Juha Riippi
CEO & Co-founder
A quiet crisis in engineering software
When we asked two hundred and fifty front-line simulation engineers about their daily reality, their answers were striking.
What 250 engineers told us about the state of simulation in 2025
-
84.8% regularly wait for licenses, hardware, or cluster slots before a run can even start.
-
89.2% simplify their models just to keep runtimes manageable, yet fewer than half are happy with the speed they still get.
-
64.8% say their current tools cannot scale far enough to explore design spaces with confidence.
-
Long runtimes top the list of day-to-day frustrations, ahead of meshing, licensing, or collaboration issues.
The implications for engineering teams are clear. Delays in simulation propagate straight into release schedules, opportunity costs, and, ultimately, competitive positioning.
Throughput, not just speed
Runtime gains are important, but the larger lever is throughput—the total number of high-fidelity studies your team can finish in a given budget window. Throughput is what allows you to:
-
Run full factorial sweeps instead of cherry-picked points
-
Move validation work earlier in the cycle
-
Generate the dense datasets that modern AI methods require
Let’s take two examples:
One team working on high-frequency RF circuits needed to understand how sensitive their Grounded Coplanar Waveguide (GCPW) layout was to manufacturing tolerances.
A full statistical sweep over six geometric parameters would normally take weeks—if it was even attempted. But by running hundreds of simulations in parallel, they completed the entire design space sweep and tolerance analysis in under 24 hours.
That included every corner case needed to finalize the layout, with enough data left over to train surrogate models for ongoing exploration.
Fig. 1
Fig. 2
Another team in the piezoelectric space wanted to apply Monte Carlo methods to quantify the impact of parameter uncertainty on the resonant behavior of their ultrasonic transducers.
Traditionally, this would be simplified down to a few handpicked variations. But with sufficient simulation bandwidth, they were able to run hundreds of randomized scenarios per geometry—revealing subtle risk patterns that would have been completely missed otherwise.
Fig. 3
This case is further studied here
Engineers are ready for this shift. In our survey, simulation speed and efficiency topped the list of areas where professionals expect the most progress over the next five years.
Survey respondents confirm the appetite. Simulation speed and efficiency account for the single largest share of the future improvements they hope software will deliver.
Quanscient Allsolve: A capacity-first platform
Quanscient built its cloud-native multiphysics solver around one idea: remove every artificial ceiling on throughput. That shows up in four practical ways:
-
Capacity based licensing, pay for the compute envelope you need, never for individual users or physics modules.
-
Unlimited concurrent jobs, hundreds of simulations can launch in parallel, limited only by the cores you choose to provision.
-
End-to-end multiphysics in one solver so models do not get fragmented across tools, scripts, or licenses.
-
Public or private cloud deployment so enterprise security and IT policies are met without extra overhead.
The impact is tangible. Customers see 25 to 100 times faster completion of full design-of-experiments campaigns and 20 to 200 times more design iterations per project compared with sequential desktop workflows.
The link with AI
Generative design, surrogate modeling, and reinforcement learning all require thousands, sometimes millions, of labeled simulation cases. Without elastic throughput, those initiatives stall. By lifting the practical limit on how many high-quality runs you can complete each week, Quanscient turns AI in simulation from a research curiosity into an operational reality.
What this means for you?
If your roadmap depends on faster iteration, tighter margins of safety, or data-hungry AI initiatives, your current tools may already be the constraint. A capacity-first approach unlocks options that static license counts and on-premise queues simply cannot reach.
Next step
We publish new benchmarks, customer stories, and technical deep dives every month. Subscribe to our updates and see where throughput-driven simulation can take your program next.
