Runpod

Train, fine-tune, and deploy AI models with scalable cloud GPUs and serverless endpoints. Enjoy sub-200ms cold starts and pay-as-you-go pricing across 31 regions.

Runpod screenshot

About Runpod

Runpod is an AI developer cloud platform engineered to take machine learning models from initial experimentation to high-scale production. It provides flexible compute infrastructure, allowing teams to launch dedicated GPU instances (Pods), run on-demand serverless inference, and spin up multi-node compute clusters without having to migrate or replatform between development phases. In practice, developers can provision GPU environments in under 30 seconds, choosing from over 30 GPU SKUs including NVIDIA B300s, H100s, and RTX 4090s distributed across 31 global regions. Workloads can be executed inside custom Docker containers using any framework. For production inference, developers push handlers to Runpod Serverless, which scales dynamically from zero to hundreds of concurrent workers with sub-200ms cold starts using FlashBoot technology, entirely eliminating idle infrastructure costs. Runpod is designed for AI researchers, ML engineers, software startups, and enterprise teams needing cost-effective, high-throughput compute. It supports a variety of workloads such as fine-tuning large language models, generating multi-modal media, orchestrating AI agents, and serving low-latency live APIs. Case studies highlight production use across applications like real-time image creation, large-scale LoRA training, and high-frequency conversational inference. What sets Runpod apart from traditional hyperscalers like AWS is its developer-first orientation and significant cost efficiency. With persistent network storage that incurs no egress fees, seamless auto-scaling without complex configuration files, managed task orchestration, and access to both high-end datacenter GPUs and affordable consumer-grade cards, Runpod allows teams to scale to thousands of requests per second at a fraction of legacy cloud costs.

Pros & cons

Pros

  • FlashBoot delivers sub-200ms cold starts on serverless workloads, eliminating idle infrastructure costs.
  • Persistent network storage incurs no egress fees, reducing transfer overhead for large datasets.
  • Broad hardware selection with over 30 GPU SKUs available across 31 global regions.
  • Serverless infrastructure autoscales from 0 to thousands of workers without custom config files.

Cons

  • Pricing for larger cluster tiers (such as H100, L40S, and B200 clusters) requires contacting sales.
  • Idle volume disk storage incurs a higher ongoing cost ($0.20/GB/mo) compared to running volume disks ($0.10/GB/mo).

Use cases

  • Machine learning engineers can train models and fine-tune LoRAs on dedicated multi-GPU pods or clusters using custom containers.
  • AI SaaS founders can deploy production inference endpoints using Runpod Serverless to automatically scale during traffic spikes while paying zero idle costs.
  • Software developers can consume pre-deployed open-source LLMs, image generation, or transcription models through pay-per-request Public Endpoints without configuring servers.
  • VFX and video production studios can render compute-heavy generative media and 3D architectural visualisations using high-memory GPU instances like the H200 and B300.

Features

  • ready-to-use public endpoints for language, image, audio, and video models
  • multi-gpu clusters scalable up to 64 gpus on-demand
  • managed task orchestration and automated queue distribution
  • persistent network storage without data egress fees
  • support for 30+ gpu skus from rtx 3090s up to nvidia b300s
  • flashboot technology providing sub-200ms cold starts
  • serverless autoscaling from 0 to thousands of workers with zero idle cost
  • rapid pod provisioning in under 30 seconds across 31 global regions

Pricing

Pods (Dedicated GPUs)

$0.27 / hour

  • Over 30 GPU SKUs available including RTX A5000, A100, H100, and B300
  • Deploy in under 30 seconds across 31 global regions
  • Billed per hour or per second
  • Custom container environments and frameworks
  • SSH access with SCP and FileZilla support
  • Available in Community Cloud and Secure Cloud options

Serverless Inference

$0.58 / hour

  • Pay only for execution time with zero idle costs
  • FlashBoot sub-200ms cold starts
  • Autoscaling from 0 to thousands of workers automatically
  • Managed orchestration and task queueing
  • Save 25% over other serverless cloud providers on flex workers
  • Real-time logs and metrics monitoring

Clusters

$1.79 / hour

  • Launch multi-GPU clusters in minutes with no commitments
  • Scale up to 64 GPUs per cluster
  • Attach high-performance shared network storage
  • Options for A100 SXM, H200 SXM, and B200
  • Pay only for what you use with no long-term contracts

Reserved Clusters (Enterprise)

Price varies

  • Guaranteed capacity scaling up to 10,000+ GPUs
  • Contract terms from 1 month to 12+ months
  • SLA-backed uptime and custom configurations
  • Company-wide access controls and compliance
  • Dedicated enterprise invoicing and hands-on support

FAQs

What is FlashBoot on Runpod Serverless?

FlashBoot is Runpod's optimization technology that enables sub-200ms cold starts for serverless workers. This eliminates the need for complex warm-up engineering while keeping idle costs at zero.

What GPU models are available on Runpod?

Runpod provides access to more than 30 GPU SKUs, ranging from consumer-grade cards like the RTX 3090, 4090, and 5090 to enterprise accelerators like the A100, H100, H200, B200, and B300.

Does Runpod charge egress fees for network storage?

Runpod offers persistent network storage starting at $0.05 per GB per month with no data egress fees, making it cost-effective for high-throughput AI pipelines.

Can I access pre-deployed models directly via API?

Yes, Runpod Public Endpoints allow you to call pre-deployed audio, image, language, and video models directly via API on a pay-per-request or per-token basis without setting up infrastructure.

Is Runpod certified for security compliance?

Yes, Runpod is ISO 27001 certified and provides enterprise-grade infrastructure controls, compliance capabilities, and Secure Cloud hosting options.

Open roles

All AI jobs

Technical Program Manager

Benefits:

  • Competitive base pay ($140,000 - $165,000)

  • Meaningful equity in a fast-growing company (stock options)

  • Generous medical, dental & vision plans

  • Flexible PTO

  • $1,200 Home Office & Equipment Stipend

Education Requirements:

  • Technical degree in Computer Science, Engineering, or a related field (Preferred)

Experience Requirements:

  • 4+ years of experience or equivalent expertise in technical program management, leading complex technology programs from planning through delivery

  • Previous experience in infrastructure-focused organizations, with exposure to capacity planning, data center operations, or vendor/partner management

  • Proven track record of partnering with 3+ cross-functional teams to deliver high-impact programs from planning through launch

  • Proven track record of working with external infrastructure or hardware vendors globally

  • Strong technical background in engineering and scalable SaaS/IaaS products or services

Other Requirements:

  • Strong quantitative and analytical skills

  • Deep understanding of the software development lifecycle (SDLC)

  • Highly organized and detail-oriented

  • Exceptional verbal and written communication skills

  • Authorized to work in the United States without visa sponsorship

Responsibilities:

  • Partner with Data Center Management and Host/Supply teams to run the capacity and utilization review cadence that feeds Supply's input into the product roadmap

  • Own the relationship and escalation path with host and infrastructure partners

  • Translate capacity and utilization data into concrete tradeoffs: where to add supply, where to consolidate, and how each option nets out on cost and reliability

  • Build and drive project plans across Engineering, Product, and Supply stakeholders

  • Act as the primary point of contact across teams, spotting risks and keeping stakeholders aligned

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Senior Data Engineer

engineeringremote US175,000 usd - 220,000 usd full-time

Benefits:

  • Competitive base pay (175,000- 220,000 usd)

  • Meaningful equity in a fast-growing company (stock options)

  • Flexible PTO

  • Remote work first environment

  • $1,200 Home Office & Equipment Stipend

Experience Requirements:

  • 5+ years of professional software engineering experience, with at least 3 years focused on data engineering in modern cloud environments

  • Strong Python and advanced SQL, applied with an engineer's discipline

  • Hands-on depth in a modern MPP/OLAP warehouse or query engine, including the internals (Snowflake preferred)

  • Experience building and operating orchestrated pipelines (Dagster, Airflow, or similar) and analytics engineering with dbt or equivalent

  • Track record of owning systems in production: monitoring, incident response, and follow-through

Other Requirements:

  • Demonstrated ability to use AI tools to improve the speed and quality of work

  • Excellent written communication

  • Authorized to work in the United States without visa sponsorship

Responsibilities:

  • Own the design, implementation, and operation of data pipelines end to end across Snowflake, dbt, and orchestration tools

  • Build well-documented, high-quality data products that analytics, finance, and engineering teams rely on

  • Treat data quality and observability as part of every deliverable

  • Diagnose and optimize warehouse cost and performance across query profiles, clustering, and warehouse sizing

  • Manage infrastructure as code (Terraform for AWS and Snowflake)

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