You can find the run time of each job under the RUN TIME (S) column in the Results tab of a project, and the related usage under the CREDITS column.

You can also find your own overall compute and storage usage, broken down by month and by day, in the Usage tab for your account. If you’re an admin user, you can see the usage for others within your organization there, too.

While our calculation of credit usage is based on behind-the-scenes rules that take into account the run time and resources, you may notice some variability in both the run time and the associated credits used by each simulation. We cannot exactly predict the time and resource requirements before running each simulation, but the factors that influence this include the number and types of cloud processors used, differences between physical models (incompressible flow physics is generally more complicated than compressible), whether you wish to solve for a steady or transient solution, and the types and setup of the linear solvers used (with algebraic multigrid (AMG) being more costly). Pre-processing optimizations performed on the first run of a mesh in a project may also speed up subsequent runs. Additionally, the overall number of iterations needed to appropriately converge a solution will be highly dependent on the problem setup itself, including the quality of the mesh, the flow-field initialization and the boundary conditions.

In some situations, our platform will also dynamically discount simulations. One example of why this might happen will be to create pricing consistency between similar simulations run on different cloud hardware. We cannot guarantee exactly when or how dynamic discounts may be applied, but can stress that when they are applied they will always be used to reduce your costs.

The details of our pricing structure can be found in our Service Consumption Table.