Navigating LLM Platforms: Pre-Deployed vs. Customizable

Category Artificial intelligence, Generative AI

If you’re new to the world of Large Language Models (LLMs), make sure to check out my previous article, “ChatGPT: Breathing New Life into LLMs(Large Language Models),” for a quick primer.

Now, let’s dive deeper into the intriguing world of LLMs by exploring the platforms that grant access to these powerful language models. In this article, we’ll closely examine three leading platforms: OpenAI, Azure, and Hugging Face.

The Two Faces of LLM Platforms:

When it comes to LLM platforms, they typically fall into two categories:

  • Pre Deployed Models
  • Customisable Deployment

Pre-Deployed Models:

These platforms offer access to LLMs that are already up and running, allowing users to leverage the models within certain usage limits. Think of it as renting a high-end tool for specific tasks.



  • Availabilty: Pre-deployed models are readily available, making them accessible for a wide range of users, including developers, businesses, and researchers.
  • Quick Implementation: You can start using these models almost immediately, saving time on model development and training (Can be fine-tuned if needed)
  • Ease of Use: These platforms provides both API’s as well as the SDK’s which simplify integration for developers, making it easier to incorporate these powerful models into applications and services.
  • Vast Services: Azure offers a wide range of AI services beyond LLMs, including computer vision and machine learning, providing a comprehensive AI ecosystem.
  • Managed Infrastructure: Pre-deployed models come with managed infrastructure, reducing the need for users to handle server setup and maintenance.


  • Latency: Latency is the biggest concern for Pre-deployment publically accessed models like GPT. As it is public the traffic will be from every one who is using this publically available model. But using enterprise account of the platform can help us avoid the latency
  • Usage Limit: Most pre-deployed models impose usage limits, which can restrict usage of the model according to the tokens(1token = 4 words approximately) per minute and Requests per minute. Increasing the limit would need the quota request process to be followed according to the platform we are using(Currently OpenAI is not increasing the quota for gpt-4 which will directly affect the gpt-4 integrated products if the user base increases)
  • Usage Cost: Accessing top-tier pre-deployed models can be expensive, especially for extensive usage, which might not be feasible for smaller businesses and projects. But, Cost is generally more manageable compared to customizable deployments
  • Dependence on Third-Party Services: Relying on pre-deployed models means you are dependent on the service provider’s availability and reliability, which can impact your own service uptime.

Customizable Deployment:

On the other side, some platforms empower users to deploy LLMs according to their unique requirements. You have the freedom to select from a pool of available models and fine-tune them to respond precisely how you need. It’s like having the raw materials and the workshop to craft your own tools.


  • Platform: Hugging Face, Azure ML, Amazon Sagemaker
  • Models: Falcon-40B, LLAMA-70B

Note: Considering hugging face as representative for customisable deployment . So when the hugging face platform is used in this article, it means i am referring to customisable deployments


  • Less latency issues: As we have deployed the model ourself paying of the resources needed for the deployment, we will have the model instance which is being used only from our application. So, traffic will only be from our platform which resolve latency issues from the model side
  • In-Depth Customization: With Hugging Face’s platform, You have the capability to tailor models (like Falcon-40B and LLAMA) to meet highly specific requirements, which means you can fine-tune the model’s behaviour to deliver precisely the results you need, whether it’s for language understanding, text generation, or other NLP tasks..
  • Wide Model Selection: Hugging Face provides access to a diverse collection of pre-trained models. This extensive library allows you to choose a starting point that closely matches your project’s needs, saving you time and effort.
  • Transparency and Responsibility: You have direct control over ethical considerations, including addressing biases and ensuring responsible use. The customization process provides transparency, enabling you to understand and influence the model’s behavior in line with ethical guidelines


  • Huge Costing: Here we dont have anything called pay as you options for deploying your own model and to pay only as you use. Instead we have to pay for the resources according the number of hours it is being up. This might actually cost more even though you are not using the model which is not the case in pre-deployed models where you only pay for what you have used
  • Maintenance: The owner has to worry about the availability of the deployment all the time for the user which will add more pressure on the owner to keep it alive. Should also calculate and think about the configurations we need to put in for scaling the model for varying or increasing user count
  • Steep Learning Curve: The steep learning curve associated with customizable deployment can be particularly challenging for users with limited machine learning expertise. It involves understanding model architectures, data preprocessing, and deployment infrastructure, which may not be feasible for newcomers.
  • High Computational Demands: Developing and fine-tuning models like Falcon-40B and LLAMA can be resource-intensive. These models require powerful hardware and significant computational resources, which may pose challenges for individuals or small teams with limited access to such resources.

In conclusion, as we navigate the intricate landscape of Large Language Models (LLMs), the choice between pre-deployed models and customizable deployments depends on your project’s unique needs, budget, resources, and ethical considerations. Both avenues offer distinct advantages and challenges, empowering you to harness the power of language models in different ways.

Regardless of your chosen path, the ability to fine-tune these models with custom data remains a powerful tool to tailor their behaviour to your specific requirements.

In our upcoming articles, we will delve deeper into the practical aspects, guiding you on how to integrate and utilize both pre-deployed models and custom deployments using Node.js. Stay tuned for more insights into the fascinating world of LLMs.

Author: Sriram C

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