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January 25, 2024

Closed API vs. Open-Source API: Gen AI Cost Strategies


Edwin Lisowski

CSO & Co-Founder

Reading time:

11 minutes

How much does it really cost to run a generative AI model? While one might consider open-source models to be more cost-effective since they’re free to use, the actual cost of running them can be high for specialized applications.

Closed, proprietary models, on the other hand, are costly to develop and deploy. However, they might cost less in the long run, especially considering the fact that they can be engineered to perform specific functions beneficial to an organization.

That said, organizations require different use cases and have unique budgets. They also have to consider how the models’ applicability aligns with their objectives. Therefore, there is no one-size-fits-all solution.

Below, we’ve prepared a detailed true cost analysis of open-source vs closed API models to help you make an informed decision.

What are open-source APIs?

Before we dive into what an open-source API is, it’s important to first define what an API is in the first place. An API is an abbreviation term for Application Programming Interface. Its UI interface enables multiple software programs to seamlessly communicate and interact with the user.

Open APIs basically allow developers to access and interact with software programs without accessing their source code. These APIs are freely available to everyone, thus its popular term, Public API. [1]

With an open API, developers can use the interface to create their own software programs to share API documentation and make calls. Despite the fact that the system is available to everyone, different parties can open up different channels of communication without having to know what exactly the other one is doing. This means that organizations can develop and maintain proprietary tools and still retain the ability to integrate with other applications for enhanced functionality.

Benefits and limitations of open-source APIs

Over the past few years, open APIs have gained immense popularity among web developers due to their ease of customization and the fact that they’re mostly easy to use. That said, these models also come with a few limitations. Here’s a breakdown of their pros and cons.


Here’s what you get with an open API:

Enhanced flexibility and customization

Open models offer unmatched flexibility. By gaining access to the API’s source code, developers can effectively create and customize tools that cater to their specific needs. This unmatched level of customization enables developers to create unique and innovative solutions, culminating in more personalized user experiences.

Community support and collaboration

The fact that open models allow users to view the source code allows the formation of a vibrant, active developer community. These communities offer a myriad of beneficial resources, including documentation, tutorials, and forums, making it easier for developers to seek help and find solutions for challenges they encounter. [2] This creates a supportive environment for developers to learn and grow and fosters innovation and knowledge sharing.

Cost-effective solution

Open-source APIs are typically available for free, making them an attractive solution for developers and organizations working on a low budget. By leveraging free API tools, developers can effectively reduce their budget and reduce the need for costly proprietary software licenses. Developers also get a chance to reinvest some of that saved money on other aspects of the project, such as marketing and design, further enhancing the project’s overall outcome.

It might be also interesting for you: Open-Source Large Language Models (LLM) in 2023: A Comprehensive guide


Here are some of the most common limitations of such APIs:

Lack of technical support

Open-source APIs typically lack dedicated customer support from the developers. Despite the numerous benefits provided by community forums, having dedicated support could help solve more complex issues with the tools. This means that developers often have to rely on their expertise or spend countless hours troubleshooting common problems that could have been better resolved with dedicated support.

Compatibility and integration challenges

Most open-source APIs are not specifically designed to address specific challenges. They’re also developed by different communities and individuals, resulting in diverse frameworks, standards, and libraries.

This diversity in their infrastructure can lead to integration and compatibility challenges when using multiple open APIs on a single project. Therefore, organizations and developers must carefully assess the compatibility of various tools to ensure seamless compatibility, which can take a tremendous amount of time depending on the project’s complexity.

Potential security risks

The fact that open-source APIs are publicly available also means that their source code is open to scrutiny. Despite the added transparency benefits that lead to faster identification and resolution of potential issues, the open model leaves projects more vulnerable to security risks. [3]

This means that any developer or organization leveraging open APIs must remain vigilant in staying updated with the latest security patches and constantly review the code to mitigate potential risks.

What are closed-source APIs?

Closed-source APIs are typically private and proprietary. They are distributed via a licensing agreement, which gives users private copying, modification, and republishing restrictions.

This means that the source code is not publicly available, which is what you would expect from most organizations that are protective of their product and would like to maintain full control over their brand and the user experience they offer their customers.

Benefits and limitations of closed APIs

Like with open API tools, Closed APIs come with their unique set of benefits and limitations. Here’s a quick breakdown of what to expect with Closed APIs.


Some of the most notable benefits of closed APIs include:

Unmatched security and accountability

The mere fact that closed APIs’ source code is not publicly accessible means that potential attackers have difficulties in exploiting vulnerabilities in the system. The companies that develop closed APIs also invest a great deal in infrastructure and skilled experts to maintain the highest level of security.


The proprietors of closed systems have complete control over what they are used for and what they can do. While this may not pair well with users who prefer to have more control over their software tools, it works great to ensure that the tools are not used for illegal purposes.


Most closed tools are designed to perform specific tasks. That said, they provide added customization options for anyone working within the scope of their capabilities. They’re also much easier to integrate with other systems since they have properly defined architectures and libraries.


Here are some of the most notable limitations of using closed API tools:

Limited accessibility

Closed APIs are generally proprietary. Users can only access them through restrictive licensing agreements, which often come at a cost.

Stifled innovation

Closed-source API developers are the only ones with access to the software’s source code. This means that they’re the only ones who can make changes to the system. This can be quite restrictive in an environment filled with skilled developers who would have otherwise come up with beneficial solutions if they had access to the model’s source code.


Closed APIs can be quite costly to procure and implement. Besides the cost of getting a licensing agreement, developers must also have the proper infrastructure in place to deploy and run the tools effectively.

If you remain uncertain about the suitable API model for your company, be sure to get in touch with a Generative AI development company.

Closed API vs. open-source API: Cost analysis

Open-source APIs are generally considered more cost-effective. However, that’s not always the case. According to a recent article by The Information, companies are investing 50% to 100% more in running Meta’s Llama 2 (an open API) compared to OpenAI’s GPT-3.5 Turbo (which is a closed model). [4]

These cost comparisons are further proven by recent tests performed by a chatbot startup called Cypher when it ran a similar test using both Llama 2 and GPT-3.5 Turbo. According to the startup, they spent $1200 on the Llama 2 model and a mere $5 using GPT-3.5 Turbo.

What’s even more impressive is that OpenAI recently announced the release of the new GPT-4 Turbo, which is touted to be three times faster than the GPT-3.5 Turbo and costs significantly less to run. [5]

The reason behind the surprising cost difference in running open and closed models is quite straightforward. Closed-source API models typically have dedicated, specialized servers that bundle the requests they get from users and process them in parallel batches rather than one at a time. This means that closed models can fully leverage the full power of their relative infrastructures, thereby reducing power usage and running costs.

Conversely, open-source API companies typically rent specialized servers from cloud computing providers. In most cases, the models don’t get enough user requests to bundle, thus limiting their ability to leverage the full computational power of their infrastructure.

What to consider when developing a cost-effective generative AI strategy

A lot of thought goes into deciding the most effective, cost-effective strategy for running generative AI models. For most organizations, this typically involves looking for a model that’s perfectly suited to their intended applications and seeing whether it aligns with their budget. However, that’s not all there is to it. Here are some other crucial factors to consider when developing a generative AI strategy:

Your R&D budget

Your R&D budget plays a pivotal role in determining whether you’ll choose a closed-source model or an open-source model. An organization utilizing a closed-source model would have to train the model from scratch, which can be quite resource-intensive both in terms of monetary costs and data requirements.

However, considering the actual cost of running an open model, it helps to compare several factors before dismissing the seemingly costly closed-source model.

When running an open-source model, you need to take the total cost of recruiting a team with the necessary skills to run the system. You also have to consider additional costs associated with deployment and whether or not you’ll have to fine-tune your model. These additional costs, particularly when it comes to fine-tuning, will ultimately depend on the model’s size and whether you need to purchase additional datasets for fine-tuning.

Similarly, closed-source models come with their own set of additional costs. Besides the cost of procuring the license, you also have to include recruitment costs as well as the cost of deployment. On the upside, the cost of fine-tuning a closed-source model is significantly lower compared to open models.

Your team’s deep learning expertise

Open-source models typically require a lot of fine-tuning to make them more effective in specialized tasks. This means that you need a dedicated team experienced in handling natural language processing (NLP) and machine learning tasks to train the model effectively.

Your team should also be conversant with various frameworks such as TensorFlow, Keras, and PyTorch. They should also be skilled in data science tasks in order to handle and analyze large datasets effectively.

Considering the specialization requirements of adopting an open-source model, it’s clear to see that it takes up nearly the same resources it would when building a model from scratch.

Conversely, using a closed-source API provides added flexibility in terms of deployment and maintenance. While you may still require some level of expertise in NLP and machine learning to properly leverage the system, there’s little need for extensive fine-tuning, and the models are relatively easier to integrate into existing systems and infrastructures.

Need for customization

What do you intend to do with the model? Asking yourself this question can help you narrow down your options when it comes to deciding on a suitable model. For instance, open-source models typically require a lot of fine-tuning for specialized, domain-specific tasks.

Similarly, while closed models may require some level of customization and fine-tuning, they’re typically made to handle a wider variety of applications, making them easier to customize.

That said, you should also note that open-source models are by far the easiest to customize. They come with countless open libraries and databases for easy customization. You also get access to a large community of skilled developers who are constantly making improvements to the model.

Final thoughts

It’s nearly impossible to narrow down the specific cost of running an API. Depending on your intended purpose and specific requirements, both options could serve as a cost-effective option. Therefore, it helps to first consider what you want to do with the model, whether you have a dedicated, specialized team to manage it, and the resources at your disposal.

For ordinary developers who don’t require a lot of specialized features and capabilities, an open-source model could do just fine, provided they have the time and computational resources to fine-tune the model. Similarly, large organizations with dedicated data science teams could benefit greatly from closed-source models that offer enhanced security for their proprietary data.


[1] Open API. URL: Accessed on January 23, 2024
[2] The Power of Community in Open Source. URL:,Accessed on January 24, 2024
[3] What are API Security Risks. URL: Accessed on January 23, 2024
[4] Metas Free AI Isn’t Cheap to Use, Companies Say. URL: Accessed on January 23, 2024
[5] OpenAI DevDay: GPT-4 Turbo, Custom ChatGPT and API Updates. URL:,Accessed on January 23, 2024


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