Remote burst compute for CAE engineers without IT approval

The moment arrives when an engineer has a deadline in 48 hours and their workstation — whether an aging Dell with 32GB RAM or a modern laptop without a discrete GPU — simply cannot finish the job. The sim either crashes halfway through, takes 18 hours to run, or they have to downsample the mesh until the results are too coarse to trust. They know cloud compute exists, but spinning up the right instance type, installing the solver license, transferring multi-gigabyte case files, and retrieving results requires either a DevOps engineer or a painful afternoon of trial and error. Most engineers never get there.

The gap persists for a structural reason: the CAE software vendors sell licenses, not compute. They have no incentive to make it easy to run their software on hardware they don't own or control. HPC clusters sold by IT departments are provisioned for the company's median workload, not peak demand, and getting allocation requires ticket queues that outlast the deadline. Cloud marketplaces offer raw VMs but nothing tuned for the specific memory and core requirements of HFSS, Fluent, or HyperMesh jobs.

What engineers describe is partly a hardware problem and partly a workflow problem: 'the software defaults to using only 4 cores for calculations regardless of hardware performance, limiting its local performance without purchasing a license.' That 4-core limit isn't a bug — it's a license tier. Running the same model on a 64-core cloud instance solves nothing if the license can't use it. Any burst compute service for this space has to handle solver license portability (HPC tokens, pay-per-use license pools) alongside the compute itself — otherwise you're just selling expensive VMs that the software still artificially throttles.

Without something like this, an engineer's only real options are: wait 18 hours on local hardware and risk a crash, beg IT for HPC allocation, or ask their manager to approve a cloud spend that has no precedent in the budget. Each of those costs real time on real projects. The need recurs every time a model gets more complex — which is every project cycle.

What to build

Build a browser-based job launcher that accepts a packaged CAE case file (Fluent .cas, HyperMesh .hm, HFSS .aedt), spins up the correctly-sized cloud instance pre-configured for that solver, manages HPC license token consumption transparently, runs the job, and returns results to a local folder — without the engineer touching a terminal.

Where to start

Start exclusively with Ansys Fluent CFD jobs, because Ansys already sells HPC token licenses that are designed to be consumed elastically — meaning the license model is already cloud-compatible, and you're solving the workflow wrapper around an existing mechanism rather than fighting licensing architecture from scratch.

The hard part

Solver license portability is the hard problem — most CAE vendors use hardware-locked or network-locked licensing that actively resists running on ephemeral cloud instances, so you either have to partner with vendors for cloud-native license pools or build a license proxy that stays within the terms of service, and that negotiation alone can take a year.

How it makes money

Usage-based billing: charge per core-hour consumed above a free monthly quota of 50 core-hours, with a flat monthly subscription tier for teams that need predictable spend; license token costs pass through at cost plus a small markup.