How Much Does It Cost to Develop an AI Solution or Custom LLM?
A cute robot is developing in a laptop. AI-generated content.
TL;DR: AI development costs depend on project complexity, model type, and data needs, ranging from under $30k to over $1 million.
Summary
AI development expenses vary based on the project’s scope, data size, and customization level. Pre-trained model projects typically cost less than $30,000, while fully custom large language models can exceed $1 million. This guide explains key cost drivers, real examples, and budget management tips.
From customer support chatbots to automated reports and writing assistants, artificial intelligence (AI), is already powering everyday tools. Now, more companies are developing their own AI solutions to save time, analyze data faster, and improve efficiency. This growing demand is pushing global AI software spending past $300 billion by 2026.
But if building your own AI product is on your roadmap, you’re probably wondering: How much does it cost to develop an AI solution? Prices vary widely. Smaller AI builds can come in under $20,000, while large, complex systems, especially those involving a new model development, can run well past $1 million.
In this guide, we’ll walk you through the key factors that affect pricing, so you can make a smarter, better-informed decision for your business.
What Affects AI Development Cost?
The cost of building an AI solution can vary a lot. You might be working on a simple chatbot or building a complex custom large language model trained on your company’s records. Either way, your AI project budget depends on a few key things. Here’s what can affect the total bill:
1. Scope and Complexity
The more complex your idea, the higher the price. A basic AI chatbot in 2025 might cost around $5,000. But advanced AI-powered tools for business, like those used by large companies, can go over $1 million.
If you’re building something that reads contracts, analyzes X-rays, or assists with financial decisions, your AI development budget will be much higher. These tools require more sophisticated logic, higher-quality data, and extra fine-tuning.
For example, AI software development for medical image analysis can range anywhere from $300,000 to $2 million, depending on how accurate and advanced it needs to be.
2. Model Choice
The model you choose plays a big role in the cost of building custom AI. Using pre-trained models like GPT-4, Claude, or Gemini can save you time and money. But if you need a fully custom LLM development, it will be much more expensive.
Training GPT-3, for example, can range anywhere between $500,000 and $4.6 million, based on how you set things up. Training Meta’s Llama 3? That reportedly starts at $500 million, according to PYMNTS.
3. Data Requirements
AI only works well when it has the right inputs. But getting that data ready can take a lot of time and money. You may need to clean it, label it, remove sensitive information, or pull it from various systems. The more work it takes, the more it adds to your budget.
Inference estimates that data prep can account for 15 to 25% of the total cost of building AI for business automation. For more complicated projects, InvoZone says it can take up 30 to 40% of the total AI development budget.
Depending on the level of detail and how the data will be used, this step alone can cost anywhere from $10,000 to over $1 million.
If your inputs are sensitive, your LLM project budget will also need to include additional safeguards, such as secure storage, privacy checks, and legal reviews. According to AIcost.ai, working in highly regulated industries can raise your overall project spending by 40% to 80%.
Extra tools can also drive up your total bill. Softermii notes that secure data systems may cost as little as $15,000 or go beyond $100,000. Legal and privacy compliance tools, according to Callin.io, may add between $10,000 and $50,000. And if you need to comply with GDPR rules, ComplyDog reports that setup fees typically range from $15,000 to $75,000.
4. Level of Customization
Using ready-made tools or APIs is usually faster and cheaper. But if you need something built specifically for your business, your AI software development costs will be higher. That’s because custom projects often involve writing specialized prompts, designing a tailored user experience, and conducting additional rounds of testing.
Capella Solutions, citing Gartner data, reports that a fully customized AI build can go from $500,000 to $1 million. The final price depends on how big the project is and how complex the features need to be. Aeologic says smaller custom tools usually start between $50,000 and $150,000. More advanced tools fall in the $150,000 to $500,000 range, while large-scale systems can cost anywhere from $500,000 to over $2 million.
5. System Requirements
System requirements include cloud hosting, data storage, and the GPU power needed to train models. If you’re just using an API like OpenAI’s, you won’t spend much on infrastructure costs. But if you’re building and training your own models, you’ll need much more powerful and expensive technology.
According to Coherent Solutions, infrastructure and tech tools usually make up 15% to 20% of the total AI project budget. But that only applies to mid-sized natural language processing work. Meanwhile, computer power alone can account for 30% to 70% of your cloud bill. Storage may add another 10% to 20%, and networking or data transfers can require an extra 5% to 15%.
6. People You Hire
Who you hire to build your AI solution has a major impact on your overall budget. A freelance developer might be more affordable, but the work could take longer or fall short of your needs.
On the other hand, a small development team or an experienced AI agency can typically deliver results faster and help you avoid common pitfalls. If you hire full-time AI researchers or data scientists, you’ll also need to pay for their salaries, tools, and management. So, your cost to build LLM or launch AI-powered tools for business will depend a lot on the team you choose.
Freelancer Files notes that on UpWork, beginner AI and machine learning specialists charge about $20 per hour. Intermediate professionals charge between $25 and $52, while experts average around $95 per hour. According to Cealea, mid-level remote workers generally charge $30 to $60 per hour, and top-tier experts can earn more than $100 per hour.
Hiring full-time staff tends to be more expensive in the long run. Mason Alexander reports that, as of 2025, AI engineers earn around $175,000 per year, while machine learning engineers make about $158,000 annually.
If you choose to work with a development agency, Clutch says software development firms typically charge between $25 and $49 per hour. These teams often build things like chatbots, machine learning tools, natural language processing systems, and robotics solutions.
7. Ongoing Maintenance and Updates
AI tools aren’t something you build once and forget. After launch, they require regular updates, bug fixes, and retraining. It’s a must especially if your data evolves or your business processes shift. You may also need to refine prompts, check how well the system is working, or upgrade to newer models. All of this adds to the total price of AI software development, particularly if you want your tool to grow with your business.
According to Markovate, keeping an AI system running usually costs about 15% to 20% of your
original AI development budget each year. So if you spent $100,000 to build it, yearly maintenance could range from $15,000 to $20,000.
Medium shares a higher estimate for more advanced systems. If your AI needs constant updates, frequent retraining, or extra server power, yearly spending can rise to 30% to 50% of the original build. For small or mid-size tools, that means maintenance may cost between $50,000 and $200,000 per year.
8. Licensing and Legal Considerations
Some models (like Meta’s Llama 3) are open source and free to use for many types of projects. But that doesn’t mean you can use them however you want. Many open-weight models come with licenses that limit commercial use, require attribution, or restrict modifications.
According to TechCrunch, Meta’s Llama 3 and Google’s Gemma are labeled as “open,” but both include rules that can affect how you use them for business. For instance, Meta’s license prohibits using Llama 3’s outputs to train other models. It also blocks companies with over 700 million users from using it without a special agreement. Gemma’s license is more flexible, but Google retains the right to revoke access if it determines you’ve violated its terms or legal requirements.
The EU AI Act is expected to take full effect by August 2026. It could impact any company that works with customers or partners in Europe, even if you’re based in the U.S. If your system is classified as high-risk, getting it compliant could add around 17% to your total AI development cost. Using a banned tool may result in penalties of up to 7% of your company’s global revenue. Even minor issues, such as missing paperwork or failure to report incidents, can bring serious consequences.
Below is a breakdown of the key factors that influence your AI project budget and why each one matters.
Cost Factor
Why It Matters
Use Case Complexity
More complex problems = more dev time and resources
Model Choice
Pretrained models are cheaper than custom ones
Data Requirements
Cleaning, labeling, and volume drive up the cost
Customization Level
Fully tailored solutions take longer to build
Infrastructure Needs
Training models = higher cloud compute costs
Team & Talent
In-house, agency, or offshore team impacts rates
Maintenance & Updates
AI needs ongoing tuning, retraining, and monitoring
Legal & Licensing
API/model licenses and compliance add to the ongoing cost
Cost Breakdown by Project Type
So, how much does it cost to develop an AI solution? There’s no fixed price. Your AI development cost depends on the type of tool you’re building, how custom it needs to be, and how much data or infrastructure is involved. A simple chatbot might be relatively affordable, while a fully custom LLM development project could take a much larger budget.
Let’s look at four common types of AI projects and their typical prices ranges:
How Generative AI and AI Coding Tools Power Vibe-Coding
At the heart of vibe-coding is generative AI, reshaping how development teams write, debug, and ship software. These AI coding tools don’t just autocomplete code; they act as intelligent collaborators that help your team move faster with fewer roadblocks.
Basic AI Feature (e.g., Smart Chatbot or Search Tool)
Example: A SaaS company adds a GPT-powered chatbot to help answer common customer questions.
This is one of the most affordable ways to use AI for business automation. Instead of training your own model, you’re leveraging one that already exists, like OpenAI, Cohere, or Claude, and integrating it into your app. Most of the work involves writing effective prompts, building a simple interface, and ensuring the responses make sense. You won’t need to do much data cleanup or fine-tuning.
Why it’s cheap:
Since you’re using a model that’s already trained, you can skip the toughest and most expensive parts, like training it from scratch or setting up complex infrastructure. Most of your money will go toward prompt engineering, integration, and paying for API usage.
Estimated cost:
$5,000 to $30,000
This range usually covers design, dev work, prompt testing, and integration.
For example, Cenozic reports that a basic rule-based chatbot usually costs between $5,000 and $10,000 to build. These bots follow fixed scripts and don’t use AI to generate responses.
If you want to add a simple AI feature, such as connecting your SaaS product to the ChatGPT API, the cost is still fairly low. According to estimates from Stormotion, it typically takes 120 to 500 hours of work, which adds up to around $6,000 to $25,000 at an average rate of $50 per hour.
Mid-Range Customization (Fine-Tuning an LLM)
Example: A healthcare company fine-tunes a language model using its own medical documents to improve how doctors record patient notes.
This is where true custom LLM development usually begins. You’re starting with a base model, like LLaMA or Mistral, and training it to understand your specific domain. The process often includes cleaning large datasets, running training cycles, and evaluating the results. Additional security is also critical, especially when handling sensitive information like patient records.
What drives up the LLM project budget here:
You’re paying for data formatting, fine-tuning, testing, and backend work to connect the model to your app. It also requires skilled machine learning engineers, access to high-performance GPUs, and secure infrastructure. All of this adds to the overall effort and expenses.
Estimated cost:
$30,000-$300,000
For reference, Applied AI noted that the GPU spending for fine-tuning LLaMA models usually ranges from tens to thousands of dollars, depending on the size of the model and how long it runs. But GPU usage is only one part of the equation. Once you factor in labor, data preparation, and infrastructure, the total costs will absolutely go higher.
Follow Life reports that fine-tuning open-source models like Mistral, LLaMA, or Falcon comes with a price tag of around $30,000 to $300,000. The final price depends on how complex the use case is and how large the project needs to be.
Fully Custom LLM Development
Example: A financial firm builds its own private model using years of internal reports, filings, client conversations, and emails.
This is one of the most complex and expensive types of AI projects. You’re building a model from scratch, which means you’ll need a full team of ML engineers, data scientists, and MLOps experts. You’ll also need robust infrastructure, including GPUs, cloud storage, and systems capable of processing large volumes of data. Developing a functional model usually involves several training runs and extensive iteration.
Why the LLM development cost is so high:
You’re paying for everything, such as engineering, infrastructure, data pipelines, security, training, and ongoing monitoring. It’s a major investment, but it gives you full control over both your model and your data.
Estimated cost:
$500,000 to $1.5M+
According to Follow Life, a typical LLM project budget in 2025 depends a lot on the sizeand complexity of the project:
- Fine-tuning an open-source LLM: $100,000 to $500,000
- Building a 7B model from scratch: $1.5 million to $3 million
- Building a 30B+ model from scratch: $5 million to over $10 million
These estimates cover everything from data prep and engineering to compute, compliance, and deployment. The article points out that smaller startups with focused goals usually stay on the lower end, while larger companies seeking full ownership and cutting-edge performance tend to spend much more on infrastructure and talent.
Here’s a quick look at what you might expect to spend on different AI projects, based on their size and difficulty.
Project Type
Description
Estimated Cost
Basic AI Feature
GPT-based chatbot or smart search with minimal setup
$5,000 – $30,000
Mid-Range Customization
Fine-tuned model with domain-specific data
$50,000 – $300,000
Fully Custom LLM Build
Private, fully trained model with end-to-end stack
$500,000 – $1.5M+
5 Hidden or Unexpected Costs to Watch For in Custom LLM Development
Even after the initial build, hidden costs can quietly eat into your LLM development expenses if you’re not prepared. Here are some common ones to plan for:
1. Data Cleaning & Labeling
Having data isn’t the same as having training-ready data. You may need to clean, structure, and manually label it, which can be a time-consuming and often expensive process, especially when dealing with large or sensitive datasets.
2. Ongoing API Usage Fees
3. System Setup Expenses
Running or fine-tuning a model requires significant computing power. Cloud fees, especially for GPU usage, can spike during training or periods of heavy demand. This is a major component of your total LLM development cost.
4. Compliance & Privacy Reviews
Working with financial data, healthcare records, or personal information often involves extra steps like legal reviews, data audits, and compliance with rules such as GDPR or HIPAA. These processes take time and may require specialized legal or security consultants.
5. Maintenance & Updates
AI models don’t stay accurate forever. As information evolves or user behavior shifts, the model may require regular updates, retraining, or fine-tuning to maintain accuracy and relevance. Ongoing maintenance should be a part of your long-term budget for custom LLM development.
Real-World Examples of AI Software Development
Looking at what other companies have spent on AI-powered tools for business can help you plan your own AI project budget more effectively. Here are a few real examples to give you a clearer idea of the possible costs.
Internal AI Assistant for Company Knowledge
Dentro helps clients build an internal AI assistant: a smarter version of ChatGPT that understands your company’s documents. It can answer employee questions instantly using internal content like HR policies, sales manuals, or technical documentation.
What It Does:
- Searches your internal knowledge base to deliver fast, accurate answers
- Supports both text and image-based content
- Can be integrated into a custom interface
- Lets you easily manage or update the knowledge base
Typical Cost: $11,000–$38,000
What Affects the Cost:
- Number and type of documents (text, images, formats)
- Complexity of the user interface
- Level of sophistication needed in search and conversation features
Use Case:
It saves employees time by eliminating the need to dig through files or ask colleagues for information, making company knowledge instantly accessible.
Gen AI Sales Training Platform
A U.S.-based SaaS company wanted to speed up the onboarding of new sales reps. Traditionally, onboarding took six months and cost over $100,000 per hire. To streamline this process, the firm partnered with ITRex to build a generative AI-powered training platform.
Project Details:
- Cost: $100K–$200K
- Timeline: 2 to 4 months
- Team: 1 AI engineer, 1 front-end dev, 1 back-end dev, part-time QA, and a project manager
What ITRex Did:
- Built a Gen AI platform using GPT-4 and a custom RAG (retrieval-augmented generation) system.
- Converted company documents into a searchable knowledge base for the AI to learn from.
- Added helpful features such as role-based training, adjustable difficulty levels, and live Q&A
- Used Microsoft Azure to run the platform and made sure the AI components could be easily updated in the future
Results:
- Onboarded new sales team members 92% faster
- Enabled the platform to create personalized training in just a few hours
- Reduced the training burden on senior staff
- Only 20% of the total budget was spent on AI. Most of it went toward building core product features like subscriptions and user roles.
Why Choose Custom AI Services?
Off-the-shelf tools like ChatGPT or Claude work well for quick tasks. But if your business has specific needs, such as secure data handling, seamless integration with your current systems, or unique workflows that generic tools can’t support — it may be time to invest in a custom solution.
Custom AI services are built around your exact goals. This means more accurate results, easier integration with the tools you already use, and better control as your details and team evolve. If you’re exploring AI for business automation or custom LLM development, a tailored solution offers greater flexibility and long-term value.
Custom tools also provide peace of mind when it comes to legal and security requirements. From the start, your system can be designed to meet your privacy, compliance, and security standards, without relying on workarounds or patches later on.
5 Tips to Manage or Reduce AI Development Cost
Worried your AI project might get too expensive? You’re not alone. Here are five practical ways to keep your development budget under control:
1. Start with a PoC or MVP
Instead of diving into a full build, begin with a proof of concept (PoC) or minimum viable product (MVP) to test value early. It’s faster, cheaper, and helps you stay focused.
2. Use Pre-Trained Models and APIs
You don’t always need to reinvent the wheel. Tools like GPT-4, Claude, Mistral, or even open-source models can save your significant time and money.
3. Leverage Cloud AI Platforms
Platforms like AWS SageMaker, Azure Machine Learning, or Google Vertex AI provide scalable infrastructure without upfront hardware fees.
4. Focus on the 80/20 Wins
Prioritize high-impact use cases first. You’ll often get 80% of the results from just 20% of the effort.
5. Work with Nearshore or Offshore Teams
If local development expenses are high, consider experienced AI developers in Latin America, Eastern Europe, or Southeast Asia. They can deliver similar quality at a lower rate.
Final Thoughts: Plan Smart, Build Smart
AI development cost depends on what you’re building, how customized it needs to be, and the team or tools you use. The best results come from planning early, staying focused, and budgeting beyond the initial build, no matter what project you’re working on. That includes everything from a simple chatbot to a fully custom LLM.
To keep costs in check, start with clear goals and a small team. A proof of concept (PoC) or minimum viable product (MVP) allows you to test your idea early without overspending. Also, remember: building the AI is just the beginning. Ongoing expenses like API usage, cloud services, and maintenance can add up over time, so be sure to plan for those as well.
AI-powered tools for business can bring real value, but only when they’re planned and built with care.
If you’re exploring your next AI project, Rio Data can help you take a smart, cost-effective path forward. Contact us today to get started.