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Runpod Blog.

DeepSeek V4 in the wild, and how to run it on Runpod New Runpod datacenter now live: AP-IN-1 The GPU supply supercycle is here. Here’s what AI builders need to know. Community Spotlight: One-click AI image and video generation on Runpod with SwarmUI | Runpod Blog Community Spotlight: LoRA Pilot Data Prep to Inference Introducing the Runpod Assistant: Manage Your Cloud GPU Resources with Natural Language OpenAI's Parameter Golf: Train the Best Language Model That Fits in 16MB on Runpod LLM inference optimization: techniques that actually reduce latency and cost Pruna P-Video and Vidu Q3 public endpoints now available on Runpod Runpod brand spelling guide Quickstart - Runpod Documentation The AI market looks nothing like the narrative Training StyleGAN3 with Vision-Aided GAN on Runpod KoboldAI – The Other Roleplay Front End, And Why You May Want to Use It How to Connect Cursor to LLM Pods on Runpod for Seamless AI Dev Community Spotlight: How AnonAI Scaled Its Private Chatbot Platform with Runpod Prompt Scheduling with Disco Diffusion on Runpod Runpod's Latest Innovation: Dockerless CLI for Streamlined AI Development Run Your Own AI from Your iPhone Using Runpod Introducing Flash: Run GPU workloads on Runpod Serverless: No Docker required Use Claude Code with your own model on Runpod: No Anthropic account required Avoid Errors by Selecting the Proper Resources for Your Pod What hackers built on Runpod at TreeHacks 2026 Easily Back Up and Restore Your Pod with Cloud Sync + Backblaze B2 The Complete Guide to GPU Requirements for LLM Fine-Tuning AI Guides, Tutorials & GPU Infrastructure Insights | Runpod Your first Claude Code project within Runpod: a complete setup guide 10 billion Serverless requests and counting Building for resilience: Runpod’s response to the AWS us-east-1 outage How to Connect Google Colab to Runpod Founder Series #1: The Runpod Origin Story AMD MI300X vs. NVIDIA H100: Mixtral 8x7B Inference Benchmark How to Run the FLUX Image Generator with ComfyUI on Runpod Run Llama 3.1 405B with Ollama on Runpod: Step-by-Step Deployment How to Run FLUX Image Generator with Runpod (No Coding Needed) How to Use 65B+ Language Models on Runpod Deploy Llama 3.1 with vLLM on Runpod Serverless: Fast, Scalable Inference in Minutes Open Source Video & LLM Roundup: The Best of What’s New Run vLLM on Runpod Serverless: Deploy Open Source LLMs in Minutes Introduction to vLLM and PagedAttention New update to Github integration: release rollback! | Runpod Blog A note to the developers who built Runpod with us Deploy ComfyUI as a Serverless API Endpoint Setting up Slurm on Runpod Clusters: A Technical Guide Building an OCR System Using Runpod Serverless From No-Code to Pro: Optimizing Mistral-7B on Runpod for Power Users Lessons While Using Generative Language and Audio For Practical Use Cases Runpod RoundUp 3 – AI Music and Stock Sound Effect Creation New Navigational Changes To Runpod UI Use alpha_value To Blast Through Context Limits in LLaMa-2 Models Runpod Roundup 5 – Visual/Language Comprehension, Code-Focused LLMs, and Bias Detection Runpod is Proud to Sponsor the StockDory Chess Engine Runpod Roundup 4 – Open Source LLM Evaluators, 3D Scene Reconstruction, Vector Search Meta and Microsoft Release Llama 2 as Open Source SuperHot 8k Token Context Models Are Here For Text Generation How to Manage Funding Your Runpod Account Encrypted Volumes on Runpod: Protect Your Data at Rest How to Run a "Hello World" on Runpod Serverless Runpod AI field notes: December 2025 Faster GitHub Builds: Major Performance Improvements to Our Automated Integration Partnering with Defined AI to Bridge the Data Wealth Gap How to Run Serverless AI and ML Workloads on Runpod How to fine-tune a model using Axolotl Transcribe and translate audio files with Faster Whisper Runpod Achieves SOC 2 Type II Certification: Continuing Our Compliance Journey Orchestrating GPU workloads on Runpod with dstack Exploring Runpod Serverless: Create Workers From Templates DeepSeek V3.1: A Technical Analysis of Key Changes from V3-0324 Deep Cogito Releases Suite of LLMs Trained with Iterative Policy Improvement Wan 2.2 Releases With a Plethora Of New Features Iterative Refinement Chains with Small Language Models The New Runpod.io: Clearer, Faster, Built for What’s Next Introducing Clusters: On-Demand Multi-Node AI Compute Run DeepSeek R1 on Just 480GB of VRAM How Do I Transfer Data Into My Runpod? 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Track GPU spend across your team with Cost Centers
Brendan McKeag · 2026-04-12 · via Runpod Blog.

Here's a scenario that'll sound familiar if you're running AI workloads across multiple teams. Your ML engineering team has a handful of serverless endpoints serving production inference. Your research team is experimenting with fine-tuning runs on dedicated pods. Someone in product just spun up an Instant Cluster to benchmark a new model. At the end of the month, you get a single invoice and have no clean way to figure out which team spent what, or whether that cluster was even necessary.

Without cost attribution, GPU spend is basically a shared credit card with no receipt tracking. Finance asks questions, you dig through the console, and nobody's happy.

What Cost Centers do

Cost centers let you organize every billable resource on Runpod, including Pods, Serverless endpoints, Network Volumes, and Clusters into named groups. Each resource belongs to exactly one cost center at a time, and your monthly invoice breaks down total spend per cost center automatically. No spreadsheets, no guesswork.

Think of it as putting name tags on your infrastructure. Your ML team's endpoints go into "ml-engineering," your research pods go into "research," and your one-off experiments go into "exploration" (or whatever naming scheme matches your org). When the invoice drops, you can see exactly how much each group spent and map it straight to your internal budget codes.

Setting it up: A walkthrough

Let's say you're a team lead managing three groups that all share a single Runpod account: an inference team, a training team, and an internal tools team. Here's how you'd get cost centers working from scratch.

Step 1: Create your Cost Centers

Head to the Cost center page in the Runpod console. Hit Add a new cost center and create one for each team. We'd recommend names that map to how your org already thinks about budgets—something like:

  • inference-prod
  • training-research
  • internal-tools

Consistent naming pays off later when you're reconciling with your accounting system. You can rename these at any time, so don't overthink it. Just get them created.

Step 2: Assign your resources

Once your cost centers exist, scroll down to the Uncategorized resources section on the same page. This is where every resource that hasn't been assigned yet lives. You'll see tabs for each resource type: Pods, Serverless Endpoints, Storage Volumes, and Clusters.

Select the checkboxes next to the resources you want to group, click Add resources to cost center, and pick the target from the dropdown. You can bulk-select, so if your inference team has six endpoints, you can assign them all in one shot.

Any resource you don't assign stays in the "uncategorized" bucket. It'll still generate charges, but those charges just won't show up under any specific cost center on your invoice. That's fine temporarily, but you'll want to clean this up before month-end.

Step 3: Check your invoice

Navigate to the Invoices tab on the Cost Center page. Each invoice now includes a breakdown of total spend per cost center. So instead of a single line item for "all of Runpod," you'll see your total spend rolled up into categories, such as inference-prod: $2,340.00

That uncategorized $45? Probably a network volume someone forgot to tag. Now you know to go find it.

One thing to note: invoices reflect the cost center assigned to each resource at the end of the billing month. If you move a resource to a different cost center after the month closes, the change only applies going forward, and previous invoices stay as-is. And new spending data can take up to 60 minutes to show up, so don't panic if you just assigned a resource and it's not on the preview invoice yet.

Tips for keeping things clean

We've seen teams get the most out of cost centers when they build a few habits early:

Tag resources as soon as you create them. It takes five seconds to assign a new endpoint or pod to a cost center right after deployment. Waiting until month-end means you're doing cleanup work instead of real work.

Check the uncategorized list weekly. Especially if your team spins up resources frequently, things slip through. A quick glance once a week prevents a pile-up of mystery charges.

Plan ahead for reorgs. When projects wrap up or teams restructure, create the new cost center first, then migrate the resources over. That way there's no gap in attribution. And if you're deleting a cost center entirely, reassign its resources before you pull the trigger. Otherwise, they all drop into the uncategorized bucket.

Wrapping Up

Cost centers are available now in the Runpod console, and there's no limit to how many you can create. Whether you're a two-person startup that just wants to separate "production" from "experiments," or a larger org that needs to map GPU spend to GL codes, the feature works the same way. Set up your cost centers, assign your resources, and let your invoices do the reporting for you.

Head to the Cost center page to get started, and hit us up on Discord if you've got questions.

Author profile: Brendan McKeag