Short answer: if you use ComfyUI for less than about two to three hours a day, renting a cloud GPU is usually cheaper than buying one. If you generate every day for hours, a local card pays for itself. I measured my own RTX 5080 16 GB today. A Krea 2 Turbo image took 29.5 seconds and used 0.76 Wh of GPU power. A 5-second Wan 2.2 14B image-to-video clip took 117 seconds warm and used 5.7 Wh. At the US average home electricity price, that’s roughly $0.0001 per image and $0.001 per video on my PC. On Comfy Cloud, the same kind of Wan 2.2 clip costs about $0.04 to $0.05. The real cost of local is the card itself, so the question is how many hours you’ll actually use it.
- Local running cost is tiny: on my RTX 5080, Krea 2 Turbo images averaged 29.5 s and 0.76 Wh each, and Wan 2.2 I2V (640×640, 81 frames, 4 steps) took 117 s and 5.7 Wh warm. That’s about $0.14 of electricity per 1,000 images at 18.16¢/kWh, counting the GPU board only.
- Cloud prices (checked Oct 26, 2026): RunPod lists the RTX 5090 at $0.69/hr Community and $1.19/hr Secure, and the RTX 4090 at $0.34/hr and $0.89/hr. Comfy Cloud Standard is $20/month, or $192/year with 50,400 credits, and Comfy says a 5 s Wan 2.2 I2V video is about 11 credits.
- Break-even: at the RTX 5080’s $999 launch price, you need about 1,520 GPU-hours of use to beat RunPod’s RTX 5090 Community rate, or about 860 hours against Secure Cloud. At 3 hours a day, that’s 10 to 17 months.
- Rent if: you need more than 16 GB of VRAM, you only generate on weekends, or you’re trying a model before buying hardware.
- Buy if: you generate daily, train LoRAs, want privacy, or already have a gaming PC that just needs a GPU.
Is a Cloud GPU Cheaper Than a Local GPU for ComfyUI?
It depends on one number: how many hours the GPU is actually busy. A rented GPU costs nothing when it’s switched off. A card you own costs the same whether you use it or not, because you paid for it up front. Once you own it, each image costs almost nothing, just a few watt-hours of electricity.
So the comparison isn’t “cloud is $0.69 an hour and local is free.” It’s “how many rented hours would I need to pay for the card?” Most guides answer that with guesses. I wanted real numbers, so I ran two common ComfyUI workloads on my own PC, logged the GPU power draw every half second, and compared them with the cloud prices published today.
If you’re still deciding which card to buy, read my how much VRAM you need for ComfyUI test first. VRAM decides which models you can run at all, and cost only matters after that.
How Much Does a Cloud GPU for ComfyUI Cost in 2026?
These are the on-demand Pod prices from the RunPod pricing page, which says “Updated October 8, 2026”. I checked them on Oct 26, 2026. Each GPU has a cheaper Community Cloud price and a higher Secure Cloud price.
| GPU (VRAM) | RunPod Community | RunPod Secure | Good for in ComfyUI |
|---|---|---|---|
| RTX A5000 (24 GB) | $0.16/hr | $0.27/hr | SDXL, FP8 image models, learning |
| RTX 3090 (24 GB) | $0.22/hr | $0.50/hr | Cheap 24 GB for image work |
| RTX 4090 (24 GB) | $0.34/hr | $0.89/hr | Fast image generation, FP8 video |
| RTX 5090 (32 GB) | $0.69/hr | $1.19/hr | Closest match to a high-end home PC |
| RTX A6000 (48 GB) | $0.33/hr | $0.59/hr | Big video models without offloading |
| L40S (48 GB) | $0.79/hr | $1.09/hr | Faster 48 GB option |
| RTX Pro 6000 (96 GB) | $1.69/hr | $2.49/hr | Full-precision video models, LoRA training |
| H100 PCIe (80 GB) | $1.99/hr | $2.89/hr | Training, very large batches |
Storage is extra. RunPod’s network storage is $0.07/GB per month under 1 TB, and a stopped Pod’s volume disk is $0.20/GB per month. That matters for ComfyUI, because model files are big. My Wan 2.2 14B image-to-video setup is about 34 GB on disk, which works out to roughly $2.38 a month on network storage.
The cheapest cards aren’t always the cheapest choice. A 24 GB card at $0.16/hr that’s three times slower than an RTX 5090 costs more per image. For ComfyUI, compare cost per finished image or video, not just the hourly price.
How Does Comfy Cloud Pricing Work?
Comfy Cloud is the official hosted ComfyUI from the Comfy team. You don’t rent a machine. You buy a plan with credits, and credits are only used while a workflow is running. Time spent building a workflow is free. Here’s what the pricing page listed on Oct 26, 2026:
- Standard: $20/month, or $16/month billed yearly ($192), with 50,400 credits per year. There’s a 30-minute limit per workflow run.
- Creator: $35/month, or $28/month billed yearly ($336). It adds importing your own models and LoRAs from Civitai or Hugging Face.
- Hardware: Comfy says Cloud runs on the Blackwell RTX 6000 Pro with 96 GB of VRAM, and that more than 900 models come pre-installed.
- Reference cost: Comfy says a 5-second video from the Wan 2.2 Image-to-Video template at default settings (81 frames, 640×640, 4-step sampler) uses about 11 credits.
- Comfy API (deploying workflows as an API): RTX PRO 6000 at $3.49/hr (736.39 credits/hr), billed by the GPU second.
That gives a credit value. On the yearly Standard plan, $192 for 50,400 credits is about $0.0038 per credit. At the API rate, $3.49 for 736.39 credits is about $0.0047 per credit. So the 11-credit Wan 2.2 clip costs roughly $0.042 to $0.052. That’s 50,400 ÷ 12 ÷ 11, or about 380 of those clips a month on Standard if you spent credits on nothing else.
The big advantage is 96 GB of VRAM with no setup. You never hit an out-of-memory error, and you never spend 20 minutes downloading models onto a fresh Pod.
My RTX 5080 16 GB Test Results
I ran these tests today, Oct 26, 2026, on my Windows PC. It uses ComfyUI portable on an RTX 5080 16 GB with nothing else loaded on that GPU. A small script sent each job to the ComfyUI API, timed it from queue to finished file, and read nvidia-smi power draw every 500 ms. Watt-hours = average board power × run time. These are GPU board figures only. I didn’t have a wall meter, so CPU, RAM, fans and PSU losses aren’t included. My card reported a 260 W power limit, and the highest draw I saw was 266.5 W.
| Workload (ComfyUI) | Settings | Time per output | Avg GPU power | Energy per output | Peak VRAM used |
|---|---|---|---|---|---|
Krea 2 Turbo FP8 (krea2_turbo_fp8_scaled.safetensors) |
1344×768 and 1024×1024, 8 steps, euler/simple, CFG 1, 6 runs | 27.1 to 32.5 s (avg 29.5 s) | 84.5 to 98.6 W | 0.72 to 0.79 Wh (avg 0.76 Wh) | 12.1 GB |
| Wan 2.2 I2V 14B FP8 + Lightning 4-step LoRAs (first run, models loading) | 640×640, 81 frames (5 s at 16 fps), 4 steps (2 high + 2 low) | 165.3 s | 125.9 W | 5.78 Wh | 10.5 GB |
| Wan 2.2 I2V 14B FP8 + Lightning 4-step LoRAs (second run) | Same settings, new seed | 117.1 s | 175.3 W | 5.70 Wh | 10.4 GB |
| Idle (between jobs) | ComfyUI open, nothing running | n/a | 7.6 W | n/a | n/a |
The Wan job copies the settings Comfy uses for its 11-credit reference clip. I used the official wan2.2_i2v_high_noise_14B_fp8_scaled and low_noise models, umt5_xxl_fp8_e4m3fn_scaled, wan_2.1_vae, and the Wan2.2-Lightning I2V A14B 4-step LoRAs (Wan2.2-Lightning_I2V-A14B-4steps-lora_HIGH_fp16 and _LOW_fp16). The input was one of my Krea 2 images. Krea 2’s average power is low because part of each run is spent loading and moving the model and text encoder, not sampling. That’s normal on a 16 GB card.
What Is the Real Cost per Image and per Video?
For local electricity I used the US average residential price of 18.16¢/kWh for August 2026, from the EIA Electricity Monthly Update. For your own rate, multiply the Wh figure by your price per kWh and divide by 1,000.
| Option | Krea 2 image (29.5 s of GPU time) | Per 1,000 images | Wan 2.2 5 s I2V clip |
|---|---|---|---|
| My RTX 5080, electricity only (GPU board) | $0.00014 | $0.14 | $0.0010 |
| RunPod RTX 5090 Community ($0.69/hr), if it only matches my 5080’s speed | $0.0057 | $5.65 | $0.022 |
| RunPod RTX 5090 Secure ($1.19/hr), same assumption | $0.0097 | $9.75 | $0.039 |
| Comfy Cloud (11 credits, Comfy’s own figure) | n/a (no official per-image figure) | n/a | $0.042 to $0.052 |
A note on the RunPod rows: I didn’t rent a 5090 for this test. I took my measured RTX 5080 times and multiplied by RunPod’s hourly price. A 5090 has twice the VRAM and more compute, so it shouldn’t be slower. Read those rows as a ceiling for pure GPU time. In practice you also pay for Pod boot time, model downloads and the minutes you leave it running while you tweak prompts. That usually costs more than the generation itself.
From the Comfy side, if Cloud credits are used at the same rate as the API (736.39 credits/hr), 11 credits works out to about 54 seconds of RTX PRO 6000 time for the clip. My 16 GB card needed 117 seconds. So a 96 GB cloud card is faster per clip, but each clip costs about 40 to 50 times my electricity.
How Many Hours Until a Local GPU Pays for Itself?
NVIDIA launched the RTX 5080 at $999 (announced Jan 6, 2025). Street prices are higher right now. TechSpot’s price tracker showed $1,999 on Oct 20, 2026. So I ran the numbers at both prices. My saving per rented hour is the rental price minus my local electricity at full video load, which is about $0.032 per hour (175 W at 18.16¢/kWh).
| RTX 5080 price | vs RunPod 5090 Community ($0.69/hr) | vs RunPod 5090 Secure ($1.19/hr) | vs Comfy Cloud Wan clips (~$0.042 each) |
|---|---|---|---|
| $999 (launch MSRP) | ~1,520 GPU-hours | ~860 GPU-hours | ~24,400 clips |
| $1,999 (TechSpot, Oct 20, 2026) | ~3,040 GPU-hours | ~1,730 GPU-hours | ~48,900 clips |
To turn that into time: at 1 hour a day, 1,520 hours is over 4 years. At 3 hours a day, it’s about 17 months. At 8 hours a day (batch jobs, LoRA training, overnight queues), it’s about 6 months. These numbers only count the GPU. If you need to build a whole PC for it, add the rest of the parts to the price. If you’d buy the card for gaming or work anyway, the AI part is close to free.
Also be honest about your hours. Most people expect to use a GPU far more than they actually do. Check your ComfyUI history or your cloud bills for a month before you decide.
What Hidden Costs Do People Forget?
On the cloud side:
- Idle billing: a Pod bills by the second while it’s running, even when you’re just reading Reddit. Forgetting to stop it is the most common surprise bill.
- Model downloads: a fresh Pod has to pull tens of GB of models before your first image. Network storage avoids that, but it has its own monthly fee.
- Storage while stopped: RunPod charges $0.20/GB per month for idle volume disks, so a 100 GB volume costs $20 a month just sitting there.
- Job limits on Comfy Cloud: Standard and Creator stop any run after 30 minutes (Pro allows 1 hour). Long upscale or video chains need to fit inside that.
- Custom nodes: Comfy Cloud supports a list of popular custom nodes, not all of them. A niche node pack can block a workflow.
On the local side:
- VRAM ceiling: 16 GB runs FP8 image models and quantized 14B video models, as my test shows, but the newest big video models need offloading or GGUF quantization. My best local AI video models guide covers what fits.
- Whole-system power: my numbers are GPU-only. CPU, RAM and fans add more, though usually much less than the GPU under load.
- Your time: driver updates, Python dependency breakage and node conflicts are real. You save money but spend some hours fixing things.
- Resale and depreciation: a card loses value every year. On the other hand, it still has resale value, while rental money is gone.
Should You Rent or Buy? My Recommendation
This is how I’d decide in 2026:
- Rent (RunPod Community or Comfy Cloud) if you use ComfyUI a few hours a week, you’re testing a model before you commit, or you need 48 to 96 GB of VRAM for full-precision video. Comfy Cloud is easiest if you just want workflows to run. RunPod is cheaper per hour if you’re happy setting up a Pod and storage.
- Buy a 16 GB card if you generate every day, run long batch queues, train LoRAs, or work with client or private images that shouldn’t leave your PC. Once you own it, a thousand images cost about 14 cents of power.
- Do both if your daily work fits in 16 GB but some jobs don’t. I run images and quantized video locally, and only rent big VRAM when a model needs it.
The same logic applies to local LLMs. In my Nemotron 3.5 Bolt E4B speed test, a small GGUF model ran at 163 tokens per second in 3.5 GB of VRAM. Once you own the card, that kind of workload costs almost nothing.
FAQ
How much does it cost to run ComfyUI on RunPod?
On Oct 26, 2026, RunPod’s pricing page listed the RTX 4090 at $0.34/hr (Community) or $0.89/hr (Secure), and the RTX 5090 at $0.69/hr or $1.19/hr. Add storage for your models, such as network storage at $0.07/GB per month. Light hobby use of a few hours a week usually costs a few dollars a month.
Is Comfy Cloud worth it?
It’s worth it if you want zero setup and 96 GB of VRAM. Standard is $20/month, or $192/year with 50,400 credits. Comfy says a 5-second Wan 2.2 I2V clip uses about 11 credits, roughly $0.04 to $0.05. If you already own a 16 GB GPU and your models fit, running locally is much cheaper per output.
How much electricity does ComfyUI use per image?
On my RTX 5080, a Krea 2 Turbo image used 0.72 to 0.79 Wh of GPU power, and a 5-second Wan 2.2 14B I2V clip used about 5.7 Wh. At 18.16¢/kWh, that’s about $0.00014 per image and $0.001 per clip, counting the GPU board only.
Is it cheaper to rent a GPU or buy an RTX 5080 for AI?
At the $999 launch price, buying beats RunPod’s RTX 5090 Community rate after about 1,520 hours of use, or about 860 hours against Secure Cloud. Under about two to three hours a day, renting is usually cheaper for the first year or two.
Can a 16 GB GPU run Wan 2.2 14B in ComfyUI?
Yes. With the FP8 scaled Wan 2.2 I2V high- and low-noise models and the 4-step Lightning LoRAs, my RTX 5080 16 GB made a 640×640, 81-frame clip in 117 seconds, with about 10.5 GB of VRAM used on the GPU.
Do cloud GPU prices change?
Yes, often. Every price in this article is dated and linked to its source. Check the provider’s pricing page before you commit, especially for Community or marketplace GPUs, where rates depend on availability.
