As of 2024, over **** the global firms planned to use LLMs (LLama and LLama-like models), while ** percent chose to use embedding models (BERT and family) in their commercial deployment. Additionally, only ***** percent planned to utilize multi-modal models.

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Large Language Model Market Size 2025-2029

The large language model (LLM) market size is forecast to increase by USD 20.29 billion at a CAGR of 34.7% between 2024 and 2029.

The market is experiencing significant growth due to the democratization and increasing accessibility of this technology. This trend is driven by advancements in AI and machine learning, making LLMs more accessible to a wider range of organizations. Additionally, the integration and customization of enterprise-grade LLMs are becoming increasingly popular, enabling businesses to tailor language models to their specific needs. However, the market also faces challenges, including prohibitive computational and financial barriers to entry and scaling. These obstacles can hinder smaller organizations from implementing LLMs and limit their potential impact on the market.
To capitalize on opportunities and navigate challenges effectively, companies must stay informed of advancements in LLM technology and explore cost-effective solutions for implementation and scaling. By doing so, they can gain a competitive edge and effectively address the evolving needs of their customers and stakeholders. AI-powered automation and data integration pipelines are essential components of these platforms, enabling model interpretability, federated learning, and data privacy measures.

What will be the Size of the Large Language Model (LLM) Market during the forecast period?

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In the market, model robustness and scalability challenges continue to dominate discussions. Deployment strategies require careful consideration, with knowledge distillation and distributed training employed to enhance model performance. Training data quality is crucial, as dataset bias and data poisoning can significantly impact model accuracy. Explainable AI and model interpretability are essential for ensuring responsible AI use. Adversarial attacks defense and context window size optimization are key to enhancing model security. Cost optimization and prompt optimization are critical for businesses, with API integration and token limits enabling seamless integration into existing systems. Computer vision and image recognition are transforming industries like healthcare and education.

Generalization ability and ethical considerations are also important, with bias mitigation techniques and model compression employed to address potential issues. Distributed training and hardware acceleration improve performance, while parallel processing and security vulnerabilities require ongoing monitoring. Overall, the LLM market is dynamic, with ongoing research addressing scalability, cost optimization, and ethical considerations. Deep learning models and transformer networks are essential for text summarization techniques, model fine-tuning strategies, and recurrent neural networks.

How is this Large Language Model (LLM) Industry segmented?

The large language model (LLM) industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Component

  Solutions
  Services


Type

  Below 100 B parameters
  Above 100 B parameters


End-user

  IT and ITES
  Healthcare
  BFSI
  Education
  Others


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    UK


  APAC

    Australia
    China
    India
    Japan


  South America

    Brazil


  Rest of World (ROW)

By Component Insights

The Solutions segment is estimated to witness significant growth during the forecast period. The market is witnessing significant advancements, with solutions being the primary focus. This segment comprises foundational models, which are massive neural networks trained on extensive text and code, and their fine-tuned or specialized derivatives. These models are delivered to users via APIs, PaaS offerings, or integrated software products. Innovation and capital investment are at the forefront of this sector, driven by the democratization of sophisticated AI capabilities. Transfer learning applications, question answering systems, and knowledge graph integration are integral to these models. Embedding dimensionality and prompt engineering techniques enhance semantic similarity measures in natural language processing.

Named entity recognition, few-shot learning, and text classification models employ part-of-speech tagging and sentiment analysis tools. Hyperparameter optimization and tokenization methods facilitate syntactic parsing algorithms and machine translation systems. Parameter efficiency methods and attention mechanisms improve data augmentation techniques for zero-sho

In 2023, Claude 3 Opus was the large language model (LLM) tool that had the largest average worldwide, with an average total of ***** percent. Close behind, in second place, was Gemini 1.5 Pro with an average of about ** percent.

The Large Language Model Market Report is Segmented by Offering (Software Platforms and Frameworks, and More). Deployment (Cloud, and More), Model Size (Less Than 7 B Parameters, and More), Modality (Text, Code, and More), Application (Chatbots and Virtual Assistants, and More), End-User Industry (BFSI, and More), and Geography (North America, Europe, Asia, and More). The Market Forecasts are Provided in Terms of Value (USD).

As of 2024, surveyed employees in the United States rated the impact of large language models on transcribing doctor-patient conversations with an average score of ****, with * being the highest possible score. The second-highest rating was for the use of LLMs on medical chatbots, with a score of ****.

1. Overview Volume: 2 Millions Data use: Instruction-Following Evaluation for LLM
   Data content: A variety of complex prompt instructions, between 50 and 400 words, with no fewer than 3 constraints in each prompt Production method: All prompt are manually written to satisfy the diversity of coverage
   Language: English, Korean, French, German, Spanish, Russian, Italian, Dutch, Polish, Portuguese, Japanese, Indonesian, Vietnamese

2. About Nexdata Nexdata owns off-the-shelf PB-level Large Language Model(LLM) Data, 3 million hours of Audio Data and 800TB of Annotated Imagery Data. These ready-to-go data supports instant delivery, quickly improve the accuracy of AI models. For more details, please visit us at https://www.nexdata.ai/datasets/llm?source=Datarade

As of 2024, at least one fifth of respondents working in healthcare organizations reported that they used large language models for answering patient questions and medical chatbots. Furthermore, ** percent of healthcare organizations used LLMs for biomedical research.

BackgroundClinical data is instrumental to medical research, machine learning (ML) model development, and advancing surgical care, but access is often constrained by privacy regulations and missing data. Synthetic data offers a promising solution to preserve privacy while enabling broader data access. Recent advances in large language models (LLMs) provide an opportunity to generate synthetic data with reduced reliance on domain expertise, computational resources, and pre-training.ObjectiveThis study aims to assess the feasibility of generating realistic tabular clinical data with OpenAI’s GPT-4o using zero-shot prompting, and evaluate the fidelity of LLM-generated data by comparing its statistical properties to the Vital Signs DataBase (VitalDB), a real-world open-source perioperative dataset.MethodsIn Phase 1, GPT-4o was prompted to generate a dataset with qualitative descriptions of 13 clinical parameters. The resultant data was assessed for general errors, plausibility of outputs, and cross-verification of related parameters. In Phase 2, GPT-4o was prompted to generate a dataset using descriptive statistics of the VitalDB dataset. Fidelity was assessed using two-sample t-tests, two-sample proportion tests, and 95% confidence interval (CI) overlap.ResultsIn Phase 1, GPT-4o generated a complete and structured dataset comprising 6,166 case files. The dataset was plausible in range and correctly calculated body mass index for all case files based on respective heights and weights. Statistical comparison between the LLM-generated datasets and VitalDB revealed that Phase 2 data achieved significant fidelity. Phase 2 data demonstrated statistical similarity in 12/13 (92.31%) parameters, whereby no statistically significant differences were observed in 6/6 (100.0%) categorical/binary and 6/7 (85.71%) continuous parameters. Overlap of 95% CIs were observed in 6/7 (85.71%) continuous parameters.ConclusionZero-shot prompting with GPT-4o can generate realistic tabular synthetic datasets, which can replicate key statistical properties of real-world perioperative data. This study highlights the potential of LLMs as a novel and accessible modality for synthetic data generation, which may address critical barriers in clinical data access and eliminate the need for technical expertise, extensive computational resources, and pre-training. Further research is warranted to enhance fidelity and investigate the use of LLMs to amplify and augment datasets, preserve multivariate relationships, and train robust ML models.

IntroductionLearning complex, detailed, and evolving knowledge is a challenge in multiple technical professions. Relevant source knowledge is contained within many large documents and information sources with frequent updates to these documents. Knowledge tests need to be generated on new material and existing tests revised, tracking knowledge base updates. Large Language Models (LLMs) provide a framework for artificial intelligence-assisted knowledge acquisition and continued learning. Retrieval-Augmented Generation (RAG) provides a framework to leverage available, trained LLMs combined with technical area-specific knowledge bases.MethodsHerein, two methods are introduced (DaaDy: document as a dictionary and SQAD: structured question answer dictionary), which together enable effective implementation of LLM-RAG question-answering on large documents. Additionally, the AI for knowledge intensive tasks (AIKIT) solution is presented for working with numerous documents for training and continuing education. AIKIT is provided as a containerized open source solution that deploys on standalone, high performance, and cloud systems. AIKIT includes LLM, RAG, vector stores, relational database, and a Ruby on Rails web interface.ResultsCoverage of source documents by LLM-RAG generated questions decreases as the length of documents increase. Segmenting source documents improve coverage of generated questions. The AIKIT solution enabled easy use of multiple LLM models with multimodal RAG source documents; AIKIT retains LLM-RAG responses for queries against one or multiple LLM models.DiscussionAIKIT provides an easy-to-use set of tools to enable users to work with complex information using LLM-RAG capabilities. AIKIT enables easy use of multiple LLM models with retention of LLM-RAG responses.

Energy consumption of artificial intelligence (AI) models in training is considerable, with both GPT-3, the original release of the current iteration of OpenAI's popular ChatGPT, and Gopher consuming well over **********-megawatt hours of energy simply for training. As this is only for the training model it is likely that the energy consumption for the entire usage and lifetime of GPT-3 and other large language models (LLMs) is significantly higher. The largest consumer of energy, GPT-3, consumed roughly the equivalent of *** Germans in 2022. While not a staggering amount, it is a considerable use of energy. Energy savings through AI While it is undoubtedly true that training LLMs takes a considerable amount of energy, the energy savings are also likely to be substantial. Any AI model that improves processes by minute numbers might save hours on shipment, liters of fuel, or dozens of computations. Each one of these uses energy as well and the sum of energy saved through a LLM might vastly outperform its energy cost. A good example is mobile phone operators, of which a ***** expect that AI might reduce power consumption by *** to ******* percent. Considering that much of the world uses mobile phones this would be a considerable energy saver. Emissions are considerable The amount of CO2 emissions from training LLMs is also considerable, with GPT-3 producing nearly *** tonnes of CO2. This again could be radically changed based on the types of energy production creating the emissions. Most data center operators for instance would prefer to have nuclear energy play a key role, a significantly low-emission energy producer.

Recent advances in scaling large language models (LLMs) has resulted in significant improvements over a number of natural language processing benchmarks. There has been some work to pretrain these language models over clinical text. These works demonstrate that training a language model using masked language modeling (MLM) on clinical notes is an effective technique for boosting performance on downstream tasks. All of these previous works use decoder-only architectures. We train 4 different clinical T5 models on the union of MIMIC-III and IV notes. Two of the models are initialized from previous T5-models (T5-base and SciFive). We additionally train a T5-Base and T5-Large model from scratch. These models should not be distributed to non-credentialed users. Research has shown that these language models have the potential to leak sensitive information. Due to this potential risk, we release the model weights under PhysioNet credentialed access.

FileMarket provides premium Large Language Model (LLM) Data designed to support and enhance a wide range of AI applications. Our globally sourced LLM Data sets are meticulously curated to ensure high quality, diversity, and accuracy, making them ideal for training robust and reliable language models. In addition to LLM Data, we also offer comprehensive datasets across Object Detection Data, Machine Learning (ML) Data, Deep Learning (DL) Data, and Biometric Data. Each dataset is carefully crafted to meet the specific needs of cutting-edge AI and machine learning projects.

Key use cases of our Large Language Model (LLM) Data:

Text generation Chatbots and virtual assistants Machine translation Sentiment analysis Speech recognition Content summarization Why choose FileMarket's data:

Object Detection Data: Essential for training AI in image and video analysis. Machine Learning (ML) Data: Ideal for a broad spectrum of applications, from predictive analysis to NLP. Deep Learning (DL) Data: Designed to support complex neural networks and deep learning models. Biometric Data: Specialized for facial recognition, fingerprint analysis, and other biometric applications. FileMarket's premier sources for top-tier Large Language Model (LLM) Data and other specialized datasets ensure your AI projects drive innovation and achieve success across various applications.

Large Language Model (LLM) Market size was valued at USD 4.6 Billion in 2023 and is projected to reach USD 64.9 Billion by 2031, growing at a CAGR of 32.1% during the forecast period 2024-2031.Global Large Language Model (LLM) Market DriversThe market drivers for the Large Language Model (LLM) Market can be influenced by various factors. These may include:Advancements in AI and Machine Learning: Continuous improvements in AI algorithms and machine learning techniques are pushing the capabilities of (LLM), making them more attractive for a variety of applications.Increasing Demand for Automation: Businesses and industries are increasingly seeking automation solutions for customer service, content creation, and data analysis, which drives the demand for (LLM).Rising Investments in AI: There is a significant influx of investments from both private and public sectors in AI research and development, fostering the growth of the (LLM) market.Expanding Application Areas: (LLM) are being applied in a wider range of fields such as healthcare, finance, legal, and education, which broadens their market scope.Enhanced Computing Power: Improvements in computing infrastructure, including the advent of advanced GPUs and cloud computing services, are making it feasible to train and deploy large language models more efficiently.Growing Digital Transformation Initiatives: Companies undergoing digital transformation are adopting (LLM) to leverage their capabilities in natural language understanding and generation for improved business processes.Increased Availability of Data: The abundance of text data from the internet and other sources provides the necessary training material for developing more sophisticated (LLM).Consumer Demand for Better User Experiences: There is a growing expectation for intuitive and responsive user interfaces enabled by (LLM), particularly in applications like virtual assistants and catboats.Developments in Natural Language Processing: Progress in natural language processing (NLP) techniques contributes to more effective and efficient (LLM), enhancing their practical utility and market value.Regulatory and Compliance Requirements: Certain industries are leveraging (LLM) to ensure compliance with legal and regulatory standards by automating documentation and reporting tasks.

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Open-Source LLM Market Size 2025-2029

The open-source LLM market size is forecast to increase by USD 54 billion at a CAGR of 33.7% between 2024 and 2029.

The market is experiencing significant growth, driven by the increasing democratization and compelling economics of large language models. The proliferation of smaller organizations and research institutions adopting these models is a key trend, as they offer cost-effective solutions for various applications. However, challenges persist in the form of prohibitive computational costs and critical hardware dependency. These obstacles necessitate the development of more efficient algorithms and the exploration of cloud computing solutions.
Companies seeking to capitalize on market opportunities must focus on optimizing resource utilization and collaborating with hardware manufacturers to address hardware dependency. By staying abreast of technological advancements and strategic partnerships, organizations can effectively navigate these challenges and thrive in the dynamic Open-Source LLM landscape. Software integration, version control, and cost optimization are key trends shaping the deployment pipeline. Computer vision and image recognition are transforming industries like healthcare and education. 

What will be the Size of the Open-Source LLM Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
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In the dynamic open-source Large Language Model (LLM) market, dialogue systems are being fine-tuned for improved performance tuning and resource utilization. Model explainability gains significance as users demand transparency, leading to innovation in LLM architecture. Adversarial attacks pose a challenge, necessitating robustness testing and model monitoring. Text summarization and machine translation applications continue to drive growth, with sequence-to-sequence models gaining popularity. Knowledge graph technology enhances contextual awareness, while natural language processing facilitates conversational AI. Security considerations, including API documentation and data annotation, are crucial for model reliability. Knowledge graph integration and model debugging are essential for enhancing model performance and fairness metrics.

Model reliability is further enhanced through model debugging and model explainability. Bias detection and data preprocessing are crucial for ensuring model accuracy and trustworthiness. Token embeddings and robustness testing are essential for model optimization and model debugging. Overall, the market is characterized by continuous innovation, collaboration, and a strong focus on model performance, reliability, and fairness. Deep learning models and transformer networks are essential for text summarization techniques, model fine-tuning strategies, and recurrent neural networks.

How is this Open-Source LLM Industry segmented?

The open-source LLM industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Application

  Technology and software
  Finance and banking
  Healthcare and biotechnology
  E-commerce and retail
  Others


Deployment

  On-premises
  Cloud


Type

  Transformer-based models
  Multilingual models
  Conditional and generative models
  Others


Geography

  North America

    US
    Canada
    Mexico


  Europe

    France
    Germany
    UK


  APAC

    China
    India
    Japan
    South Korea


  Rest of World (ROW)

By Application Insights

The Technology and software segment is estimated to witness significant growth during the forecast period. The Open-Source Large Language Model (LLM) market is witnessing significant growth and innovation in the Technology and Software segment. This domain is not just a consumer of the technology but its primary incubator, fostering a symbiotic relationship between software development and advanced language models. Transparency, control, and freedom from company lock-in are key drivers for the adoption of open-source LLMs in this sector. Performance benchmarking, model governance, model interpretability, data augmentation, model scalability, bias mitigation techniques, and embedding techniques are all integral components shaping the market's dynamics. Key technologies such as edge computing, augmented reality, and virtual reality are also contributing to the market's expansion.

Evolving trends include the integration of advanced techniques like attention mechanisms, transformer networks, and prompt engineering to enhance model performance and adaptability. Ethical considerations and data privacy are increasingly becoming essential aspects of model development, with a growing em

A June 2025 study found that Reddit was the most frequently cited web domain by large language models (LLMs). The platform was referenced in approximately 40 percent of the analyzed cases, likely due to the content licensing agreement between Google and Reddit in early 2024 for the purpose of AI models training. Wikipedia ranked second, being mentioned in roughly 26 percent of the times, while Google and YouTube were mentioned 23 percent.

Off-the-shelf 2 millions pairs SFT text data. Contains 12 types of SFT QA, and the accuracy is not less than 95%. All prompts are manually written to meet diversity coverage.

Supplementary material and data for Pfohl and Cole-Lewis et al., "A Toolbox for Surfacing Health Equity Harms and Biases in Large Language Models" (2024).

We include the sets of adversarial questions for each of the seven EquityMedQA datasets (OMAQ, EHAI, FBRT-Manual, FBRT-LLM, TRINDS, CC-Manual, and CC-LLM), the three other non-EquityMedQA datasets used in this work (HealthSearchQA, Mixed MMQA-OMAQ, and Omiye et al.), as well as the data generated as a part of the empirical study, including the generated model outputs (Med-PaLM 2 [1] primarily, with Med-PaLM [2] answers for pairwise analyses) and ratings from human annotators (physicians, health equity experts, and consumers). See the paper for details on all datasets.

We include other datasets evaluated in this work: HealthSearchQA [2], Mixed MMQA-OMAQ, and Omiye et al [3].

• Mixed MMQA-OMAQ is composed of the 140 question subset of MultiMedQA questions described in [1,2] with an additional 100 questions from OMAQ (described below). The 140 MultiMedQA questions are composed of 100 from HealthSearchQA, 20 from LiveQA [4], and 20 from MedicationQA [5]. In the data presented here, we do not reproduce the text of the questions from LiveQA and MedicationQA. For LiveQA, we instead use identifier that correspond to those presented in the original dataset. For MedicationQA, we designate "MedicationQA_N" to refer to the N-th row of MedicationQA (0-indexed).

A limited number of data elements described in the paper are not included here. The following elements are excluded:

1. The reference answers written by physicians to HealthSearchQA questions, introduced in [2], and the set of corresponding pairwise ratings. This accounts for 2,122 rated instances.

2. The free-text comments written by raters during the ratings process.

3. Demographic information associated with the consumer raters (only age group information is included).

References

1. Singhal, K., et al. Towards expert-level medical question answering with large language models. arXiv preprint arXiv:2305.09617 (2023).

2. Singhal, K., Azizi, S., Tu, T. et al. Large language models encode clinical knowledge. Nature 620, 172–180 (2023). https://doi.org/10.1038/s41586-023-06291-2

3. Omiye, J.A., Lester, J.C., Spichak, S. et al. Large language models propagate race-based medicine. npj Digit. Med. 6, 195 (2023). https://doi.org/10.1038/s41746-023-00939-z

4. Abacha, Asma Ben, et al. "Overview of the medical question answering task at TREC 2017 LiveQA." TREC. 2017.

5. Abacha, Asma Ben, et al. "Bridging the gap between consumers’ medication questions and trusted answers." MEDINFO 2019: Health and Wellbeing e-Networks for All. IOS Press, 2019. 25-29.

Description of files and sheets

1. Independent Ratings [ratings_independent.csv]: Contains ratings of the presence of bias and its dimensions in Med-PaLM 2 outputs using the independent assessment rubric for each of the datasets studied. The primary response regarding the presence of bias is encoded in the column bias_presence with three possible values (No bias, Minor bias, Severe bias). Binary assessments of the dimensions of bias are encoded in separate columns (e.g., inaccuracy_for_some_axes). Instances for the Mixed MMQA-OMAQ dataset are triple-rated for each rater group; other datasets are single-rated. Instances were missing for five instances in MMQA-OMAQ and two instances in CC-Manual. This file contains 7,519 rated instances.

2. Paired Ratings [ratings_pairwise.csv]: Contains comparisons of the presence or degree of bias and its dimensions in Med-PaLM and Med-PaLM 2 outputs for each of the datasets studied. Pairwise responses are encoded in terms of two binary columns corresponding to which of the answers was judged to contain a greater degree of bias (e.g., Med-PaLM-2_answer_more_bias). Dimensions of bias are encoded in the same way as for ratings_independent.csv. Instances for the Mixed MMQA-OMAQ dataset are triple-rated for each rater group; other datasets are single-rated. Four ratings were missing (one for EHAI, two for FRT-Manual, one for FBRT-LLM). This file contains 6,446 rated instances.

3. Counterfactual Paired Ratings [ratings_counterfactual.csv]: Contains ratings under the counterfactual rubric for pairs of questions defined in the CC-Manual and CC-LLM datasets. Contains a binary assessment of the presence of bias (bias_presence), columns for each dimension of bias, and categorical columns corresponding to other elements of the rubric (ideal_answers_diff, how_answers_diff). Instances for the CC-Manual dataset are triple-rated, instances for CC-LLM are single-rated. Due to a data processing error, we removed questions that refer to `Natal'' from the analysis of the counterfactual rubric on the CC-Manual dataset. This affects three questions (corresponding to 21 pairs) derived from one seed question based on the TRINDS dataset. This file contains 1,012 rated instances.

4. Open-ended Medical Adversarial Queries (OMAQ) [equitymedqa_omaq.csv]: Contains questions that compose the OMAQ dataset. The OMAQ dataset was first described in [1].

5. Equity in Health AI (EHAI) [equitymedqa_ehai.csv]: Contains questions that compose the EHAI dataset.

6. Failure-Based Red Teaming - Manual (FBRT-Manual) [equitymedqa_fbrt_manual.csv]: Contains questions that compose the FBRT-Manual dataset.

7. Failure-Based Red Teaming - LLM (FBRT-LLM); full [equitymedqa_fbrt_llm.csv]: Contains questions that compose the extended FBRT-LLM dataset.

8. Failure-Based Red Teaming - LLM (FBRT-LLM) [equitymedqa_fbrt_llm_661_sampled.csv]: Contains questions that compose the sampled FBRT-LLM dataset used in the empirical study.

9. TRopical and INfectious DiseaseS (TRINDS) [equitymedqa_trinds.csv]: Contains questions that compose the TRINDS dataset.

10. Counterfactual Context - Manual (CC-Manual) [equitymedqa_cc_manual.csv]: Contains pairs of questions that compose the CC-Manual dataset.

11. Counterfactual Context - LLM (CC-LLM) [equitymedqa_cc_llm.csv]: Contains pairs of questions that compose the CC-LLM dataset.

12. HealthSearchQA [other_datasets_healthsearchqa.csv]: Contains questions sampled from the HealthSearchQA dataset [1,2].

13. Mixed MMQA-OMAQ [other_datasets_mixed_mmqa_omaq]: Contains questions that compose the Mixed MMQA-OMAQ dataset.

14. Omiye et al. [other datasets_omiye_et_al]: Contains questions proposed in Omiye et al. [3].

Version history

Version 2: Updated to include ratings and generated model outputs. Dataset files were updated to include unique ids associated with each question. Version 1: Contained datasets of questions without ratings. Consistent with v1 available as a preprint on Arxiv (https://arxiv.org/abs/2403.12025)

WARNING: These datasets contain adversarial questions designed specifically to probe biases in AI systems. They can include human-written and model-generated language and content that may be inaccurate, misleading, biased, disturbing, sensitive, or offensive.

NOTE: the content of this research repository (i) is not intended to be a medical device; and (ii) is not intended for clinical use of any kind, including but not limited to diagnosis or prognosis.

Introduction

The global enterprise LLM market is undergoing rapid expansion, supported by the accelerating adoption of large language models in business operations, knowledge management, and customer engagement. In 2024, the market was valued at approximately USD 4,500.1 million, and it is projected to reach nearly USD 58,324 million by 2034, reflecting a strong CAGR of 29.2% between 2025 and 2034. The growth is being fueled by the ability of LLMs to streamline workflows, enhance decision-making, and reduce costs through automation and intelligent data processing across industries.

The Enterprise Large Language Model (LLM) market refers to the use of advanced AI language models tailored to meet the needs of businesses and organizations. These models help automate complex workflows, enhance decision-making, and improve customer interactions by processing vast amounts of data and generating human-like text. Enterprises use LLM technology to unlock value from unstructured data across diverse functions such as customer service, data analysis, and content creation.

A major driving factor behind the growth of the Enterprise LLM market is the increasing demand for intelligent automation across business processes. Organizations actively seek to deploy LLM-powered tools like chatbots and virtual assistants that deliver personalized, 24/7 customer service, reduce operational costs, and allow employees to focus on more strategic tasks. Furthermore, LLMs' ability to extract insights from large volumes of unstructured data accelerates data-driven decision-making, offering enterprises a competitive edge.

https://market.us/wp-content/uploads/2025/09/Enterprise-LLM-Market-size.png" alt="Enterprise LLM Market size" width="1216" height="706">

ABSTRACT:

Context: Large Language Models (LLMs) have revolutionized natural language generation and understanding. However, they raise significant data privacy concerns, especially when sensitive data is processed and stored by third parties.
Goal: This paper investigates the perception of software development teams members regarding data privacy when using LLMs in their professional activities. Additionally, we examine the challenges faced and the practices adopted by these practitioners.
Method: We conducted a survey with 78 ICT practitioners from five regions of the country.
Results: Software development teams members have basic knowledge about data privacy and LGPD, but most have never received formal training on LLMs and possess only basic knowledge about them. Their main concerns include the leakage of sensitive data and the misuse of personal data. To mitigate risks, they avoid using sensitive data and implement anonymization techniques. The primary challenges practitioners face are ensuring transparency in the use of LLMs and minimizing data collection. Software development teams members consider current legislation inadequate for protecting data privacy in the context of LLM use.
Conclusions: The results reveal a need to improve knowledge and practices related to data privacy in the context of LLM use. According to software development teams members, organizations need to invest in training, develop new tools, and adopt more robust policies to protect user data privacy. They advocate for a multifaceted approach that combines education, technology, and regulation to ensure the safe and responsible use of LLMs.