Sold by: Lightning AI
Deployed on AWS
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From the creators of PyTorch Lightning, the most advanced AI platform purpose-built for the Gen AI era. Use pre-built studios to jumpstart any use case. Code, prototype, train, and deploy AI apps, all in one place using the Lightning Studio.
4.5
Overview
Lightning AI is the most advanced end-to-end platform to build, train, and deploy any model, on your data, in your cloud or data center with the tools you love. Studio integrates your favorite ML tools into a single cohesive experience. It also eliminates the environment discrepancy between local code which runs on the cloud. This allows for seamless multi-node, scalable AI web apps, endpoints and more.
Environment discrepancies burns weeks of development. Context switching between tools kills productivity. Iterations take hours. Debugging is slow. Laptops don't have a 1,000 GPUs or petabytes of storage. Lightning Studio brings a true paradigm shift to AI development and boosts productivity by 60% or more.
- Highly intuitive, fully integrated AI platform for data-prep, model development, distributed training, and app deployment consistently governed and available for all your AI needs
- Pre-built studios (https://lightning.ai/studios ) from our experts and the PyTorch ecosystem to jumpstart AI innovation. Deep learning expertise at your fingertips to unblock any issue
- Enterprise-grade, secure, and scalable platform to power all your models in your AWS VPC - simple ML to massive LLMs
Access powerful on-demand compute (A100s/H100s), multi-node training, serverless deployments, and more.
Highlights
- Unified: - Start in minutes, not weeks - Scale from CPU to GPU to 100s of machines at the click of a button - One platform across your cloud and on-premises - Collaborate, debug, and deploy from one interface"
- Open: - Pre-built studios from the app gallery - Battle-tested for SOTA AI (LLMs, Diffusion, GNNs) - Open standards for easy integration with other tools - Extensible, buildable plugins to tailor to your needs
- Enterprise-grade: - Data never leaves your account with BYOC - SLAs backed by deep learning experts - Fine-grained access control - Private networking
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Deployed on AWS
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Pricing
Free trial
Try this product free according to the free trial terms set by the vendor.
Pricing is based on the duration and terms of your contract with the vendor. This entitles you to a specified quantity of use for the contract duration. If you choose not to renew or replace your contract before it ends, access to these entitlements will expire.
Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator to estimate your infrastructure costs.
Dimension | Description | Cost/month |
|---|---|---|
Teams Tier 1 Seat | 50 monthly credits per user, multi-node training, access to GPUs | $140.00 |
StartUp Package | 3 teams licenses and $8K usage credits; standard support | $1,250.00 |
Vendor refund policy
No Refunds
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Delivery details
Software as a Service (SaaS)
SaaS delivers cloud-based software applications directly to customers over the internet. You can access these applications through a subscription model. You will pay recurring monthly usage fees through your AWS bill, while AWS handles deployment and infrastructure management, ensuring scalability, reliability, and seamless integration with other AWS services.
Resources
Support
Vendor support
Please reach out to awsmp.support@lightning.ai with any questions or for options on contract or pricing terms. Technical Support: For help setting up your account, connecting to data, or exploring the platform, please reach out to awsmp-onboarding-help@lightning.ai
AWS infrastructure support
AWS Support is a one-on-one, fast-response support channel that is staffed 24x7x365 with experienced and technical support engineers. The service helps customers of all sizes and technical abilities to successfully utilize the products and features provided by Amazon Web Services.
Updated weekly
By Lightning AI
By Dataloop AI/GenAI development platform
By Anyscale
Sentiment is AI generated from actual customer reviews on AWS and G2
Functionality
Ease of use
Customer service
Cost effectiveness
Positive reviews
Mixed reviews
Negative reviews
AI generated from product descriptions
Multi-Node Distributed Training
Supports multi-node training capabilities enabling scalable AI model training across multiple machines with on-demand compute resources including A100 and H100 GPUs.
Integrated Development Environment
Provides unified platform integrating data preparation, model development, distributed training, and application deployment within a single cohesive interface.
Pre-built Model Templates
Includes pre-built studios from expert contributors and PyTorch ecosystem optimized for state-of-the-art AI applications including LLMs, Diffusion models, and Graph Neural Networks.
Enterprise Security and Isolation
Offers enterprise-grade security features including Bring Your Own Cloud (BYOC) capability, fine-grained access control, and private networking to ensure data remains within customer accounts.
Serverless Deployment
Supports serverless deployment options enabling application deployment without infrastructure management overhead.
Generative AI and RAG Pipeline Deployment
Deploy RAG pipelines for GenAI solutions including summarization, chatbots, and data preparation with support for RLHF and RLAIF incorporation.
Unstructured Data Management and Analysis
Explore and analyze unstructured data from diverse sources with automated preprocessing, embeddings generation, and similarity identification capabilities.
Model Versioning and Experimentation
Version, experiment, compare, and fine-tune AI models with production deployment capabilities without requiring external tool integration.
Workflow Orchestration with Drag-and-Drop Interface
Orchestrate data, models, applications, and human feedback using a drag-and-drop interface or Python SDK with pre-created pipeline templates.
Enterprise Security and Compliance
Implement GDPR, ISO 27001, ISO 27701, and SOC 2 Type II compliance with RBAC, SSO, 2FA, AES-256 encryption, and granular audit trail capabilities.
Distributed Computing Runtime
Unified runtime that distributes Python code and AI libraries across thousands of CPUs, GPUs, or both, scaling from single machine to large clusters
Multi-Framework Support
Support for distributed execution of XGBoost, PyTorch, vLLM, and other AI libraries within a single platform
Infrastructure Deployment Flexibility
Deployment options including fully managed Anyscale-hosted experience, bring-your-own-cloud (BYOC) into customer VPC, VM-based infrastructure (EC2), and Kubernetes environments (AWS EKS and SageMaker HyperPod)
Enterprise Security Integration
Native integration with AWS security frameworks including AWS Identity and Access Management (IAM) with inherited access controls, policies, and governance standards
Workload Optimization and Resilience
Built-in head node resilience, intelligent autoscaling, advanced scheduling, GPU sharing capabilities, and safe rollout mechanisms to maximize resource utilization and prevent cost overruns
Standard contract
No
No
Customer reviews
Shivam G.
LightingAI :A platform to build AI Products Lightning fast
Reviewed on Apr 20, 2025
Review provided by G2
What do you like best about the product?
Minimal or No setup required: LightingAI helps developers in building ai products without any setup on their hardware .
A whole lot of existing models : It has a lot of existing models to tune .A large variety of LLMs for ex :CodeLlama , Llama2 etc
Easy to create models : We can easily create model from it with no code or very low code .
A whole lot of existing models : It has a lot of existing models to tune .A large variety of LLMs for ex :CodeLlama , Llama2 etc
Easy to create models : We can easily create model from it with no code or very low code .
What do you dislike about the product?
limited storage in free tier: The free tier offers limited storage capacity for the users .
What problems is the product solving and how is that benefiting you?
It is making easy for us to develop AI models for our use .
Low code to train models
Low code to train models
Banking
Lightning AI review
Reviewed on Apr 19, 2025
Review provided by G2
What do you like best about the product?
Most Helpful Features:
Project Templates & Componentization
– Pre-built templates
No Hardware Barrier for AI Work
Distributed Training & Multi-GPU Support
Integrated Dev Environment
Workflow Automation
Community & Open Source Tools
Upslides of using Lightning AI:
Smart Formatting in Excel
PowerPoint Automation Tools
Excel to PowerPoint/Word Link
Library of Reusable Slides & Templates
Project Templates & Componentization
– Pre-built templates
No Hardware Barrier for AI Work
Distributed Training & Multi-GPU Support
Integrated Dev Environment
Workflow Automation
Community & Open Source Tools
Upslides of using Lightning AI:
Smart Formatting in Excel
PowerPoint Automation Tools
Excel to PowerPoint/Word Link
Library of Reusable Slides & Templates
What do you dislike about the product?
Lightning AI – Least Helpful Aspects
few pain points:
Credit System Can Be Limiting
Studio Configuration Bugs
Not Ideal for Beginners
Startup Vibes
Some Locked Features Behind Paywall
Downslides-
Pricing for Small Teams or Individuals more
Even a time-saver like UpSlide has its moments of frustration:
Can Feel Clunky at Times
– lag or sluggish behavior in Excel or PowerPoint when UpSlide is running.
Not Very Customizable
– You get a structured workflow, but power users who want to tweak templates or automation logic might feel boxed in.
Limited Outside Microsoft Office
few pain points:
Credit System Can Be Limiting
Studio Configuration Bugs
Not Ideal for Beginners
Startup Vibes
Some Locked Features Behind Paywall
Downslides-
Pricing for Small Teams or Individuals more
Even a time-saver like UpSlide has its moments of frustration:
Can Feel Clunky at Times
– lag or sluggish behavior in Excel or PowerPoint when UpSlide is running.
Not Very Customizable
– You get a structured workflow, but power users who want to tweak templates or automation logic might feel boxed in.
Limited Outside Microsoft Office
What problems is the product solving and how is that benefiting you?
Solving AI/ML Infrastructure Bottlenecks
Rakshit A.
Best platform to train and deploy the AI models
Reviewed on Apr 16, 2025
Review provided by G2
What do you like best about the product?
I used the Lightning AI for finetuning one of the LLM models on the client data for a chatbot use case, the setting up of the virtual environment and installation is very easy and they have maintained proper documentation for this. And also deploying the models on their cloud and setting auto-scaling requires no coding.
What do you dislike about the product?
One issue I felt was that their customer support or community was very slow, I was facing some version issue, and when I asked them I got a reply after 4 days. The platform crashes often when kept for long training hours.
What problems is the product solving and how is that benefiting you?
It really helps to save my time, Instead of buying a GPU machine and writing the fine-tuning script from scratch including intallization the Lightning AI has many templates for fine-tuning and also for RAG applications in which all the code and models weight are already seted up. I used the already present fine-tuning script and tranied the model and also tested it there itself this all happens in few hours which really saves time.
Ramanujam K.
ai development within the cloud
Reviewed on May 29, 2024
Review provided by G2
What do you like best about the product?
i used lightning ai to build ai apps within the cloud ,used vs code,and other lot of ml related tools and softwares with in the web.,like jupyter notebook,easy to setup,i had run big big llms seamlessly without any hassle .
What do you dislike about the product?
there are some times where the configured studios were not opening after setup,again i have to reconfigure them again .
What problems is the product solving and how is that benefiting you?
i used this particularly to overcome the burden of running large language models(llms) locally.it is very easy to setup a studio of 16gb vram of 4cpus and 1 Gpu.made my ml learning journey very effective and productive.its basically all your vs code,jupityer notebooks running all together within your private cloud.