Hardware and cloud cost planner for CAE teams

The moment this problem hits is when an engineer queues a large Ansys job, it runs out of memory on local hardware, and the team has no clear answer for whether they should buy more RAM, move to cloud compute, or restructure the mesh. Users say Ansys 'requires a lot of computing power,' that 'bigger problems need server support,' and that 'Ansys software requires a higher initial investment due to the need for a powerful computer system' — but none of those complaints are accompanied by a clear framework for what hardware is actually right for what simulation type and size.

This gap persists because Ansys has no incentive to help customers spend less on hardware, and no direct stake in what compute infrastructure customers run. IT departments don't know enough about simulation workloads to spec hardware correctly, and simulation engineers don't know enough about infrastructure pricing to evaluate cloud vs. on-prem tradeoffs. The decision falls into a gap between two teams who speak different languages.

Currently, a company over-buying workstation hardware for small CFD jobs or under-speccing servers for large structural models has no feedback loop — they just accept slow solves or out-of-memory crashes as normal. The information needed to make a better decision (how does job size relate to RAM and core count for Ansys Fluent vs. Ansys Mechanical, at what point does cloud burst compute become cheaper than a new workstation) exists in benchmark data and in user forums, but is never assembled into a decision tool.

This is a business rather than a feature because the hardware decision recurs every budget cycle, every time the team takes on a new class of problem, and every time cloud pricing changes. A company that makes the wrong call either buys $40,000 of workstations that are wrong for their workloads, or pays three times market rate for cloud compute by not knowing how to size jobs correctly.

What to build

Build a web-based calculator where a simulation engineer inputs their Ansys module, problem type, mesh size range, and job frequency, and receives a specific hardware spec recommendation alongside a break-even analysis comparing on-prem workstations, on-prem servers, and major cloud HPC options at current pricing.

Where to start

Start with Ansys Fluent CFD jobs, where mesh size and RAM usage are more predictable and better documented in public benchmarks than structural nonlinear problems, and target mid-size aerospace or automotive suppliers who already know they have a compute bottleneck and are actively budgeting for infrastructure.

The hard part

Benchmark data across Ansys modules, problem types, and mesh sizes is sparse and version-dependent — building a recommendation engine that's accurate enough that engineers trust it requires running a significant number of actual Ansys jobs across controlled configurations before the product is credible.

How it makes money

Free for the basic calculator to drive traffic and trust; charge engineering managers a one-time fee for a detailed infrastructure audit report, or a recurring subscription for teams that want updated recommendations as cloud pricing shifts quarterly.