Use Case

Compute-heavy tasks.

Run compute-heavy workloads like rendering, simulations, and batch processing on powerful, on-demand GPUs.
How Aneta Handles Bursty GPU Workloads Without Overcommitting
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"Runpod has changed the way we ship because we no longer have to wonder if we have access to GPUs. We've saved probably 90% on our infrastructure bill, mainly because we can use bursty compute whenever we need it."
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https://media.getrunpod.io/latest/aneta-video-1.mp4
How Civitai Trains 800K Monthly LoRAs in Production on Runpod
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"Runpod helped us scale the part of our platform that drives creation. That’s what fuels the rest—image generation, sharing, remixing. It starts with training."
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How InstaHeadshots Scales AI-Generated Portraits with Runpod
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"Runpod has allowed us to focus entirely on growth and product development without us having to worry about the GPU infrastructure at all."
Bharat, Co-founder of InstaHeadshots
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https://media.getrunpod.io/latest/magic-studios-video.mp4
How KRNL AI scaled to 10K+ concurrent users while cutting infra costs 65%.
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"We could stop worrying about infrastructure and go back to building. That’s the real win.”
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How Coframe scaled to 100s of GPUs instantly to handle a viral Product Hunt launch.
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“The main value proposition for us was the flexibility Runpod offered. We were able to scale up effortlessly to meet the demand at launch.”
Josh Payne, Coframe CEO
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How Glam Labs Powers Viral AI Video Effects with Runpod
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"After migration, we were able to cut down our server costs from thousands of dollars per day to only hundreds."
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How Segmind Scaled GenAI Workloads 10x Without Scaling Costs
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Runpod’s scalable GPU infrastructure gave us the flexibility we needed to match customer traffic and model complexity—without overpaying for idle resources.
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High-performance compute, on demand.

Harness the power of Runpod’s GPUs for intensive, parallelized workloads.

GPU-powered speed

Leverage A100 and H100 GPUs for simulations and rendering.

Parallel processing

Run large-scale jobs even faster with high-efficiency multi-GPU execution.

Scale compute precisely when you need it.

Run massive compute workloads with dynamic scaling and zero idle costs.

Pay-per-use pricing

Avoid idle GPU costs and pay only for active inference time.

Spot GPU savings

Use low-cost spot instances to reduce expenses without sacrificing performance.

Deploy and run HPC
jobs effortlessly.

Launch pre-configured environments for simulations, rendering, and AI training.

Pre-config setups

Launch environments with CUDA, PyTorch, and other dependencies ready to go.

Batch or interactive

Run large-scale batch jobs or interactive sessions—no manual config needed.
Developer Tools

Built-in developer tools & integrations.

Powerful APIs, CLI, and integrations
that fit right into your workflow.

Full API access.

Automate everything with a simple, flexible API.

CLI & SDKs.

Deploy and manage directly from your terminal.

GitHub & CI/CD.

Push to main, trigger builds, and deploy in seconds.

Build what’s next.

The most cost-effective platform for building, training, and scaling machine learning models—ready when you are.