Ai Engineer at a computer software company with 11-50 employees
Real User
Top 20
Jul 12, 2026
Lightning AI is currently in a good stage, but for improvements, integrated tools could be added to easily update ticket statuses directly from Lightning AI, persistent storage offerings could be enhanced, and drag-and-drop capabilities could be included for better management. Integrating Lightning AI with existing workflows and tools is somewhat tricky. I added three or four tools on my end, and it was challenging, but I managed to get it done. New users getting started with Lightning AI should first consult the manuals and follow easy setup steps with coding tools or LLMs, after which they can dive into hands-on tasks. For experienced developers, I find that half a day to one day is sufficient to become familiar with some of Lightning AI's features. For scaling workloads, we typically go with on-tap GPU scaling. However, I believe there are opportunities to automatically speed up or scale up GPUs, which I found challenging at times, but I managed my computations and calculations well to accommodate the necessary infrastructure, rarely facing a need for auto-scaling. Lightning AI is good enough, but they should aim to provide additional infrastructure-related support, especially for faster model training and optimizations that can be executed with a single click, such as quantization and model optimizations. It would be beneficial to integrate a backend AI agentic workflow that suggests changes for model optimization and creates smaller models while working on larger ones, so developers can avoid wasting time figuring things out and quickly select the best models recommended by Lightning AI.
There are definitely a few areas where Lightning AI can improve. Overall, we have had a positive impact, but there are definitely a few areas it could enhance. One area is cost visibility and resource management. There are multiple teams running experiments, GPUs, and long-running sessions. It is not always obvious how much compute is being consumed and what the projected costs might be. More granular visibility and alerts would help the team manage usage proactively. Another area is workspace and project organization. As the number of experiments grows, it can become difficult to keep projects, notebooks, data sets, and test environments organized. Better lifecycle management could help achieve this and discoverability would be useful for larger teams. We have also encountered situations where long-running sessions or development environments needed more resilience. While this is not unique to Lightning AI, interruptions during model training and experimentation can be frustrating, especially when working with larger data sets. From an enterprise perspective, I think there is room to strengthen governance and operational control. Features around permissions, auditability, environment standardization, and usage policies become increasingly important as adoption expands across teams. I would particularly appreciate better support for moving successful experiments into production workflows. There could be better cost and resource visibility, stronger project and experiment organization, improved reliability for long-running sessions, stronger governance capabilities, and a smoother journey from experimentation to production. None of these are major blockers for us, but these are areas where the platform could become more valuable as the team and workload scale. A minor annoyance would be stronger project and experiment organization. When more data sets and more projects come into place, it becomes difficult to organize, and keeping them in a standardized way becomes slightly difficult. That is an area I wanted to highlight. There is not much of a pain point. There are a few minor suggestions I would mention, such as observability and experiment tracking at scale. When teams start running many experiments across different models, it becomes increasingly important to have a clear view of what changed and why performance improved or declined. That could be one area. Another area is cross-team discoverability. As AI adoption grows within an organization, valuable experiments and reusable components can be scattered. Better mechanisms for surfacing reusable workflows and templates would be beneficial. I would also appreciate continued investment in LLM and agent development workflows. The AI landscape is evolving rapidly. These suggestions come from the perspective of a team that is using the platform heavily. Most of the core capabilities work well today, which is why the feedback is more about helping the platform scale with a growing AI organization rather than fixing major shortcomings.
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...
Lightning AI is currently in a good stage, but for improvements, integrated tools could be added to easily update ticket statuses directly from Lightning AI, persistent storage offerings could be enhanced, and drag-and-drop capabilities could be included for better management. Integrating Lightning AI with existing workflows and tools is somewhat tricky. I added three or four tools on my end, and it was challenging, but I managed to get it done. New users getting started with Lightning AI should first consult the manuals and follow easy setup steps with coding tools or LLMs, after which they can dive into hands-on tasks. For experienced developers, I find that half a day to one day is sufficient to become familiar with some of Lightning AI's features. For scaling workloads, we typically go with on-tap GPU scaling. However, I believe there are opportunities to automatically speed up or scale up GPUs, which I found challenging at times, but I managed my computations and calculations well to accommodate the necessary infrastructure, rarely facing a need for auto-scaling. Lightning AI is good enough, but they should aim to provide additional infrastructure-related support, especially for faster model training and optimizations that can be executed with a single click, such as quantization and model optimizations. It would be beneficial to integrate a backend AI agentic workflow that suggests changes for model optimization and creates smaller models while working on larger ones, so developers can avoid wasting time figuring things out and quickly select the best models recommended by Lightning AI.
There are definitely a few areas where Lightning AI can improve. Overall, we have had a positive impact, but there are definitely a few areas it could enhance. One area is cost visibility and resource management. There are multiple teams running experiments, GPUs, and long-running sessions. It is not always obvious how much compute is being consumed and what the projected costs might be. More granular visibility and alerts would help the team manage usage proactively. Another area is workspace and project organization. As the number of experiments grows, it can become difficult to keep projects, notebooks, data sets, and test environments organized. Better lifecycle management could help achieve this and discoverability would be useful for larger teams. We have also encountered situations where long-running sessions or development environments needed more resilience. While this is not unique to Lightning AI, interruptions during model training and experimentation can be frustrating, especially when working with larger data sets. From an enterprise perspective, I think there is room to strengthen governance and operational control. Features around permissions, auditability, environment standardization, and usage policies become increasingly important as adoption expands across teams. I would particularly appreciate better support for moving successful experiments into production workflows. There could be better cost and resource visibility, stronger project and experiment organization, improved reliability for long-running sessions, stronger governance capabilities, and a smoother journey from experimentation to production. None of these are major blockers for us, but these are areas where the platform could become more valuable as the team and workload scale. A minor annoyance would be stronger project and experiment organization. When more data sets and more projects come into place, it becomes difficult to organize, and keeping them in a standardized way becomes slightly difficult. That is an area I wanted to highlight. There is not much of a pain point. There are a few minor suggestions I would mention, such as observability and experiment tracking at scale. When teams start running many experiments across different models, it becomes increasingly important to have a clear view of what changed and why performance improved or declined. That could be one area. Another area is cross-team discoverability. As AI adoption grows within an organization, valuable experiments and reusable components can be scattered. Better mechanisms for surfacing reusable workflows and templates would be beneficial. I would also appreciate continued investment in LLM and agent development workflows. The AI landscape is evolving rapidly. These suggestions come from the perspective of a team that is using the platform heavily. Most of the core capabilities work well today, which is why the feedback is more about helping the platform scale with a growing AI organization rather than fixing major shortcomings.