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Why You Might Need Your Own Private OpenAI

Sam Altman’s announcement on OpenAI’s ARR crossing $1.6 billion created a buzz in the genAI world. What’s more interesting is that the majority of this revenue comes not from the ChatGPT subscriptions, but through software developers accessing the GPT models via the APIs, developing a wide range of genAI-based applications spanning different domains.

The popularity of generative AI is largely owed to the companies racing to adopt genAI into their businesses. Which brings us to an important concern. 

In the rapidly evolving landscape of generative artificial intelligence (AI), developing and deploying large language models (LLMs) has become a central concern for businesses and developers alike. 

As these models grow in complexity and capability, the choice of deployment method significantly impacts the operational effectiveness, cost, and scalability of genAI applications. 

This blog post addresses the different deployment options you have when it comes to your LLMs. We'll dive into the pros and cons of each approach, helping you make an informed decision for your genAI projects.


Proprietary LLM Commercial APIs

The simplicity of usage of proprietary LLM commercial APIs is their main attraction. Strong APIs from businesses like OpenAI and others enable developers to include complex language processing features into their apps without requiring a lot of infrastructure or AI knowledge. The benefits of these services include easy setup at first, efficient operation, and regular updates from the service provider.

Of course, this will help enhance user experiences and also, it will improve business outcomes. The disadvantages of Proprietary LLM Commercial APIs are also there, which include operational costs, little room for customization, and data concerns. 

Pros:

  • Ease of Initial Setup and Operation: These APIs often include a great deal of documentation and support and developers can effectively utilize powerful AI functions while concentrating on their initial application logic. Developers can quickly integrate LLM capabilities into their applications, reducing time-to-market for genAI features.

  • Managed Service: Continuous updates and improvements are handled by the provider, ensuring the LLM remains cutting-edge.This continuous maintenance keeps the system up to date with altering standards in the industry and technology, which is just as important as the initial setup.


  • Automated security and performance updates: Ensures ongoing security against emerging threats and optimization of the performance. This helps with smooth operation and frees businesses to focus on innovation.

Cons:

  • Huge Operational Costs: The cost of Proprietary Commercial APIs mirrors the investment these companies make for developing, maintaining, and supporting advanced LLM processing powers. It does involve ongoing investments in technology, resources, and support.


  • Limited Control Over Model Output: Users are at the mercy of the API provider's interpretation of what the model should produce, with little room for customization. It's impossible to alter or modify the model architecture of proprietary LLMs due to their closed-source nature. It's a case of one model fitting all.If your company needs to innovate and try new things, you may need to look at more options.


  • Closed-Source Model and Data Security Concerns: The proprietary nature of these APIs means that the users have no insight into the model's workings. How the data is processed and protected by the proprietary LLM service provider remains a black box. For companies that deal with sensitive and regulated data, this raises data security and privacy concerns. Challenges may include potential risks with data regulatory compliance. 

These kinds of security concerns may be avoided if businesses opt for alternative solutions that guarantee transparency and control over data handling practices.


Shared Managed Infrastructure Providers

Unlike Proprietary LLM Commercial APIs, Shared Managed Infrastructure Providers offer an attractive proposition for those seeking a middle ground between complete outsourcing and in-house deployment. These services allow businesses to run open-source LLMs on a shared infrastructure, reducing operational costs while maintaining a degree of control over the models and their outputs.

With this solution, businesses can get the computational resources for running complex language models without having to pay for hardware upfront or deal with continuous maintenance by using shared infrastructure. 

Sharing resources with other companies within the same infrastructure can benefit businesses by reducing the costs of scale, which can be much more affordable than standalone deployments. Additionally, shared managed infrastructure providers often offer services like optimization, monitoring, and security.

Although shared managed infrastructure is advantageous in many ways, users still need to carefully evaluate this solution to make sure the provider meets their unique objectives and needs.

To reduce the risks of future complications, businesses who opt for this solution should also consider factors like performance guarantees, data residency, compliance certifications, and service-level agreements (SLAs). 


Pros:

  • Cost-Effectiveness: Operating on an open-source LLM stack can significantly reduce costs, especially in production environments. Cost per token is cheaper because there are relatively lower compute requirements for the OSS models.Utilizing open-source LLM stacks users also save on costly infrastructure overhauls or proprietary add-ons.


  • Faster Innovation Circles: Open-source solutions frequently profit from a collaborative development paradigm, in which contributions from a worldwide developer community lead to ongoing improvements without requiring a substantial commitment from particular enterprises.

  • Ease of Operation: Open-source LLM stacks have a lot to offer in terms of ease of use in addition to lower costs. These systems are made available to users with different degrees of technical experience by their frequently user-friendly interfaces, extensive documentation, and community assistance.

  • Flexibility: Shared Managed Infrastructure Providers offer flexibility by providing users with a choice of LLMs to integrate into their applications. They also can customize the language model so that users can maximize its relevance.

Cons:

  • Rate-Limiting: Sharing infrastructure with other users can lead to rate limiting, affecting application performance during peak times. 


    As multiple users have access to the same source of computing resources (such as CPU cycles, memory, and network bandwidth), the available resources may be pushed during times of high demand. 


  • Scaling Concerns: When applications need to rapidly scale, due to the sudden increase of demand, using shared managed infrastructures can become challenging. During Periods of high traffic, when users need to handle large volumes of data, the available resources may become insufficient to meet the increased workload.


  • Security Risks: There are interdependencies within this infrastructure among multiple users. Sharing the same pool of resources can result in the possibility of unauthorized access (intentionally or not) or data breaches. These are one of the most common security risks. Other challenges include having different security requirements or risk profiles with different industry regulations and standards (GDPR, HIPAA, or PCI DSS, etc) 


Provisioned Throughput on Dedicated Resources

Deploying LLMs on dedicated resources with provisioned throughput represents the high end of the spectrum. This model might be the ideal choice for companies looking for performance consistency. It makes it possible to assign huge processing power to specific applications within dedicated cloud and on-premise environments.

Provisioned throughput on dedicated resources offers a predictable and solid basis for business-critical applications, in contrast to shared environments where resource availability may vary depending on demand or competing workloads.

This model provides full autonomy and flexibility, enabling users to customize resource distribution to fit their specific needs, including adjusting compute capacity, optimizing network bandwidth, or arranging storage resources.

Offering unparalleled control and scalability, this approach is ideal for businesses with specific needs that cannot be met by shared infrastructure or proprietary APIs. 

Dedicated resources add an extra degree of security and isolation which eliminates the risks of performance fluctuations, resource contention during critical workloads, and the possibility of data breaches or unauthorized access. 

With specialized infrastructure, companies can implement strong security measures and uphold ultra-careful access controls to protect sensitive data. 

In general, companies looking to maximize the performance, management, and security of their workloads can easily achieve it with provisioned throughput on dedicated resources. 

This model is the perfect choice as it encompasses all the advantages the other infrastructures provide but with better security, customizable features, and predictable performance.


Pros:

  • Flexible Scaling: Scaling the deployment to meet high demand is way more easier and predictable with dedicated resources. If the workload requirements change, users can quickly adjust resource allocation without cutting back on performance and reliability.


  • Better SLA Guarantees: Dedicated resources often offer better service level agreements (SLAs). The SLAs typically include commitments to uptime, performance guarantees, and responsiveness to support requests. This, of course, ensures higher availability and reliability.


  • Open-Source Stack: Like shared infrastructure, this approach allows for the operation on an open-source LLM stack. This means users can save money while taking advantage of open-source tools to customize language processing capabilities, improving interoperability, and minimizing vendor lock-in.

Cons:

  • Quota Restrictions: While scaling is more flexible, it often comes with quota restrictions that need careful monitoring and management. Cloud providers often quota-restrict resource availability, requiring long-term resource commitments and reserved instances to get around these limitations.


  • Additional Costs: The benefits of dedicated resources come at a higher price, making it a less viable option for smaller projects or businesses with budget restrictions.


    Sourcing GPUs for on-premise implementations can be expensive, especially with a global GPU shortage and providers like AWS often offer provisioned throughput as a service add-on, which might add to the total cost of operation.


How ScaleGenAI Addresses The Shortcomings of Provisioned Throughput

From the perspective of finding the best LLM solution, we can see that Provisioned Throughput covers all the disadvantages of other models. There are shortcomings for deploying LLMs on dedicated resources though. Most common ones for single-provider provisioned throughputs (AWS, Azure, etc.) are quota restrictions and that SLAs often require additional costs. 

To solve these issues ScaleGenAI comes up with solutions that makes using Provisioned Throughput on dedicated resources the ultimate option. 

ScaleGenAI guarantees smooth integration with the users' cloud and on-premise infrastructures while reducing operational overhead. By automating complex LLMOps and infrastructure management workflows and optimizing resource allocation, ScaleGenAI's orchestration logic continuously adapts to variations in throughput and latency demands for your deployments, providing optimal performance and scalability. This makes us the leading option for LLM deployment in production.

How ScaleGenAI solves the issue of additional cost 

To reduce extra expenses related to LLM implementation, ScaleGenAI uses a blended approach of offering both advanced technologies and smart optimizations. ScaleGenAI dramatically reduces costs without compromising performance by utilizing spot instances, which provide a 6x to 9x reduction in compute costs when compared to on-demand resources.

In addition, the methods for fault-tolerant fine-tuning and deployment on spot instances guarantee continuous operation, reducing downtime and optimizing cost-effectiveness.

Ultra-quick GPU machine provisioning enables ScaleGenAI's inference deployments to scale up just in seconds, which is very beneficial for companies that want fast scalability without paying astronomical overheads. This smooth scaling ability results in significant cost reductions in addition to optimizing resource use.

This is an affordable solution for private and secure data processing in addition to improving performance.

How ScaleGenAI solves the issue of quota restrictions 

ScaleGenAI's multi-cloud orchestration enables users to avoid the restrictions of cloud providers by allowing the smooth deployment of LLMs across several clouds and dedicated computers. 

This adaptability guarantees resource availability and scalability that are not challenged by quota restrictions on a particular cloud platform. 

ScaleGenAI makes sure that operations continue even with quota constraints by smartly redistributing workloads to available resources in the event of spot-instance failures. The quick auto-scaling system automatically modifies computing resources in real time which optimizes resource usage and avoids quota restrictions.


Conclusion

The best deployment approach for your business case depends on your unique business objectives, financial resources, and the requirements of the scalability of your project. 

It requires a considerable evaluation of the situation for optimal decision-making. Understanding the advantages and disadvantages of each option is important, regardless of whether you choose the affordability of shared infrastructure, the scalability of dedicated resources with provisioned throughput, or the convenience of using proprietary commercial APIs.

By aligning your choice with your application's demands and growth prospects, you can use the extensive power of LLMs to drive your genAI applications to new levels. 

This exploration of LLM models highlights the importance of strategic decision-making when it comes to finding the best deployment method for your generative AI application. As the field continues to mature, staying informed and adaptable is key to using these powerful technologies effectively.

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