Private LLMs vs Public Models: What Enterprises Need to Know

Large language models (LLMs) are at the heart of most generative AI development efforts today and have found wide-ranging applications from chatbots to automated content creation, streamlining workflows, predicting outcomes, and more. But as organizations look to adopt generative AI solutions, a number of important questions arise about implementation. Should you build private LLMs? Use public models? Both? 

In this blog, we’ll break down what’s possible with private LLM deployment and compare public vs. private generative AI models.

What private and public LLMs offer, tradeoffs to consider, enterprise deployment considerations, best practices, and more.

We’ll also highlight considerations for your enterprise LLM strategy and how Hexaware can help businesses unlock secure, scalable GenAI solutions. Learn more about Hexaware’s enterprise GenAI solutions here.

Introduction to LLMs and Enterprise GenAI

What is an LLM? 

Large Language Models (LLMs) are machine learning models trained over time to both understand and generate human language. These models form the basis for many modern generative AI tools we use today, including, but not limited to, writing assistance, summarization, question answering, table/code generation, etc.

Why should Enterprises care about LLMs? 

Organizations are looking to LLMs as a starting point for innovation and improved business outcomes. From automating manual tasks like customer support to personalization at scale, generative AI solutions allow businesses to dream big. By embedding generative AI across lines of business, enterprise-grade AI solutions help organizations:

• Automate customer support operations 
• Minimize documentation work 
• Enhance knowledge discovery/retrieval 
• Strengthen compliance/risk detection 
• Personalize at scale 

The key is having an enterprise AI strategy in place. For example, depending on your security and compliance requirements, deploying public LLMs may not provide the rigor you need for certain use cases. Organizations need a structured approach to deploying GenAI solutions. Hexaware’s enterprise GenAI framework provides a way for businesses to plan and execute their generative AI strategy.

Public LLMs vs Private LLMs 

There are primarily two options when choosing generative AI models.

Public Models 

These models include everything from large public models made available via API — think ChatGPT-type models — to public cloud-hosted solutions or third-party services that accept prompts and return responses over the internet.

Benefits of using public models:

• Easy to get started
• Low cost
• Models are maintained by the provider

Limitations of using public models:

• Potential data privacy concerns if using models that send your data over the internet
• Limited customization options if you need a model for proprietary use cases
• May have compliance limitations based on where your organization is located

Public models are great for getting started and tackling low-risk problems. However, when it comes to enterprise workloads, most organizations will need to take control of their own models.

Private LLMs 

Private LLM solutions are language models trained or deployed exclusively for a single enterprise. Private language models aren’t dependent on third-party APIs and keep data processing and storage inside your infrastructure.

Benefits of private LLM deployment: 

• Maintains full control of your data along with enhanced governance
• Ability to train models on your own knowledge base for maximum domain relevance
• More secure and can be built to comply with regulatory requirements
• Less risk of exposing sensitive data 

Considerations for private LLM deployment: 

Private language models take expertise to build/manage. This could mean building your own team or working with a partner.

• More expensive to manage initially due to computing and setup costs
• Your organization is responsible for keeping the model up to date

At Hexaware, we offer custom private LLMs for enterprises that need specialized LLMs built for their unique business requirements. Learn more about how we’ve built private LLMs for legal risk detection and recommendations here.

Detailed Generative AI Model Comparison

To help enterprises choose wisely, let’s compare public vs private LLMs across key enterprise considerations:

This generative AI model comparison clearly shows why enterprises with sensitive data, regulatory constraints, or custom workflows often lean toward private LLM deployment.

Drivers for Enterprise Adoption of LLMs 

Some of the primary business needs that drive enterprise adoption of LLMs include:

• Data Privacy/Compliance: When working within regulated industries such as finance, healthcare, and legal, you may be required to meet certain standards around how data can be used. Private LLMs help ensure your data never leaves your system.
• Security/Risk Mitigation: Large language models typically learn from their training data. If your model was trained on data that includes sensitive information, there is a risk that this data will be exposed during generation. Private LLM deployment allows your organization to set boundaries.
• Domain-Specific Knowledge: Industries such as Insurance and Legal involve significant knowledge work. By training private models on your own documents and contracts you can tailor generation for maximum accuracy/relevance.

There are many more reasons why an organization would benefit from considering a private LLM solution.

How to Implement a Private LLM Deployment Strategy

When developing a private LLM solution for your enterprise, there are several steps you should consider.

• Identifying GenAI use cases

Look across your organization for potential genAI use cases. Not all use cases are worth building. Evaluate business problems and use a framework to identify what can be built and prioritized.

• Data Preparation and Integration

Data is king. Cleanse your data and make sure it is accessible and ready for consumption. Connect to your databases or knowledge sources and integrate them into your model.

• Selecting a Base Model 

You’ll need to choose what base model you want to start with. There are a number of open source models as well as proprietary models you can choose from.

• Fine-tune your Model 

Once you select your base model, you can begin fine-tuning your model with your own data. Fine-tuning can help your model provide more relevant responses. For example, you can use a technique called RAG (Retrieval Augmented Generation) to build a model that includes both retrieval and generative capabilities.

• Setup LLMOps 

LLMOps includes the tools and processes you put in place to monitor your model’s performance, trigger updates, and ensure proper governance.

• Security, Compliance, and Governance 

Last but not least — make sure you have proper security, compliance, and governance measures in place. This includes things like authentication & authorization (who can access the model), logging, audit trails, etc. Implementing these steps will help you set up your own private LLMs. Every organization’s approach will be different and may require additional steps not outlined here.

• Case Use: Private LLM for Legal 

Legal is one of the first examples that comes to mind when we think about private LLM deployment. Legal teams manage large volumes of sensitive data from contracts to regulations and everything in between.

Using a general-purpose LLM may introduce risk by sharing sensitive data outside of your systems. Generations from public models may also miss the mark by failing to understand the context around your organization’s unique knowledge.

Case Example: Enterprise Legal LLM

Legal teams handle a mix of sensitive contracts, regulatory texts, and domain knowledge. General public models might inadvertently expose sensitive language or misinterpret context. In contrast, a private LLM for legal applications allows for:

• Controlled training on proprietary contract libraries
• Confidential case analysis, risk identification, and recommendation generation
• Compliance with data regulations such as GDPR or industry standards

This approach aligns with Hexaware’s offerings, which are designed to maintain full data control while enabling AI insights.

Hybrid and Public Model Use Cases 

That isn’t to say public models don’t have their place in an enterprise workflow. Many organizations will use a hybrid approach: 

• Public models for non-critical internal operations (knowledge base questions, internal docs, etc.)
• Private models when dealing with sensitive information/business logic (Legal, Finance, Healthcare, etc.)

Using a hybrid approach allows you to balance the need for rapid AI innovation and scenarios where your organization can’t afford mistakes.

Risk and Governance Considerations 

As we mentioned before, AI ethics is important. Trying to build governance into your AI solutions after the fact can lead to costly mistakes. Building a comprehensive governance model should be part of your overall enterprise AI strategy.

• Inclusions around how to mitigate bias 
• Performing fairness testing 
• Traceability 
• Model monitoring by role 

Learn more about Hexaware’s Responsible AI Strategy. 

Private vs Public: Enterprise Checklist 

Here are some questions to ask yourself when considering a private LLM deployment strategy.

• Do you have data governance policies in place?
• What are your specific use cases? (Ensure use cases have success metrics) 
• Have you identified compliance requirements your model must adhere to?
• How will your model integrate with existing systems?
• What security and monitoring procedures will you put in place?
• How will you maintain your model once it’s deployed? (feedback loops, etc.) 

What’s Next for Enterprise Adoption of LLMs? 

As more organizations jump on board with implementing LLMs we’ll likely see:

• Increased automation of compliance requirements 
• Integration of LLMs within existing enterprise software (CRM’s, ERPs, etc.)
• Hybrid cloud implementation will become more common 
• Rise of Narrow AI (small models tailored for specific use cases.)
• Tools to help automate model retraining/versioning 

At Hexaware, we continue to develop AI solutions with these trends in mind to empower you to innovate with confidence.

Conclusion

Businesses face an important decision when implementing Large Language Models. While public APIs provide a quick way to solve non-critical problems, private LLM deployments allow teams to maintain governance and build custom solutions tailored to their business.

Whether you’re just starting your GenAI journey or ready to build your own custom solutions, security, compliance, and governance should be top of mind.

Explore Hexaware’s Generative AI Services and Solutions to learn more about how we can help power your secure GenAI transformation.