Lightning AI
Browser-based AI development with Studios, on-demand GPUs, experiments, training, evaluation, and inference in one workspace.
About Lightning AI
Lightning AI gives teams a browser-based workspace and infrastructure for building specialized AI models. Projects can cover dataset preparation, interactive prototypes, parallel experiments, model comparison, training, evaluation, and inference without creating a separate local environment for each stage. Studios are the main workspace, with on-demand CPUs and GPUs available for heavier jobs. The free tier suits learning and light experiments: it includes one concurrently running CPU Studio and 15 credits per month. Pro raises compute and storage limits for individual developers. Teams provides more capacity for shared work, while Enterprise adds custom pricing, bulk credits, cloud-credit support, expert help, and a stated 99.95% service level. Costs can become harder to forecast because GPU jobs and extra concurrent Studios use credits or paid capacity. Free credits expire monthly, purchased credits expire after 12 months, and current cloud compatibility favors AWS and GCP rather than Azure.
Best for
Best use cases
Use a browser Studio to debug a model, then attach an on-demand GPU when the workload becomes compute-heavy.
Run multiple training experiments in parallel and compare their results before choosing a model for evaluation.
Prepare a dataset, post-train a specialized model, evaluate it, and move it into inference within one platform.
Compare before choosing
A broader workflow than a raw GPU rental because Studios, experiments, evaluation, and inference sit together.
More infrastructure-oriented than a notebook-only service, with cluster, storage, and production inference options.
Less suitable for Azure-centered organizations today because current cloud support is AWS and GCP.
Features
Prototype GPU-backed models
Open a browser-based Studio, attach on-demand GPU resources, and iterate through coding, debugging, and model refinement without setting up a local GPU machine.
Train and compare experiments
Run training experiments in parallel, track their results, and compare candidate models before selecting one for evaluation or deployment.
Operate specialized-model workflows
Move through dataset preparation, post-training, evaluation, and inference on one platform, with larger clusters and support available on higher tiers.
Pros
- The $0 tier includes one concurrent CPU Studio and 15 monthly credits, lowering the cost of learning and small experiments.
- One workspace covers prototyping, training, evaluation, and inference, reducing environment setup and tool handoffs.
- AWS and GCP support plus higher CPU, GPU, and storage limits give growing teams a clear expansion path.
Cons
- GPU work and extra concurrent Studios consume credits or paid capacity, so sustained workloads can be difficult to budget.
- Free credits expire every month and purchased credits expire after 12 months, which penalizes irregular usage.
- Azure is not currently supported, and using an organization’s AWS or GCP credits requires Enterprise.
Pricing Plans
Free
- 1 concurrently running CPU Studio
- 15 Lightning credits each month
- Up to 32 CPUs and 1 GPU per machine
- 10 GB persistent disk and 50 GB storage
Pro
- Unlimited Studios
- 40 monthly or 240 annual Lightning credits
- Up to 64 CPUs and 4 GPUs per machine
- 10 GB persistent disk and 200 GB storage
Teams
- Unlimited Studios for small teams and research labs
- 50 monthly or 600 annual Lightning credits
- Up to 192 CPUs and 8 GPUs per machine
- 10 GB persistent disk and 2 TB storage
Enterprise
- Bulk pricing and custom credits
- Up to 192 CPUs and 8 GPUs per machine
- Unlimited storage options
- 24/7 expert support and 99.95% service level
* Pricing information is for reference only. Please refer to the latest announcement on Lightning AI's official website.