Private beta live now

Background jobs on spot, in your own cloud. 60 to 80% off your cloud bill.

JobRunr Spot finds the cheapest spot instance in your own AWS and Google Cloud accounts, dispatches your job there, and re-runs it if the instance gets killed.

Spot is cheap. Capturing the saving is hard.

AWS and Google Cloud spot instances are 60 to 80 percent cheaper than on-demand. Most teams leave that money on the table because using spot well takes more work than it's worth.

Spot prices change by the minute

The same instance can be four times cheaper in another region, or half the price an hour from now. Tracking that across AWS and Google Cloud is a full-time job. So most teams give up and pay on-demand.

Sudden shutdowns break your jobs

You move a batch job to spot. It gets killed three times. You miss your SLA. You move it back to on-demand. Spot only works if something else handles the failover for you.

Your jobs know they can wait. Your infrastructure doesn't.

A nightly report can wait until 3 AM, when spot is cheapest. A user-facing PDF needs to run now. Neither of those signals lives anywhere your spot manager can read. So you either run everything as if it were urgent, or you build the deadline logic yourself.

Even when you find cheaper capacity, you can't use it

The cheapest spot right now might be a different region, a different AZ, or a different instance family. Your Terraform pins all three. By the time you redeploy to capture the saving, the price has already moved.

One method. Same JobRunr API you already use.

Tell JobRunr Spot when you need the result and what you're willing to pay. We watch spot pricing across AWS and Google Cloud, run your job when it fits, and re-dispatch if the instance gets killed. The API mirrors the carbon-aware jobs API you may already know from JobRunr Pro.

  • ✓ Set a deadline: we pick the cheapest moment to run inside your window
  • ✓ Runs in your own AWS and GCP accounts: your data, your VPC, your existing cloud setup. We're just the broker.
  • ✓ CPU and GPU both supported: starting with CPU in the September release
  • ✓ Automatic failover: spot instance killed? Re-dispatched on the next cheapest match
  • ✓ One outbound WebSocket: no firewall changes, no webhook setup
ReportService.java
// Run a PDF report sometime in the next 4 hours,
// on the cheapest spot CPU we can find across AWS and Google Cloud
BackgroundJob.schedule(
    CostAware.between(now, now.plus(4, HOURS)).on(CPU),
    () -> reportService.generatePdf(reportId));

// GPU works the same way:
BackgroundJob.schedule(
    CostAware.between(now, now.plus(6, HOURS)).on(GPU, "A100"),
    () -> myService.runInference(input));

This shows the shape of the API. Final method names may change based on what design partners tell us. Inspired by the carbon-aware jobs API in JobRunr Pro.

Be one of the first 25 design partners

We're picking a small group of teams to shape the product before the September launch. You'll have direct input on the API, the pricing, and which providers we add next. No commitment, no spam, easy to opt out.

We'll reach out before the private beta opens. No spam, ever.

Early access before the September public release
Direct input on the API, pricing, and provider roadmap
Free use of the JobRunr Spot platform for the first 90 days
1:1 onboarding call with the founding team

If any of this sounds like you, the waitlist is the right place

JobRunr Spot is for the teams already feeling the cost.

Java teams running heavy batch work

PDF generation, ETL, video transcoding, report rendering, image processing. You're paying on-demand and the bill grows every quarter.

Teams who tried spot and got burned

Sudden shutdowns killed your throughput. You missed an SLA. You quietly moved back to on-demand. We handle the failover so spot actually works.

Teams planning to add AI workloads

GPU brokering ships right after CPU. Capture the CPU savings now and the GPU savings later, with the same API and the same dashboard. No rewrite when you scale into inference.

From the team behind JobRunr. Trusted by

Adobe JP Morgan Chase Thoughtworks Capgemini Intuit

FAQ

Questions before you sign up?

What we know so far. Some of this will change based on what design partners tell us.

The first public release ships in September 2026. The private beta opens earlier for design partners on the waitlist. You’ll get access weeks before everyone else.
Your jobs run in your own AWS and Google Cloud accounts. You connect them once with a service role, and JobRunr Spot dispatches work into your environment. The data stays in your VPC, your security and compliance posture doesn’t change, and you keep your existing cloud relationships and discounts. We’re the broker, not the host.
AWS and Google Cloud at launch, with full multi-region and multi-AZ spot routing. We picked these two because they cover most of the Java workloads in production today. Azure and the GPU-specialist clouds (CoreWeave, RunPod, Lambda Labs) follow once the broker is stable.
Yes, but only because we want to ship something stable. GPU brokering is the very next milestone, on the same API and dashboard. Most Java workloads are CPU-bound (PDF, ETL, reporting, transcoding), so starting there gives the most teams an immediate win.
Same API, same dashboard, same limit-order pricing. You’ll pass GPU and the model you need (A100, H100, L4) instead of CPU. If your team is already thinking about AI workloads, tell us in the waitlist follow-up and we’ll keep you in the loop on GPU access specifically.
Yes. JobRunr Spot connects to your existing JobRunr or JobRunr Pro instance over a single outbound WebSocket. You don’t change how you define jobs. The spot features are opt-in per job.
You pay AWS and Google Cloud directly for the compute, and the 60 to 80 percent spot saving stays with you. Our own pricing on top of that is still open. A flat platform fee, a small percentage of spot spend, tiered usage: we’re not locked into any model yet. That’s one of the things we want to figure out together with design partners, so we land on something that actually works for the teams using it.
No. JobRunr Pro keeps doing what it does inside your app: priority queues, batches, workflows, multi-cluster dashboard. JobRunr Spot handles what needs to happen outside your app: the public webhook, the multi-provider broker, the cost optimization.
Sign up anyway and tell us in the follow-up email. We’d rather hear about a use case we hadn’t thought of than miss a design partner who would have been a great fit.