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?