples of API providers/vendors offering these APIs:

1. OpenAI GPT
   
   OpenAI offers APIs for their GPT models, which are known for their performance in tasks like chatbots, content creation, and code generation. OpenAI also provides fine-tuning, which allows businesses to train the models to their specific needs, and embeddings for semantic search.

2. Google Gemini (formerly Bard)
   
   Gemini provides an API to build with Google’s ecosystem and benefit from Google's rich AI/ML infrastructure.

3. Anthropic Claude
   
   Anthropic’s Claude is designed with an emphasis on safety and reliability. It offers NLP capabilities for tasks like summarisation, content creation, coding co-pilot, and more.

4. Microsoft Azure OpenAI Service
   
   Microsoft Azure OpenAI Service integrates OpenAI’s models with Azure’s enterprise-grade scalability. This service is ideal for businesses needing strong solutions for large-scale applications.

When to Choose an LLM API

If you're wondering how to integrate AI into your application or business and unsure if using an API is the right choice, here’s when opting for an LLM API makes the most sense:

Limited talents: If your team lacks expertise in deploying and maintaining AI models, LLM APIs provide an accessible way to leverage advanced AI without the steep learning curve.

Moderate Usage Needs: When your application doesn’t require heavy, continuous processing, APIs offer a cost-efficient pay-as-you-go model that aligns with your usage patterns.

Prototype Development: For businesses testing new AI-driven features or building prototypes, APIs enable quick experimentation without committing to long-term infrastructure.

Short-Term Projects: For projects with a limited timeline, LLM APIs are ideal as they allow you to implement AI features quickly without the overhead of self-hosting or fine-tuning.

Understanding Self-Hosted LLMs

When we say self-hosted LLM, what exactly does it mean? Does it mean training your LLM from scratch, or does it refer to running a pre-trained model on your infrastructure? Let’s find out.

Self-hosting an LLM is simply running a pre-trained LLM on your own infrastructure rather than relying on third-party API providers like OpenAI or Google. This means that the model is deployed and maintained on the company’s servers or cloud instances, giving full control over the model’s performance, usage, and data privacy.
You can access model cards and platforms to run them on sites like Hugging Face Models and Kaggle Models.

What are the requirements for self-hosting LLMs?

Compute: LLMs are resource-intensive and require high-performance hardware, particularly GPUs or TPUs. These resources are used to handle the large computations involved in training, fine-tuning, and inferencing models.

Engineering talent: Deploying and managing an LLM requires technical knowledge in machine learning and model optimisation.
Most importantly, you will need to optimise the deployed model for latency and high throughput. This involves applying ML engineering concepts such as quantising the model, using inference containers, sharding across GPUs, and much more.

Budget: Hosting an LLM can be expensive, and it's important to consider your budget and integration costs. For instance, if you want to host a 6-billion-parameter LLM like GPT-J on a cloud platform such as AWS, you’d likely choose a GPU instance, like the NVIDIA V100 GPU. These instances cost around $3.06 per hour. While this might seem affordable at first glance, it adds up to roughly $26,800 per year for a single instance. If you want to run the model across multiple regions for redundancy, the annual cost can quickly multiply.

When to Choose Self-Hosting

Self-hosting isn’t the right solution for every business looking to integrate AI, but it’s the best choice in the following scenarios:

Businesses with Long-Term Projects: If your organisation relies heavily on AI for core operations and plans to scale significantly, self-hosting offers more control, long-term cost efficiency, and the ability to tailor the model to your specific needs.

Privacy: For industries like healthcare, finance, or cybersecurity, where sensitive data is involved, self-hosting ensures you retain complete control over your data, reducing exposure to third-party risks and simplifying regulatory compliance.

Customisation: When LLM APIs don’t meet your business requirements, self-hosting allows you to fine-tune models with your proprietary data, create specialised features, and adapt the model to niche use cases.

Comparing LLM APIs and Self-Hosting

Let's take a side-by-side look at both setups and see where each one shines:

Final Thoughts

So far, we've discussed key areas to consider for your AI integration setup. In addition to the areas highlighted, there are other factors to consider as well.

When planning to integrate AI into your business, keep in mind the scale you need. Do you want to lock yourself into proprietary models? Do you want to fine-tune your models? These questions will help guide your decision.

Check out this helpful guide to see how other companies are deploying LLMs: LLMOps Database. If you have any questions or suggestions, feel free to reach out.