Issue 02 2023MAGAZINETechnology
GBO_ alternatives to OpenAI

Exploring the alternatives to OpenAI

Once ChatGPT astounded many with its capacity to respond to complicated inquiries in understandable English, OpenAI became a household name

There are now more options for software companies wishing to tap into the anticipated $90 billion artificial intelligence (AI) market besides Microsoft-backed OpenAI.

Over a dozen start-ups and investors are embracing the services of competitors of industry leader OpenAI, casting doubt on expectations that the tie-up between Microsoft and Sam Altman-led firm will dominate the young field.

The motivations of these businesses include wanting models tailored to specific tasks, a wariness of relying on a single company, and the chance to save money.

It is possible to predict how the next phase of generative AI, described as technology that can produce text, images, or other media in response to cues, would emerge from the change made by some software developers towards alternate foundation models.

AI investor George Mathew linked the foundation models to past technological advances that created a rivalry. Foundation models are AI systems with the capacity to learn to carry out a range of tasks that have been trained on vast datasets.

“Were there only a few internet service providers?” George Mathew remarked, while adding, “Similarly, a healthily operating ecosystem will require numerous core model providers. The current advantage that OpenAI has will not make it the only choice.”

Tome, an AI storytelling firm that makes it easier for users to create presentations quickly, was initially constructed using the GPT-3 foundation architecture, which OpenAI first released in 2020. According to Tome, it reached 3 million users in 2023 and began experimenting with alternative models.

It has a text model from OpenAI rival Anthropic added to the mix. In addition, it plans to switch from OpenAI’s photo generation model DALL-E to Stability AI’s Stable Diffusion.

According to Keith Peiris, Tome CEO, the objective is to identify the model that performs each operation with the least time and highest quality.

AI developers and investors claimed that there is a new industry consensus to lessen reliance on a single model to deliver more dependable services, control costs, and capitalize on the specialization of several models.

Once ChatGPT astounded many with its capacity to respond to complicated inquiries in understandable English, OpenAI became a household name. Microsoft has invested $10 billion in it while major competitors like Google race to develop similar models.

OpenAI’s recently released GPT-4 model remains the most potent by many metrics.

OpenAI’s Resources

According to PitchBook, the global market for generative AI is anticipated to reach $98.1 billion by 2026.

Foundation models, which form the foundation of AI applications, have received the most investments. OpenAI has stated its hopes of attaining $1 billion in income by 2024, depending on how these base models are employed by applications.

In 2023, OpenAI expects to earn $200 million in income. As an illustration of how it generates revenue, it costs 6 cents to process 1,000 tokens of prompts in its GPT-4 model and offers a $20/month subscription tier for ChatGPT.

Start-ups are also concerned that Microsoft’s use of OpenAI models across its Office Suites, search, and other products could put it in direct competition with its AI clients.

According to Mike Volpi, partner at Index Ventures, which supports OpenAI rival Cohere, “Some of these applications may leverage sensitive company data and the foundation models will witness these firms’ interactions with their own consumers. Many of these businesses will feel awkward depending on Microsoft or a business that Microsoft controls in general.”

While starting with OpenAI’s models, writing assistant does not want to rely on just one model, Founder Dave Rogenmoser told Reuters. It has now included Cohere and Anthropic, two other significant language model businesses with Google cloud computing relationships. In addition, it is introducing an AI engine to assist marketers in customizing voices by utilizing a variety of models.

According to CEO Matt Shumer, the AI copywriting tool HyperWrite combines each user’s activities with several models based on various factors. For instance, it uses the OpenAI model to produce lengthy articles and Cohere more quickly and cheaply auto-complete sentences. Because OpenAI is having difficulty keeping up with the growing demand, some have turned to alternatives.

However, according to CEO Srinath Sridhar, “A writing assistant that assists sales teams, employing numerous models lets us answer queries more cheaply and improves the experience for our users.”

Several start-ups, like customer service software provider Intercom Inc., support OpenAI wholeheartedly. Intercom’s director of machine learning Fergal Reid acknowledged that OpenAI’s GPT-4 is “quite expensive”.

“We currently believe we need to employ GPT-4 to attain the accuracy level that we need for customer service,” he added.

Here are the alternatives to OpenAI.


Since its establishment in 2010, this UK-based AI research institute has made considerable strides in the field. DeepMind’s mission is to use AI to tackle the world’s most pressing problems. To use artificial intelligence to address some of the most complex issues facing the globe, like healthcare and climate change. DeepMind’s research focuses primarily on machine learning and other related techniques, including reinforcement and unsupervised learning.

Artificial intelligence (AI) systems are created by DeepMind Technologies for various purposes, including robotics, image recognition, and gameplay. Additionally, it is employed in developing machine learning algorithms for data analysis, pattern identification, and judgement. Defence, banking, and the healthcare sectors are just a few of the sectors that employ DeepMind Technology. The healthcare industry uses DeepMind Technologies for clinical decision assistance, medical picture analysis, and healthcare system optimization. It is employed in finance for investment research, financial forecasting, and fraud detection. It also helps in military operations.

IBM Watson

It is an AI platform that facilitates the creation of cognitive applications. It is built on data-driven algorithms and understands complex unstructured data using natural language processing and machine learning. Its main objective is to give users a simple, automated approach to extracting meaning from their data. Healthcare, banking, and retail are just a few industries that can benefit from IBM Watson. It also offers services such as speech recognition, picture and natural language processing to help organizations make smarter decisions and enhance the consumer experience.

Among the sectors where IBM Watson can be applied are healthcare, banking, retail, and education. Watson can be used in medicine to identify illnesses, choose the best course of action, and even spot cancer tumours in the earliest stages. It can look at financial data, spot trends, and catch fraud in the financial sector. It can be used in retail for product suggestions, consumer behaviour analysis, and tailored customer experiences. It can be used to identify students in danger of failing classes, create personalized lessons, and give instructors personalized feedback.

Microsoft Azure

Microsoft developed the Microsoft Azure cloud computing platform, which offers several cloud services like processing, storage, networking, analytics, and the development of mobile and web apps. The Microsoft Azure cloud computing platform is made to facilitate the swift installation and management of cloud apps and services by organizations. Programmers and organizations can create, administer, and deploy cloud-based apps and services using Microsoft Azure’s user-friendly platform. Additionally, it offers a range of services, including big data analytics, the Internet of Things (IoT), artificial intelligence (AI), and machine learning, which enables companies to optimize their operations and gain knowledge from their data.

Google Cloud AI

To create AI applications, Google Cloud AI is a bundle of AI services and tools. It offers pre-trained models and services for creating intelligent apps that anticipate outcomes, react to user input, and understand speech. Google Cloud AI aims to make it easier for programmers to develop AI applications that can solve complex problems and improve user experiences.

Google Cloud AI aims to create apps that can understand natural language, recognize images, process audio and video, and distinguish objects in photos. It can be used to construct applications that advertise products, offer advice, and automate customer support tasks. In addition, developing insights and analytics to improve corporate operations and reach wise conclusions may be possible.

Amazon Machine Learning

A cloud-based AI tool and product suite, Amazon Machine Learning, enables programmers to build predictive applications. It equips you with the methods and tools to develop apps that examine data, identify patterns, and generate forecasts. With the aid of Amazon Machine Learning, programmers may construct software that predicts user behaviour, suggests goods or services, searches for fraud or other anomalies, and spots trends. This method can create software that automates customer service tasks, finds anomalies in medical data, or extracts insights from massive databases.

Nvidia DGX

A high-performance computer system called NVIDIA DGX was created to handle the demands of data-intensive tasks like deep learning. It is designed to accelerate the creation of deep learning models by accelerating AI and machine learning workflows. It consists of a collection of robust GPU servers powered by NVIDIA, fully integrated and deep learning-focused software. As a result, NVIDIA DGX offers the best deep learning and AI development platforms, allowing users to create, deploy, and manage their applications quickly and efficiently.

The primary purpose of NVIDIA DGX is to accelerate AI and machine learning operations. It enables high-performance computer operations, application deployment and management, and rapid, deep-learning model construction. By leveraging NVIDIA DGX to create virtualized computing environments, data scientists may experiment with their deep-learning models more quickly and productively.

Intel AI

Intel AI is a collection of hardware and software solutions for artificial intelligence (AI) developed to provide performance and flexibility for AI workloads. With enhanced analytics and real-time insights, deep learning and other AI models can be run on Intel AI systems. Robotics, autonomous driving, and healthcare are just a few of the many applications for which Intel AI technology can be employed. Intel AI solutions are fully integrated with Intel architecture and designed for Intel-based platforms. To execute AI models, analytics, and real-time insights, Intel AI primarily aims to provide the computing power and flexibility required.

Many fields, such as robotics, autonomous driving, and healthcare, can benefit from using Intel AI. Intel AI solutions can provide deep learning model training, analytics processing, and real-time insight generation. In addition, AI applications can be created and deployed on Intel-based platforms using Intel AI technologies.

These are a few of the top OpenAI substitutes you should consider for your AI initiatives. When deciding on one of these AI research centres, weigh your options because each has benefits and drawbacks.

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