Ai Engineer at a computer software company with 11-50 employees
Real User
Top 20
Jul 12, 2026
My main use case for Lightning AI was personally training a large language model named Bharat LLM, which is a Hindi, English, and Hinglish model with seven billion parameters, trained on roughly eight A100 GPUs with an FSDP PyTorch setup. I single-handedly handled and created that entire pipeline, achieving good results and ease of working on AI projects with the help of the tool. Lightning AI specifically helped me during the training and deployment of Bharat LLM by providing a suite that is quite good, including AI Studio, which helps handle everything very easily, alongside a co-coding environment with peer-to-peer features that benefited me greatly. Lightning AI also offered GPU access as needed and enabled easy code switching between CPU and GPU, which was a huge win for me. I didn't have to juggle much between platforms, and the work could be seamlessly pulled over a single interface with rich functionality.
We primarily use Lightning AI as an experimentation and rapid prototyping environment for AI products. At Klydo, our engineering team works on a mix of recommendation systems, product matching, catalog intelligence, customer support automation, and several internal AI agents. Lightning AI became useful because it allowed us to spin up development environments quickly without spending too much time managing infrastructure. I would like to give an example where we used Lightning AI for the product matching system of our fashion catalog. We needed to identify similar products across different brands and suppliers, even when the product title, description, and attributes were not very consistent. Before Lightning AI, this was very difficult and experimentation took a fair amount of engineering efforts. The biggest benefit was iteration speed. We could run multiple experiments and compare model outputs, share notebooks with team members, and validate the results with our business stakeholders in a much shorter cycle, which might have taken several days. This reduced the time to several hours. As a result, we were working on prototypes significantly faster and we were able to focus more on improving model quality rather than spending time on infrastructure and environmental management. Another use case involves AI agents and internal productivity tools. Apart from experimentation, we have used Lightning AI for a sandbox environment for building and testing agent-based workflows that interact with multiple data sources. For example, we experimented with an assistant that could query operational data, analyze business metrics, and help our team retrieve insights without having to write SQL queries or navigate dashboards manually. Lightning AI was valuable here because data scientists, product engineers, and business stakeholders could easily collaborate in the same environment. We could quickly prototype an idea and test it with real business scenarios rather than wait for feedback and iterate with delays. We also use Lightning AI for AI tools and one use case is knowledge transfer. If someone was out of office or moved to another project, the work was already documented and available within the platform. Onboarding another engineer was much simpler. From a startup perspective, collaboration was not just multiple people editing the same project. It was reducing the back and forth which is typically involved in AI development instead of sharing screenshots or exporting notebooks or setting up environments repeatedly. The entire team could work on common content and iterate much faster. That is probably why I view Lightning AI less as a notebook platform and more as a shared experimentation workspace for AI teams. The standout features would be fast setup, onboarding, easy access to resources, strong collaboration capabilities, flexible support of AI and LLM workflows, and a faster path from idea to working prototype.
Lightning AI is an advanced platform that supports AI development through flexible solutions catering to diverse industry demands. It streamlines complex processes to enhance machine learning capabilities for businesses.Offering versatile functionality, Lightning AI integrates sophisticated AI architecture designed for efficiency and speed. It is crafted to accelerate AI workflows and is particularly advantageous for enterprises adopting data-driven strategies. The platform supports various...
My main use case for Lightning AI was personally training a large language model named Bharat LLM, which is a Hindi, English, and Hinglish model with seven billion parameters, trained on roughly eight A100 GPUs with an FSDP PyTorch setup. I single-handedly handled and created that entire pipeline, achieving good results and ease of working on AI projects with the help of the tool. Lightning AI specifically helped me during the training and deployment of Bharat LLM by providing a suite that is quite good, including AI Studio, which helps handle everything very easily, alongside a co-coding environment with peer-to-peer features that benefited me greatly. Lightning AI also offered GPU access as needed and enabled easy code switching between CPU and GPU, which was a huge win for me. I didn't have to juggle much between platforms, and the work could be seamlessly pulled over a single interface with rich functionality.
We primarily use Lightning AI as an experimentation and rapid prototyping environment for AI products. At Klydo, our engineering team works on a mix of recommendation systems, product matching, catalog intelligence, customer support automation, and several internal AI agents. Lightning AI became useful because it allowed us to spin up development environments quickly without spending too much time managing infrastructure. I would like to give an example where we used Lightning AI for the product matching system of our fashion catalog. We needed to identify similar products across different brands and suppliers, even when the product title, description, and attributes were not very consistent. Before Lightning AI, this was very difficult and experimentation took a fair amount of engineering efforts. The biggest benefit was iteration speed. We could run multiple experiments and compare model outputs, share notebooks with team members, and validate the results with our business stakeholders in a much shorter cycle, which might have taken several days. This reduced the time to several hours. As a result, we were working on prototypes significantly faster and we were able to focus more on improving model quality rather than spending time on infrastructure and environmental management. Another use case involves AI agents and internal productivity tools. Apart from experimentation, we have used Lightning AI for a sandbox environment for building and testing agent-based workflows that interact with multiple data sources. For example, we experimented with an assistant that could query operational data, analyze business metrics, and help our team retrieve insights without having to write SQL queries or navigate dashboards manually. Lightning AI was valuable here because data scientists, product engineers, and business stakeholders could easily collaborate in the same environment. We could quickly prototype an idea and test it with real business scenarios rather than wait for feedback and iterate with delays. We also use Lightning AI for AI tools and one use case is knowledge transfer. If someone was out of office or moved to another project, the work was already documented and available within the platform. Onboarding another engineer was much simpler. From a startup perspective, collaboration was not just multiple people editing the same project. It was reducing the back and forth which is typically involved in AI development instead of sharing screenshots or exporting notebooks or setting up environments repeatedly. The entire team could work on common content and iterate much faster. That is probably why I view Lightning AI less as a notebook platform and more as a shared experimentation workspace for AI teams. The standout features would be fast setup, onboarding, easy access to resources, strong collaboration capabilities, flexible support of AI and LLM workflows, and a faster path from idea to working prototype.