Large Language Model (LLM) Development and Consulting

FAQ

• What is a large language model?

  A large language model is a type of artificial intelligence that relies on a wide range of NLP, deep learning, and ML algorithms to understand the structure of the language. It is trained on a very large dataset to generate accurate responses and catch up with conversations.

  Large language models have been shown to outperform traditional models on a variety of tasks, including machine translation, question answering, and sentiment analysis. Also, unlike traditional chatbots and virtual assistants, LLMs can come in handy for a variety of tasks, including text generation, image captioning, summarization, and other large language models use cases.

• What are the major large language models?

  Large language models examples include the GPT model which is trained on a dataset of 570 GB and fine-tuned for a variety of language tasks, such as translation, summarization, and question-answering. The model is 175 billion parameters in size, which makes it the largest language model ever trained.

  Megatron is another example of a large, powerful transformer with 11 billion parameters. Our team also works with OpenLLaMA, StableLM, PaL, and other major conversational AI solutions. We select the right LLM that suits your business needs and workloads.

• How is a large language model created?

  A large language model is created by training a neural network on a large corpus of text. The neural network learns to predict the next word in a sequence, based on the previous words in the sequence. The more parameters the mode has, the more capable it is, and the more training data it needs to score a high accuracy rate.

  Unlike traditional AI software, LLMs are general purpose and can be fine-tuned to match the specific needs of a given business. From sentiment analysis to content generation to granular recommendations, language models can support business operations across multiple areas.

• How much does a large language model cost?

  The cost of developing, training, and deploying a large language model can vary significantly depending on several factors, including the model’s size, complexity, usage, and whether you’re building it in-house or using a cloud-based API. Here’s an overview of the potential costs involved:

  • Training сosts
  • Hardware and compute сosts
  • Data сosts
  • Operational costs (Maintenance and fine-tuning).
  • Development team costs.
• What are the stages of LLM development?

  The development of a Large Language Model typically involves several key stages, each of which is crucial to building a robust, effective, and scalable model. Below are the primary stages in LLM development:

  1. Problem definition and requirement gathering
  2. Data collection and preprocessing
  3. Model architecture design
  4. Model training
  5. Fine-tuning (Optional)
  6. Evaluation and testing
  7. Model optimization and compression
  8. Deployment and integration
  9. Monitoring and maintenance
  10. Ethical considerations and bias mitigation.
• Is GPT an LLM?

  GPT is one of the most popular language models that is based on the combination of NLP, reinforcement learning, neural networks, and other innovative technologies. This ready-made model can be integrated into applications or customized on proprietary datasets through fine-tuning.

• How much does a large language model cost?

  The cost of a large language model varies widely depending on how it is built and used.

  • Building a custom LLM: Training a model from scratch requires large datasets, expert teams, and high-performance computing infrastructure. Costs typically range from hundreds of thousands to many millions of dollars.
  • Deployment and infrastructure: Running an LLM in production involves ongoing expenses for cloud GPUs, storage, monitoring, and security, which scale with usage.
  • API access: Many organizations use third-party LLM APIs with usage-based pricing (e.g., per token or request), allowing projects to start at relatively low cost and scale gradually.
  • Customization and maintenance: Fine-tuning, integration with internal systems, and ongoing monitoring add additional expenses.

  The cost of a large language model project ranges from a few thousand dollars for API integration, $5k–$300k for fine-tuning, up to $500k–$5M+ for a fully custom model with deployment and infrastructure.

• Who offers mobile app development services that are experienced with integrating large language models?

  Companies experienced in mobile app development with large language model integration include InData Labs, which delivers AI-powered mobile apps with chatbots, AI agents, analytics, and custom LLM solutions.

  Other strong U.S. providers include Globant, ThoughtWorks, IBM Consulting, and Accenture—all known for enterprise mobile apps with AI, automation, and conversational interfaces.

  These firms combine mobile engineering, cloud architecture, and generative AI expertise to deliver scalable apps with LLM-powered assistants, personalization, and advanced analytics.

• What is an LLM assistant, and how can it benefit my business?

  An LLM assistant is an AI-powered solution built on large language models that can understand natural language, generate human-like responses, and perform knowledge-driven tasks such as answering customer inquiries, summarizing documents, automating internal workflows, assisting with research, and supporting decision-making.

  For businesses, this translates into faster customer service, reduced operational costs, improved employee productivity, consistent communication, scalable personalization, and better use of internal data through intelligent search and insights.

  To achieve these benefits reliably and securely, it is important to work with an experienced development partner such as InData Labs, who can design a solution tailored to your infrastructure, ensure data privacy and compliance, integrate the assistant with existing systems, and provide ongoing optimization and governance for long-term performance and ROI.