Large language models (LLMs) are a type of artificial intelligence model designed for natural language processing (NLP) tasks.

They are trained on vast amounts of text data to understand and generate human-like responses or outputs.

This article covers what LLMs are, why they've become central to enterprise operations, and how organizations are deploying them, including the trade-offs and implementation approaches that matter most for decision-makers.

TL;DR: Large language models are deep learning systems trained on massive text datasets to understand and generate natural language. In enterprise contexts, they power knowledge retrieval, customer support, code generation, content automation, and agentic workflows. The global enterprise LLM market was valued at $6.7 billion in 2024 and is projected to reach $71.1 billion by 2034. Enterprises can access LLMs via API, deploy open-source models on their own infrastructure, or work with platforms like TextCortex that handle multi-model orchestration and compliance on their behalf.

What are Large Language Models?

Large language models are powerful deep learning algorithms that are capable of understanding and generating natural language.

large language model for enterprises

How do they work?

These models use complex algorithms and neural networks to understand the context, semantics, and syntax of text input and generate relevant and coherent responses. The primary mechanism behind large language models is deep learning, particularly the use of transformers, which have shown remarkable success in NLP tasks.

Popular LLMs

The LLM market shifted fast since 2023. The most widely deployed models in enterprise environments now include:

  • OpenAI GPT-4o and o3: GPT-4o is still widely used for general enterprise tasks; o3 is OpenAI's reasoning-focused model for complex multi-step problems
  • Anthropic Claude Sonnet 4 and Opus 4: Claude emerged as the leading enterprise model in 2025, capturing 32% enterprise LLM market share according to Menlo Ventures research1
  • Google Gemini 2.0 and 2.5: Google's expansion into enterprise via Google Workspace integration drove 69% developer usage in early 2025
  • Meta Llama 3.x: The leading open-source option, widely adopted by organizations that want to run models on their own infrastructure
  • Mistral Large 2: A European open-source alternative, particularly relevant for EU enterprises focused on data sovereignty

A notable shift: 37% of enterprises now run 5 or more LLMs in production environments simultaneously, selecting different models for different tasks rather than committing to a single vendor.2

Capabilities

These models handle a range of tasks: answering questions, summarizing text, translation, sentiment analysis, code generation, and creating original content. Newer models also support agentic workflows, planning and executing multi-step tasks autonomously, with tool access and memory across sessions.

The Role of LLMs in the Enterprise

Now that you have a better understanding of what LLMs are, let's take a look at the role they play within enterprises.

the role of LLMs in the enterprise

Using Model APIs

There are different ways you can rely on an LLM in the enterprise beyond the simple web interface.

You could make an API call to a model presented as a service: different companies provide public APIs users can easily connect to software. The process brings several benefits including increased sophistication and speed.

Running an open-source model

You could also download and use an open-source model in an environment you personally manage.

This might be the right solution for certain companies or use cases. These models can run on servers owned internally by a business or in a cloud computing system managed by the enterprise.

Trade-Offs

Of course, enterprises should take into account the trade-offs that come with LLMs and enforce precautionary measures to reduce some of the potential hazards associated with using them:

  • Complexity: Setting up and maintaining an LLM is very complex. Organizations should always investigate whether they possess the expertise and capacity of data science and engineering employees to set up and oversee an LLM.
  • Confidentiality: When using LLMs, keep in mind that they can process and generate text based on input that can contain sensitive or confidential information. Confidential data must be kept secure and not exposed during interactions with LLMs, both for individual users and when they are integrated into other processes.
  • Data Privacy: Although LLMs don't explicitly store or share the data they use for training, there is still a risk of unintended information leakage or privacy violations, particularly when dealing with personal or sensitive information. If a service is regularly retrained on user interactions, other users may be able to access data that was sent to the service at some point.
  • Regulatory Compliance: It is essential to adhere to data protection regulations like the GDPR and EU AI Act when using LLMs in business applications, as not doing so can lead to hefty fines, legal repercussions, and damage to one's reputation.

Ensuring that your organization's use of LLMs aligns with ethical standards and does not result in the production of inaccurate or harmful content is a baseline requirement for any enterprise deployment.

TextCortex, Enterprise AI for LLM Deployment

TextCortex is an EU-based enterprise AI infrastructure platform that gives organizations access to multiple LLMs, GPT-4o, Claude, Gemini, and others, from a single, governed platform. Instead of managing separate API contracts, security reviews, and access controls for each model, enterprises run everything through TextCortex.

TextCortex integrates with your enterprise data, SharePoint, Google Drive, Confluence, and other sources, and lets employees retrieve and act on that information through natural language. Access controls, audit logs, and permission management sit at the platform level, not per-model. This is a critical difference for enterprises handling sensitive data across multiple departments.

Results from b2venture, an investment firm with over €800M AUM:

  • 7x AI usage growth across the investment team
  • 70% team adoption achieved
  • 5-10 hours saved per investment opportunity assessed
  • 10+ specialized AI agents deployed across distinct research and workflow functions

TextCortex is ISO 27001 certified, SOC 2 certified, fully GDPR compliant, and EU AI Act aligned. It serves Fortune 500 and DAX 40 customers worldwide, and includes a 3-month AI training program with 4 workshops, team certification, and a dedicated account manager to drive adoption from the start.

Frequently Asked Questions

What is a large language model (LLM)?

A large language model is a deep learning algorithm trained on vast amounts of text data to understand and generate natural language. LLMs use transformer architectures to understand context, semantics, and syntax, and they can handle tasks ranging from answering questions and summarizing text to writing code and executing multi-step agentic workflows.

Which LLMs are most used in enterprise environments?

As of 2025, the leading enterprise LLMs are OpenAI's GPT-4o and o3, Anthropic's Claude Sonnet 4 and Opus 4, and Google's Gemini 2.0 and 2.5. Menlo Ventures research shows Anthropic captured 32% enterprise LLM market share by mid-2025. Meta's Llama 3.x is the most widely deployed open-source option for organizations that want on-premise control.

How do enterprises deploy LLMs?

There are 3 main approaches: using public API services from model providers, running open-source models on internal infrastructure, or deploying through a platform like TextCortex that handles multi-model orchestration, enterprise data integration, and compliance on your behalf. Most enterprises doing serious AI work combine at least 2 of these approaches.

What are the main risks of using LLMs in the enterprise?

The primary risks are data privacy (LLMs can process sensitive information), regulatory compliance (especially under GDPR and the EU AI Act), model inaccuracy (hallucinations or incorrect outputs), and complexity of setup and maintenance. Most of these risks are manageable through the right platform, access controls, and governance framework.

How big is the enterprise LLM market?

The global enterprise LLM market was valued at $6.7 billion in 2024 and is projected to reach $71.1 billion by 2034, growing at a CAGR of 26.1%. Gartner projects that more than 80% of enterprises will have deployed generative AI applications or APIs by 2026, up from less than 5% in 2023, one of the fastest technology adoption curves on record.

What is the difference between using an LLM API and running an open-source model?

API-based models like GPT-4o or Claude are managed by the provider and accessed via the cloud. They're faster to start with and regularly updated, but your data passes through the provider's infrastructure. Open-source models like Llama 3.x run on your own servers, giving you full data control, but they require significantly more infrastructure expertise to deploy and maintain.

1 Menlo Ventures. "2025 Mid-Year LLM Market Update: Foundation Model Landscape + Economics." July 2025. menlovc.com

2 Kong Inc. "What's Next for Generative AI in the Enterprise." 2025. konghq.com