The world of Web3 is changing significantly with Artificial Intelligence (AI) driving new advanced technologies such as blockchain applications, decentralised finance, non-fungible tokens (NFTs), and other projects. In the case of C-level executives and founders of the company, perhaps the most important strategic choice to be made is whether to use open-source AI frameworks or proprietary in-house AI solutions. This is not an obvious answer, and it is necessary to consider a number of interdependent factors: control, cost, scalability, privacy, and customisation. In the discussion that follows, the two models are evaluated and practical advice is provided to assist leadership teams to decide which model is more useful in achieving their long term goals.

To those who are interested in a more practical, Web3-focused development of AI, I would advise reading our in-depth guide on AI development in Web3 companies.

What Are Proprietary AI Models?

Specific Artificial Intelligence (AI) models are developed, owned, and operated by privately-owned organisations. These models do not publish their code, training data or architectures publicly. They are available to businesses under licences or application programming interfaces (APIs) but the companies purchasing such systems do not take ownership of them nor do they manage them. Examples of proprietary AI models are:

• GPT-4o (OpenAI): It is a powerful system that can multitask, generate texts, and codes simultaneously.

• Gemini 1.5 Pro (Google): A system optimised on complex natural language tasks and on processing of long context windows.

• Claude 3.5 Sonnet (Anthropic): A system designed to be safe and reliable in its responses.

• IBM Watsonx: The most popular in healthcare use cases and enterprise analytics.

Such solutions are normally at the forefront as far as performance, security, and general reliability are concerned but are always followed by a set of strict conditions of use and the corresponding expenses.

They offer powerful computation and high levels of security, but they come with advanced usage demands and periodic charges. To get a deep analysis of the process of combining advanced artificial intelligence with your individual goals, refer to our analysis of custom AI development.

What Are Open-Source AI Models?

By definition, open-source artificial intelligence (AI) models are ready to be consumed publicly. They have their source code and the model parameters, or weights, that have been prerecorded and are freely inspectable, optimised, and repurposed. This ease of access facilitates openness and community-based innovation. The prominent examples of open-source AI models are as follows:

• LLaMA (Meta): Most adopted for tailor-made solutions.

• BERT( Google): Super effective for natural language processing.

• Mixtral-8x22B: Highly adpt for multi language and coding duties.

• Falcon 2: Multimodal features, in particular daylight reading with image-to-text conversion.

• BLOOM: 46 languages multilingual model.

• GPT-NeoX (EleutherAI): Top advanced large language models.

When selecting an open-source model, organisations achieve a lot of flexibility and control, but the model requires a greater degree of technical expertise.

In case you want to read more about how AI is transforming industries, or more about the open-source innovations of Web3, please refer to our recent article on Web3 AI innovations and trends.

Comparison Table: Proprietary vs. Open-Source AI

To get comprehensive ideas about the principles of AI development, refer to our guidebook on AI for Web3 development.

Proprietary AI Advantages & Disadvantages

1. Advantages

• Newest Technology: Tends to be the first in terms of performance, reliability and features.

• Enterprise-Grade Security: Compliance and strong security are built-in, and it is suitable for regulated industries.

• Simple to install: Fast to install. No need for deep technical teams within the house.

• Support: The vendor provides constant model updates and supports.

2. Disadvantages

• Huge Running Costs: Cost of pricing increases as the usage grows.

• Restricted Customisation: Tweaking and adjustments are normally limited.

• Vendor Lock-In: Reliance on one supplier and software licence conditions.

• Data Privacy: Your information can be processed beyond your environment based on terms with the vendor.

Proprietary AI models are normally technologically advanced, highly secure and fast to implement. They do not need to have in-depth technical teams and enjoy professional assistance. Nevertheless, they are at risk of being locked in with vendors and data privacy risks and can become expensive to use quickly when they are customised.

Interested in the deployment of AI agents with the help of these systems? Our resource on the development of AI agents can be of use.

Open-Source AI Advantages and Disadvantages

1. Advantages:

• No Licensing Fee: There is no fee on usage of models, which reduces the cost.

• Full Control: Code and model full access to make customisations.

• Transparency: Model behaviour and usage of data are simpler to audit.

• Community Innovation: Quick fix by open partnership.

2. Disadvantages:

• Complexity of Technicality: Needs a skilled team to set up, integrate and maintain it.

• High Cost: Infrastructure (servers, storage), support and continued maintenance may be costly.

• Security Responsibility: You will be in charge of updates, compliance and vulnerability.

• Slower Deployment: It will be more time consuming to build and alter models.

Open-source AI models do not require any licensing and provide complete control over code and customisation. They are open and undergo fast community innovation. Nevertheless, it is necessary to set up and maintain it with specialist teams and you have to take care of all updates, security, and compliance.

The issue of hidden costs can also be seen in terms of IT infrastructure and support that can be seen in open-source routes. Read about how to build custom AI solutions to learn how to take advantage of open-source AI solutions.

Understanding AI Model Ownership: Renting vs. Buying

Consider proprietary AI to be as an apartment rental:

• You have ready-made space and assistance (characteristics and vendor assistance).

• You are not able to move the walls (change the model).

• You cannot negotiate the lease when it expires or when the rates increase, you have to go.

In the same way, consider open-source AI as purchasing a home:

• You are free to renovate and add (customise the model).

• You do all the maintenance, repairs and upgrades (technical work).

• The house you buy will be more expensive initially, but you will not get into rent traps and will be able to add sustainable value.

The proprietary AI model is similar to renting an apartment: you can move in fast, get some support, but alterations and expenses are determined by the landlord. Open-source AI is similar to purchasing a home, you can do whatever renovation and extensions you wish but it is up to you to maintain and upgrade.

In the case that the reader is interested in customising or creating new AI agents, our guide to building AI agents can provide detailed instructions and professional advice.

Which Should Leaders Use? (Proprietary AI or Open-Source AI)

It does not have a single answer as every path suits various needs:

• Proprietary AI will typically suit:

  • Companies whose AI teams are small.

  • Applications that require a dominant performance, ease, and support by the vendor.

  • Projects that require enterprise level security and quick implementation.

• Open-Source AI would be perfect when you require:

  • Full-control and transparent custom solutions.

  • Not to fall into a vendor lock-in and recurrent licence expenses.

  • To make close integration of AI into the blockchain structure.

  • The abilities and means to operate on complicated models within the company.

There is also the growth of hybrid solutions: a few companies are using proprietary AI in general applications and open-source AI in sensitive or highly customised workflows. In order to witness custom AI in action and get inspired, visit TMX AI, a powerful Web3 AI platform.

Proprietary AI Deployment: What’s Required?

Using proprietary AI models, the large cloud providers, OpenAI (Azure), Google Cloud (Gemini), AWS (Anthropic Claude), and IBM Watson AI, take care of the infrastructure, providing business users a plug-and-play experience. This considerably lowers the necessity of in-house AI architecture but creates long-term dependencies and possible cost increase.

In order to scale the use of proprietary models, businesses must have:

• Cloud AI Infrastructure: AI workloads that are enterprise-grade are run on Azure AI, Google Vertex AI or AWS Bedrock, allowing access to APIs, hosting, and fine-tuning of the models. The platforms are compute, storage, and security friendly but lack customization options.

• Integration of AI Business: Proprietary AI models are commonly provided with pre-built API connectors and SDKs, which enables direct integration with platforms, including Salesforce, Oracle, and SAP, which minimises the need to use middleware. However, when it comes to businesses that have multi-cloud or hybrid information technology platforms, middleware, like MuleSoft, Workato, or Boomi, can help organise AI-powered workflows between different frameworks.

• AI Governance & Compliance teams: Since the models used are proprietary, and at black box level, AI governance experts, compliance officers and data privacy experts should play a vital role in the oversight of AI outputs, fairness and ethical issues.

• Data Science and ML Experts: Proprietary models decrease the necessity of advanced ML competence but companies will still need to call on immediate engineers, AI product administrators, and analysts of a specific space to optimise API call and tune results.

• AI Strategy and ROI Optimisation: Business intelligence groups collaborate with AI strategy consultants to analyse cost-efficiency, API consumption and vendor lock-in risks.

To learn more about best practices in carrying out enterprise-level AI, see our AI development services page.

Open-Source AI Deployment: What’s Required?

The open-source AI models are more controllable and configurable, but they need substantial contribution in AI infrastructure, security, and technical skills. In contrast to proprietary solutions, open-source implementations require an entire stack of an AI system, including the use of GPUs, model fine-tuning and compliance systems. 

Firms need the following to use open-source models:

• A Compute framework: An AI compute requires high performance GPUs (e.g. NVIDIA H100, AMD Instinct or TPU v4 on Google Cloud). Companies can either leverage the on premise GPU clusters (NVIDIA DGX, Lambda Labs), or the AI infrastructure hosted in the cloud (AWS EC2, Google Cloud TPU or Azure ML).

• Open-source MLOps/AI Engineering Teams: An open-source approach needs a distinct MLOps team to run training, fine-tuning, versioning, and deployment of models.Here are machine learning engineers, AI architects, data scientists, prompt engineers, DevOps specialists that guarantee the performance and scale of the model.

• Enterprise Data Pipelines: Open-source models, as opposed to proprietary AI, must be fed and prepared by ETL pipelines. Apache Spark, Databricks, Snowflake, Airflow are some of the tools used to process the large flows of AI data.

• Security & Compliance Frameworks: Security and compliance frameworks: Open-source AI needs to deploy self-hosted security mechanisms like encryption, access controls, and compliance auditing. To overcome the risks of bias, hallucinations, and privacy of data, enterprises need to provide Responsible AI guidelines that will be used to ensure the safety of data.

• Using Cross-Functional AI Pods: Unlike in the case of a conventional IT team, today, AI teams will be working in AI pods where data engineers, ML researchers, AI product managers, and domain experts will integrate. These fast moving AI development teams are constantly updating models and streamlining their deployment.

New to AI in Web3? Start here with our introduction to AI in Web3 and crypto.

Factors to Consider When Choosing Open-Source vs. Proprietary AI

There are no easy formulas to decide which one is better, proprietary AI or open source based on the needs of your organisation. Most companies will prefer to consider a number of issues:

• Business Goals: Are you looking at speed or optimum flexibility?

• Data Sensitivity: Is privacy and local control of data of the essence?

• Budget: Do you have the funding to pay up front on hardware and developers, or would you rather subscribe?

• Technical Capability: Do you or are you able to have in-house specialists?

• Compliance: Does the AI model assist in industry compliance, or cause risks?

• Scalability Requirement: Are you going to see a sudden jump in usage?

• Vendor Reliability: Does it have a long term support?

• Sustainability: Will your decision contribute to the development of the business and advancement of the products?

To get an even more in-depth industry view, we have a blog on the AI of Web3 with the latest analysis and practical advice.

How AI Leaders Customise AI with Proprietary Data

The effectiveness of AI models sharpens when they are tuned using proprietary data of your firm. These involve blockchain transaction history, customer queries, events on smart contracts and platform-specific documentation. In the case of proprietary AI, vendors enable users to customise data to some extent, whether through prompt engineering or secure data injection, not all vendors enable complete model fine-tuning.

You can do a complete re-training or fine-tuning of the model on your sensitive data with open-source AI, providing exclusive insights and decision-making capabilities in your Web3 business context.

Find out how to incorporate custom data with confidence in our custom AI solutions article.

Among the things that make AI Leaders different is the fact that they are confident that they can tailor their AI initiatives to maximum value. This does not imply that an organisation has to create its models all by itself so that it can be unique. Rather, it is able to customise existing AI models through the one thing that no one else possesses: proprietary enterprise data.

How Proprietary Data Brings Enterprise Context to AI Models

Your data will provide context, which will enhance accuracy in:

• Detection of fraud and anomaly in blockchain ledger.

• Smart contract auditing.

• Customised NFT suggestions.

• Individualised DeFi platform and risk analysis.

There is also potential in less generic AIs where specific models are trained privately on small datasets to perform better at specific tasks within a sector than generic, vendor-trained AIs. Nevertheless, data privacy and compliance are vital and, particularly, in the case of external models.

Three are the main systems of feeding proprietary data to an AI model: prompt engineering, retrieval augmented generation (RAG) and fine-tuning.

• Prompt engineering implies the incorporation of proprietary data into the prompt given to the AI.

• Retrieval augmented generation (RAG) is the concept of connecting an AI model to a proprietary database. This database can be accessed in answering prompts because the model has the ability to draw the relevant information.

• Fine-tuning refers to providing an AI model with sufficient further data so that some of its parameters are altered. Fine-tuning alters the behavior of a model permanently, and scales it to a specific use case or context. It is also less expensive and quicker to train as compared to a new model.

In more complex use cases, it is also possible to provide a guide on best practices and common mistakes when building AI agents on Web3.

How Much Does It Cost to Run an Open-Source vs. Proprietary AI Model? (in SGD)

Medium and large Web3 companies:
(Estimated; 2025 values, SGD converted at approximate rates)

Note: Most proprietary models tend to transfer costs related to infrastructure to licensing whereas with open source greater initial contribution on both hardware and talent is needed.

Licensing Considerations for Proprietary Models: Security and Privacy

• Limited conditions: Decide what you can do and how you can use the model of AI.

• No Model Ownership: You are just licenced to access not to own, alter or redistributing.

• Data Handling: Ask whether your data is utilised in training of your vendors or held elsewhere.

• Security Guarantees: Vendors usually carry robust data security and compliance to their business users, although they might still reserve their rights to use a specific data.

• Limits of Fine-Tuning: A significant proportion of proprietary models have rules against retraining entirely on proprietary data.

Licensing Considerations for Open-Source Models

• Permissive Licences: (e.g. Apache 2.0, MIT) permit wide use, even commercial use.

• Reciprocal licences: (e.g. GPL, AGPL, RAIL) may demand that your modifications are returned or not commercial.

• Data licences: Certain data sets are licensed separately--read code as well as dataset licences.

• Compliance Risks: Developing your business carelessly with the help of open-source code may subject it to IP, reputational or legal risks.

Open-source licences can be permissive (e.g. Apache or MIT), or reciprocal (e.g. GPL or RAIL), which can obligate you to share any improvements. Watch out datasets may contain special terms. See additional pitfalls and best practices in our knowledge base about custom AI solutions.

Why Understanding Licensing Is So Critical

Failure to understand terms of licence can:

• Put your business at huge legal risks.

• Limit the introduction of products or expansion.

• Cause reputational losses in Web3.

• Influence the long-term viable nature of your AI and information resources.

C-level executives must engage law and technology professionals early, consider the terms of all licences including terms of data privacy and possible limitations.

Lack of reviewing licensing might result in costly lawsuits, limited launching possibilities, and even loss of reputation. It is crucial that the decision-makers engage the legal and technological teams early and comprehend each phrase. To further consult, as an expert AI development company, we have created a full guide about compliance and you can check our article on compliance with AI development.

Scalability and Performance

• Proprietary AI can be conveniently scaled via vendor clouds, where expanding loads simply cost more without much effort.

• Open-source AI can be more scalable in the long-term but needs some contribution in the infrastructure and ability to hire cloud engineering talent.

• Proprietary solutions are very optimised but when they are designed specifically to your domain, well tuned open-source models will perform just as well and better.

Proprietary AI can be deployed rapidly on vendor platforms but the expenses may escalate rapidly. Open-source models require more commitment to your IT at the start but over time you may find it cheaper to scale extensively. To get a technical breakdown, read how our AI agent solutions can scale with Web3 innovation.

Building a Data Architecture to Unlock the Value of Proprietary Data

The effective use of artificial intelligence is impossible without the value of organisational data. To do this you need to proceed as follows:

1. Why Data Management Matters

• The best artificial intelligence (AI) companies stand out in data management. They easily access, tabulate, and utilise data in favor of AI projects, thus creating a considerable edge over less successful rivals.

• Though, there are challenges to providing AI models access to corporate data. Issues with data-quality and data silos often create a bottleneck to information flow that is necessary to use AI effectively.

2. The Big Solution: Integrated Data Fabric

• A data fabric is a framework that unifies all the data sources regardless of where they are located, hence making it instantly accessible to artificial intelligence and analytics.

  • Such a system breaks down silos to enable different departments and applications to work on one real-time data set..

  • Data collection, transformation, and migration capabilities are also automated and make operational processes even more efficient and reduce the risk of errors.

3. How to Put This in Practice

Data Integration

• The initial problem is the gathering of data that is spread in various places.

• Traditional databases are often used to create silos that hinder the combination of relevant data.

• The resolution starts by:

  • creating data pipelines in order to acquire, clean, and store the information in a centralised place.

  • Use tools for:

    • Stream-processing solutions, like Apache Kafka process real-world data, and data movement between platforms and preparation of data

    • Extract-Transform-Load (ETL) instruments, such as IBM DataStage to move data across platforms and prep it for AI.

• Select what works best in your storage:

  • Data lakes are low cost and unstructured data storage.

  • The data warehouses contain data that have been cleansed, and put in order to be used in the analytics.

  • Data lakehouses allow both flexibility because they support raw data and more structured data and are effective in machine learning and analytics.

• Hybrid clouds are the new norm. Your data can be in a place that is partly in the company servers and partly in the cloud. You should ensure that your architecture assembles all the data you require to use towards AI.

Cleaning and Preparing Data

• To produce excellent results, AI requires clean data that is reliable.

• Edit data before accessing it to eliminate mistakes, resolve discrepancies, and delete irrelevant or deceptive chunks.

• Find out what is valuable in your data, the gold, and filter the noise that can mislead your AI models.

Example:
A support ticket can show a number of unsuccessful attempts until a real solution is found. This means that you should only feed the right solution to your AI because this is the only way you can prevent confusion.

• Cleaning and curation usually needs both expert manual review and special tools:

  • The ability of the data management tools to automatically detect and correct errors is made possible by AI.

  • Synthetic data generators are used to impute missing values, or augment small datasets.

  • Tools of data engineering (such as Apache Spark or pandas) are used to prepare and work with data in order to train it with machine learning.

  • The data observability tools monitor the data flow and modifications; thus, the problems can be detected quickly.

In Summary

The development of a new data architecture of AI is concerned with:

• Eliminating data silos.

• Cleaning, and centralisation of your data.

• Engagement of intelligent integration and monitoring devices.

In so doing, your proprietary data becomes an invaluable tool to high-end AI, analytics, and business innovation.

The next important step to achieving maximum AI value is the establishment of a strong data architecture. This involves the creation of secure centralised data storage, the emphasis on quality data engineering, and the privacy by design. Check our blog about custom AI architectures to create AI products specific to your business.

Final Takeaways 

Choosing proprietary artificial intelligence (AI) or open-source AI depends on specific goals and conditions of an organisation. Proprietary AI is specifically beneficial to businesses that require accelerated deployment, comprehensive vendor support, comprehensive compliance architecture, and high model performance with little noticeable reliance on internally built technological capacity. On the other hand, open-source AI is specifically suitable to organisations that value independent management of their technology environment, seek high levels of customization and transparency, and expect long-term cost savings, assuming that they are ready to deal with an increased complexity of onboarding and a larger need of internal technical skills.

As the new world of Web3 is emerging, many businesses are realizing the benefits of proprietary artificial intelligence and the benefits of an open-source ecosystem. Proprietary AI is used by these organisations in routine or automated tasks, open-source AI is used in strategic, highly customisable or sensitive projects. In this decision leaders should first come up with accurate business goals, assess readiness of the organisation in implementing, and review the demands in data privacy. At the same time, they ought to examine every licensing requirement and create a solid data structure. Since the technological changes and Web3 environment are dynamic, this decision should be reevaluated periodically. Well executed, both methods can lead to real gains to a company and its users in the fast-evolving space of Web3 AI.

Ready to Implement AI in Your Web3 Business?

Take your AI game to the next level and seek the expertise of TokenMinds to steer you in the right direction and offer tailored solutions.

• As an AI development company, our professionals use the potential of open-source or proprietary AI technologies to their full potential in your Web3 organisation.

• Become a beneficiary of customised AI development, seamless integration, end-to-end support, which is ensured by the best industry knowledge.

• Secure your AI roadmap by ensuring effective licensing, high data security, and a next-generation architecture.

Upgrade your Web3 company to cutting-edge AI. Book your free consultation with TokenMinds and start building your AI advantage now.

Frequently Asked Questions: AI Strategy in Web3 Firms

1. What are the main differences between proprietary and open-source AI for Web3 firms?

The inherent difference between proprietary AI and open-source AI to Web3 businesses relates to control and access. Proprietary AI is owned by the corporations and has severe restrictions on how the user can use and customise the systems; open-source AI, on the other hand, gives complete freedom to access, modify, and integrate the model as he or she wishes. Even though open-source AI is more adaptive, its suitable management is possible only with an experienced technical team in place.

2. Which are the primary benefits and drawbacks of utilising open-source AI in Web3?

Open-source artificial intelligence has opportunities and limitations. Its main strengths are the increased ability to customise and heightened transparency. Through the use of open-source solutions, organisations avoid paying licence fees and regularly gain access to fast community updates. However, the implementation of open-source AI requires a very high level of technical expertise; businesses will have to provide security, compliance, and maintenance. Moreover, adoption can be made complex by the lack of support or documentation.

3. Why might a Web3 firm still opt for proprietary AI solutions?

Most Web3 businesses still use proprietary AI systems since they are simpler to use and implement. Such systems are more likely to have support by the vendor, faster implementation, and strong security and compliance features. Proprietary AI is beneficial to businesses that do not have vast technical capabilities and are in need of fast implementation.

4. How do costs compare between open-source and proprietary AI models for enterprises?

In terms of cost, open-source AI requires, first of all, a higher initial contribution in infrastructure and employees, but their recurring expenses can be smaller. Proprietary AI, in turn, is usually associated with fewer upfront costs, but there are regular subscription or use fees which grow with time, and may increase as an organisation scales.

5. What are key considerations when choosing between open-source and proprietary AI for Web3 projects?

The key principal business objectives of the organisation, whether the organisation can provide the technical resources and expertise and the nature of the data being processed are those factors that must be prioritised when making the decision between an open-source and proprietary artificial-intelligence technologies. The adherence to the current rules and regulations should be established and the flexibility of the selected solution with regard to future scalability should be proven. Last but not least, due diligence should be conducted on technical requirements, licensing terms and possibilities of long-term maintenance prior to committing oneself.