One cluster across
every cloud.

Overcast pools hyperscalers, neoclouds, and your own racks into one cluster of GPUs and CPUs. Built for developers processing massive amounts of data. Run on our capacity or plug in your own cloud accounts.

Get started

label_video.pyyour workload

Lambda256 GPUs

CoreWeave224 GPUs

Nebius160 GPUs

AWS128 GPUs

Azure96 GPUs

Google Cloud64 GPUs

Crusoe64 GPUs

Your racks32 GPUs

One Overcast cloud

Every provider, routed as one cluster

label_video.py

One Overcast cloud

From one prompt to thousands of GPUs or CPUs.

Install the skill

One line teaches your agent to use Overcast.

Cursor~/pipelines

●Read getovercast.com/skill.md

●Added the overcast skill

●Verified cluster access, GPUs and CPUs

✓Ready. Ask me to run anything on Overcast.

Also works in

Claude Code

Codex

Pick your providers

Mix our capacity with your clouds and racks.

Providers

Lambda

CoreWeave

Nebius

AWSyours

Azureyours

Google Cloud

Crusoe

Your racksyours

Price. Each job goes where it costs least, counting the cost of moving your data.Security. Sensitive jobs stay on your own accounts and racks, in regions you approve.Speed. Every provider at once, for the most machines and the fastest finish.

Ask for the job

Cursor writes the code. No hardware to size.

Cursor Agent

I need you to run object detection on all 1 million hours of our dashcam video in S3.

On it. label_video.py is running on 1,024 GPUs across your cluster. Detections stream to s3://burla/dashcam/detections as each batch finishes.

●overcast run label_video.py

Manage and monitor your cluster.

Every machine, GPU or CPU, and dollar in one view. When the job finishes, the cluster scales to zero.

console.getovercast.com/burla/jobs/label_video

Jobs / label_video

Running

job_8f2k41 · started 12:04 by joe@burla.dev via Cursor · H100 · optimize for speed

View logsCancel job

Machines running

128

of 128, 1,024 GPUs

GPU utilization

80.2%

live

Throughput

823,319/s

frames

Spend

$8,970

cap $25,000

1,656,000,000 / 3,600,000,000 frames46% · 40 min left

Machines 128 / 128 receiving code running

Lambda80%

32 machines · 256 GPUs

CoreWeave81%

28 machines · 224 GPUs

Nebius81%

20 machines · 160 GPUs

AWSyours80%

16 machines · 128 GPUs

Azureyours80%

12 machines · 96 GPUs

Google Cloud80%

8 machines · 64 GPUs

Crusoe80%

8 machines · 64 GPUs

Your racks80%

4 machines · 32 GPUs

EventsLogsOutputsstreaming

12:04:03INFOrouteSpeed: all 8 providers at once

12:04:04INFOdeployShipping code to 128 machines

12:04:41INFOcluster128 machines running, 1,024 GPUs at 80% utilization

One cloud is a bottleneck.

Overcast gets you past capacity shortages and gives every workload more options on cost, speed, and security.

Quota lives in one region

When your provider runs out of capacity in us-east-1, GPU or CPU, your job waits in a queue.

Overcast pools every provider into one cluster, so capacity is wherever it is cheapest right now.

Spot reclaims lose work

A machine pulled mid-run means lost results and a retry loop you have to write yourself.

Reclaimed machines are replaced in seconds and every finished result is kept.

Idle machines still bill

Clusters sit half-used between jobs while the meter keeps running.

The cluster scales to zero the moment a job finishes, and never spends past your cap.

Same jobs. Smaller bills.

Without OvercastWith Overcast

Summarize 40M documentsH100 GPUs$68,8003.3 days$35,10012 hours

Detect objects in 3.6B video framesH100 GPUs$11,50013 hours$10,8008 hours

Backtest 5,000 FX strategiesCPUs$2,20012 hours$1,4006 hours

Modeled from public list prices, not measured runs. See the math

You say what to run. Overcast picks where.

Every job goes to the cheapest, most secure, or fastest compute. Then every machine you rent runs close to full, without running out of memory.

How dynamic hardware works

Overcast picks where it runs

Choose price, security, or speed for each job.

PriceThe cheapest GPUsH100s from $3.99 an hour, vs $6.88 on AWS

SecurityYour own cloud accountsData never leaves your cloud

SpeedEvery provider at onceThousands of CPUs and GPUs in parallel

Every machine runs full

Small tasks fill each machine. When a big task needs more memory, Overcast gives it room and boots another machine for the small tasks.

Typical cluster31% used

Crashes when a big task runs out of memory

Overcast90% used

Big tasks get room, nothing crashes

Get on Overcast.

Managed capacity on all hyperscalers and neoclouds
Bring your own AWS, Azure, and Google Cloud accounts, plus your racks
Every call can optimize for price, security, or speed, under your spend cap