With the surge in AI adoption across industries, Large Language Models (LLMs) like OpenAI’s GPT series have proven their worth in a variety of applications ranging from content creation to automated customer service. Yet, as organizations continue to expand their AI infrastructure, the challenges of data security, cost-effectiveness, and optimized utilization of existing resources become more pronounced.
Let’s explore why private LLMs are increasingly becoming a necessity for modern organizations.
Data Security & Privacy
The value of data in today’s digital age is undeniable. Every interaction, transaction, and query generates data, and it’s often the lifeblood of many organizations. When companies utilize public or shared LLMs, there is always a lingering concern about data security and privacy.
Sensitive Data Exposure
Public LLMs, while efficient, can inadvertently be trained on or exposed to sensitive data. A private LLM, confined within an organization’s boundaries, reduces the risk of external data leaks.
Regulatory Compliance
Many industries have strict regulations about data handling and processing (think GDPR, CCPA, HIPAA). Hosting a private LLM ensures that companies can closely monitor and adhere to these standards without external dependencies.
The Fixed Cost of Generative AI
Budgetary constraints and cost-efficiency are top of mind for most organizations. With AI, it’s not just about the upfront investment; it’s also the recurrent costs and ongoing infrastructure requirements. Here’s where private LLMs can offer a significant advantage:
Predictable Pricing
Licensing public LLMs can sometimes come with variable costs based on usage. With a private LLM, organizations can have a fixed cost, followed by manageable maintenance expenses.
Customization & Training
Private LLMs can be fine-tuned to specific organizational needs. This means companies won’t have to constantly pay for upgrades or new features—they can develop and implement them in-house.
Utilizing Existing Infrastructure
Not every organization has the luxury of the latest GPU clusters or dedicated AI hardware. Luckily, with the evolution of LLMs, there’s increasing flexibility in deployment:
CPU Over GPU
Advanced LLMs can now run efficiently on CPUs. This means organizations can leverage their existing infrastructure without the need for hefty investments in GPUs.
Scalability
With a private LLM, companies can decide how to scale based on their resources and needs. Whether it’s distributing the model across multiple servers or integrating it with current systems, the control lies with the organization.
While the allure of public LLMs and their rapidly evolving features is strong, the long-term advantages of private LLMs cannot be ignored. They offer a secure, cost-effective, and resource-optimized solution, tailor-made for organizational needs. As the generative AI landscape continues to evolve, it’s crucial for businesses to make strategic choices that ensure growth while safeguarding their data and resources.
SearchBlox understands the challenges faced by enterprise customers for LLMs. That is why our SearchAI ChatBot solution uses the SearchAI PrivateLLM Server to provide secure and fixed cost deployments on existing infrastructure.
Finding information across knowledge silos is a significant need for all organizations. SearchAI ChatBot offers the benefits of Chat GPT with more control and oversight. Now, with SearchAI Private LLM Server, organizations can enjoy even more control, efficiency and customized frameworks.
Timo Selvaraj, Chief Product Officer, SearchBlox
You’ve invested in your organization’s data. Harness what’s already yours and deliver it efficiently and automatically to your distributed teams and customers.
Here are some of the top benefits and use cases Generative AI and ChatBot services from SearchBlox can offer your organization, your distributed teams and employees and your customers — backed by security-compliant encryption standards.
Not sure? Schedule a private demo, customized with your organization’s content.
With the surge in AI adoption across industries, Large Language Models (LLMs) like OpenAI’s GPT series have proven their worth in a variety of applications ranging from content creation to automated customer service. Yet, as organizations continue to expand their AI infrastructure, the challenges of data security, cost-effectiveness, and optimized utilization of existing resources become more pronounced.
Let’s explore why private LLMs are increasingly becoming a necessity for modern organizations.
Data Security & Privacy
The value of data in today’s digital age is undeniable. Every interaction, transaction, and query generates data, and it’s often the lifeblood of many organizations. When companies utilize public or shared LLMs, there is always a lingering concern about data security and privacy.
Sensitive Data Exposure
Public LLMs, while efficient, can inadvertently be trained on or exposed to sensitive data. A private LLM, confined within an organization’s boundaries, reduces the risk of external data leaks.
Regulatory Compliance
Many industries have strict regulations about data handling and processing (think GDPR, CCPA, HIPAA). Hosting a private LLM ensures that companies can closely monitor and adhere to these standards without external dependencies.
The Fixed Cost of Generative AI
Budgetary constraints and cost-efficiency are top of mind for most organizations. With AI, it’s not just about the upfront investment; it’s also the recurrent costs and ongoing infrastructure requirements. Here’s where private LLMs can offer a significant advantage:
Predictable Pricing
Licensing public LLMs can sometimes come with variable costs based on usage. With a private LLM, organizations can have a fixed cost, followed by manageable maintenance expenses.
Customization & Training
Private LLMs can be fine-tuned to specific organizational needs. This means companies won’t have to constantly pay for upgrades or new features—they can develop and implement them in-house.
Utilizing Existing Infrastructure
Not every organization has the luxury of the latest GPU clusters or dedicated AI hardware. Luckily, with the evolution of LLMs, there’s increasing flexibility in deployment:
CPU Over GPU
Advanced LLMs can now run efficiently on CPUs. This means organizations can leverage their existing infrastructure without the need for hefty investments in GPUs.
Scalability
With a private LLM, companies can decide how to scale based on their resources and needs. Whether it’s distributing the model across multiple servers or integrating it with current systems, the control lies with the organization.
While the allure of public LLMs and their rapidly evolving features is strong, the long-term advantages of private LLMs cannot be ignored. They offer a secure, cost-effective, and resource-optimized solution, tailor-made for organizational needs. As the generative AI landscape continues to evolve, it’s crucial for businesses to make strategic choices that ensure growth while safeguarding their data and resources.
SearchBlox understands the challenges faced by enterprise customers for LLMs. That is why our SearchAI ChatBot solution uses the SearchAI PrivateLLM Server to provide secure and fixed cost deployments on existing infrastructure.
Finding information across knowledge silos is a significant need for all organizations. SearchAI ChatBot offers the benefits of Chat GPT with more control and oversight. Now, with SearchAI Private LLM Server, organizations can enjoy even more control, efficiency and customized frameworks.
Timo Selvaraj, Chief Product Officer, SearchBlox
You’ve invested in your organization’s data. Harness what’s already yours and deliver it efficiently and automatically to your distributed teams and customers.
Here are some of the top benefits and use cases Generative AI and ChatBot services from SearchBlox can offer your organization, your distributed teams and employees and your customers — backed by security-compliant encryption standards.
