Customer Development Interview. AI Cloud Compute Users
Customer Development Interview with AI cloud compute users
We are looking to speak with experienced AI practitioners who have hands-on experience using GPU cloud infrastructure for model training or inference.
This is a short research conversation about what has worked well and what has been painful in your past experience. The goal is to learn from practitioners and use those insights to shape a product in the future. It is not an evaluation of you, and is purely a learning conversation.Who is a good fit?
You are:
An AI Engineer, ML Engineer, Applied AI Researcher, or Technical Founder
Currently working at:
An AI startup (Seed to Series B preferred), OR
An AI-heavy product company (gaming, video, agents, multimodal, LLM apps)
Directly involved in infrastructure decisions for:
Model training (fine-tuning, SFT, LoRA, QLoRA, etc.)
Inference workloads (batch or real-time)
Long-running AI agents or multimodal pipelines
Infrastructure Experience Required
You have used at least one of the following beyond AWS/GCP/Azure:
RunPod
CoreWeave
Lambda Labs
Paperspace
Vast.ai
Modal
Together.ai
Any other GPU cloud provider
Bonus if youve:
Switched providers due to pricing or reliability
Experienced scaling issues across multiple GPUs
Compared bare metal vs managed GPU solutions
Faced GPU availability shortages
We are especially interested if:
You manage AI compute budgets
You care about price/performance optimization
Youve struggled with unpredictable costs
Youve deployed production inference workloads
Youve optimized GPU utilization
Not a Fit If:
You only used AWS Sagemaker once for a tutorial
You have no direct infrastructure decision-making involvement
You are not hands-on with model deployment
Research interview Details
30 minute structured interview
Remote (Google Meet)
Discussion topics:
GPU provider selection criteria
Pricing models and cost predictability
Performance bottlenecks
Workload types (training vs inference vs agents)
Switching costs and lock-in
To Apply
Please include:
What AI infrastructure providers have you personally used?
What type of workloads did you run?
Approximate monthly compute spend?
Your role in infrastructure decision-making?
Apply Now
We are looking to speak with experienced AI practitioners who have hands-on experience using GPU cloud infrastructure for model training or inference.
This is a short research conversation about what has worked well and what has been painful in your past experience. The goal is to learn from practitioners and use those insights to shape a product in the future. It is not an evaluation of you, and is purely a learning conversation.Who is a good fit?
You are:
An AI Engineer, ML Engineer, Applied AI Researcher, or Technical Founder
Currently working at:
An AI startup (Seed to Series B preferred), OR
An AI-heavy product company (gaming, video, agents, multimodal, LLM apps)
Directly involved in infrastructure decisions for:
Model training (fine-tuning, SFT, LoRA, QLoRA, etc.)
Inference workloads (batch or real-time)
Long-running AI agents or multimodal pipelines
Infrastructure Experience Required
You have used at least one of the following beyond AWS/GCP/Azure:
RunPod
CoreWeave
Lambda Labs
Paperspace
Vast.ai
Modal
Together.ai
Any other GPU cloud provider
Bonus if youve:
Switched providers due to pricing or reliability
Experienced scaling issues across multiple GPUs
Compared bare metal vs managed GPU solutions
Faced GPU availability shortages
We are especially interested if:
You manage AI compute budgets
You care about price/performance optimization
Youve struggled with unpredictable costs
Youve deployed production inference workloads
Youve optimized GPU utilization
Not a Fit If:
You only used AWS Sagemaker once for a tutorial
You have no direct infrastructure decision-making involvement
You are not hands-on with model deployment
Research interview Details
30 minute structured interview
Remote (Google Meet)
Discussion topics:
GPU provider selection criteria
Pricing models and cost predictability
Performance bottlenecks
Workload types (training vs inference vs agents)
Switching costs and lock-in
To Apply
Please include:
What AI infrastructure providers have you personally used?
What type of workloads did you run?
Approximate monthly compute spend?
Your role in infrastructure decision-making?
Apply Now