SimOps 101: SimOps vs. DevOps vs. MLOps: What's the Difference?
In June 2009, two engineers from Flickr, John Allspaw and Paul Hammond, walked on stage at the O'Reilly Velocity conference and described how their development and operations teams shipped more than ten deployments a day [1]. At the time, most organizations released software a few times a year. The talk became one of the founding moments of DevOps. A few months later, Patrick Debois organized the first DevOpsDays in Ghent, Belgium, and the movement had a name [2].
Six years later, a team at Google published a paper arguing that machine learning systems carry a special kind of technical debt: the model is easy to build, but everything around it (data pipelines, monitoring, configuration, serving) is expensive to maintain [3]. That observation became the foundation of MLOps.
Engineering simulation has followed the same arc. Solvers have become faster, hardware more powerful, and models larger. But in many organizations, an engineer still waits days for a cluster slot, a license token, or an IT ticket before a simulation can start. SimOps, short for Simulation Operations, is the response to that gap.
All three disciplines end in "Ops," and all three borrow from each other. They are not interchangeable. This guide compares SimOps vs. DevOps vs. MLOps: what each one solves, how they differ, and where they meet.
Why "Ops" Keeps Appearing
DevOps, MLOps, and SimOps share an origin pattern. In each case, a technical discipline grew faster than the operational practices around it:
Software teams could write code faster than operations teams could release it safely.
Data science teams could train models faster than organizations could deploy, monitor, and retrain them.
Engineering teams could build simulation models faster than HPC infrastructure, licensing, and data management could support them.
The result in each case was friction between the people who build and the people who run. Each "Ops" discipline answers that friction with the same three ingredients: shared practices, automation, and a culture of collaboration across team boundaries. What differs is the unit of work, the constraints, and what "done" means.
DevOps: Shipping Code Continuously
DevOps brings software development and IT operations into one workflow. Its central idea is that a code change should move from a developer's laptop to production through an automated, repeatable, observable path.
The core practices are well established:
Continuous integration and continuous delivery (CI/CD): every change is built, tested, and made deployable automatically.
Infrastructure as code: servers, networks, and configurations are defined in version-controlled files instead of manual setups.
Monitoring and fast feedback: production behavior is measured, and problems flow back to the team that can fix them.
Shared ownership: developers and operators are responsible for the same outcome rather than handing work over a wall.
DevOps is also measurable. The DORA research program tracks software delivery performance with five metrics, grouped into throughput (change lead time, deployment frequency, failed deployment recovery time) and instability (change fail rate, deployment rework rate) [4].
The unit of work in DevOps is a code change. A typical CI pipeline runs in minutes, executes on commodity infrastructure, and produces a binary pass or fail.
MLOps: Keeping Models Accurate in Production
MLOps applies DevOps principles to machine learning. Google Cloud describes it as an ML engineering culture and practice that unifies ML system development and ML system operation [5].
The reason MLOps exists is that a machine learning system is not just code. As Google's architecture guide puts it, "only a small fraction of a real-world ML system is composed of the ML code" [5]. The rest is data collection, feature engineering, validation, serving infrastructure, and monitoring.
This is the heart of the DevOps vs. MLOps distinction. Three things make ML different from conventional software:
Data is a first-class artifact. A model can break without a single line of code changing, because the input data shifted.
Models decay. Prediction quality drops as the real world drifts away from the training data. This is why MLOps adds continuous training (CT), which Google calls "a new property, unique to ML systems" [5].
Testing is statistical. A model is not correct or incorrect; it is more or less accurate on a given dataset.
Google's guide describes three maturity levels: Level 0 (manual process), Level 1 (ML pipeline automation), and Level 2 (CI/CD pipeline automation) [5].
The unit of work in MLOps is a trained model, together with the data and pipeline that produced it.
What Is SimOps? Running Simulations at Scale
SimOps applies the same operational thinking to engineering simulation and the HPC infrastructure it runs on, covering both simulation operations (how models are built, run, and managed) and HPC operations (how clusters, licenses, and data are provisioned). The framework was developed from more than 230 engineering simulation projects and 160 case studies collected over 12 years, from which the founding team extracted 44 best practices. DevOps served explicitly as the model [6]. The SimOps Framework was introduced in 2024.
What makes simulation operationally different from software and ML is the nature of the workload itself:
Runtimes are long. A CI test runs in minutes. A high-fidelity CFD or crash simulation can run for hours, days, or weeks.
Workloads are tightly coupled. Many solvers distribute a single job across hundreds or thousands of cores that exchange data constantly over low-latency interconnects. This is a different infrastructure problem from scaling independent web services. For a closer look, see our guide to HPC Architectures 101.
Licenses are a hard constraint. Commercial solvers are often licensed per core or per token. A job can sit idle in a queue not because hardware is busy, but because no license is available.
Results are large. A single run can produce hundreds of gigabytes of output, which makes data movement and storage an operational decision, not an afterthought.
Correctness is physical. A simulation is validated against physical tests and engineering judgment, not unit tests. A run that finishes without errors can still be wrong.
The users are engineers, not software developers. Most simulation engineers are experts in structures, fluids, or thermal physics. They should not need to be Linux administrators to launch a job.
The SimOps framework organizes its response through the Golden Triangle of Technology, Process, and People [7], a set of 20 SimOps Best Practices [8], and a Maturity Model with three stages: Prove, Scale, and Optimize [9]. With SimOps 2.0, the scope is widening from operational excellence in simulation to engineering capability more broadly, as AI, HPC, digital twins, and engineering data converge [10].
The unit of work in SimOps is a simulation run, together with the model, solver settings, hardware, licenses, and data that make it reproducible.
SimOps vs. DevOps vs. MLOps: Side-by-Side Comparison
Dimension | DevOps | MLOps | SimOps |
Core problem | Slow, risky software releases | Models that are hard to deploy and decay in production | Simulations slowed by infrastructure, licensing, and manual workflows |
Primary artifact | Code change | Trained model plus data | Simulation run plus model, solver settings, and results |
Primary users | Software developers, operations engineers | Data scientists, ML engineers | Simulation engineers, HPC administrators, IT, engineering leaders |
Typical run time | Minutes | Minutes to days (training) | Hours to weeks |
Key infrastructure | Cloud, containers, CI servers | GPU clusters, feature stores, model registries | HPC clusters, schedulers, parallel file systems, cloud HPC |
Key constraint | Release risk | Data quality and drift | Compute capacity, licenses, data volume |
What "correct" means | Tests pass | Accuracy within tolerance | Physically valid, verified against test data |
What gets versioned | Code, infrastructure definitions | Code, data, models, features | Solver settings, meshes, scripts, containers, results |
Maturity reference | DORA performance levels | Google MLOps Levels 0 to 2 | SimOps Maturity Model: Prove, Scale, Optimize |
What SimOps Borrows from DevOps
Several of the 20 SimOps Best Practices are direct translations of DevOps ideas into the simulation world [8]:
Automate end-to-end workflows (Best Practice 3). Pre-processing, solver execution, post-processing, and data archival become one repeatable pipeline, triggered by APIs or events. This is the simulation equivalent of a CI/CD pipeline.
Version-control workflows, not just code (Best Practice 10). Solver settings, mesh scripts, post-processing macros, and container specifications live in Git, so any result can be reproduced on demand.
Provide self-service simulation portals (Best Practice 4). Engineers launch and track jobs from a browser without opening an IT ticket, just as developers deploy without waiting for an operations team.
Integrate with PLM, CAD, and CI/CD pipelines (Best Practice 18). A design change can trigger a simulation automatically, with pass/fail results fed back into the change process.
Establish continuous feedback and improvement loops (Best Practice 14). Post-mortems after each project feed lessons back into templates and training, similar to blameless retrospectives in DevOps.
What SimOps Borrows from MLOps
The connection to MLOps is growing quickly, because simulation is now one of the largest sources of engineering training data:
Treat data as a production asset. SimOps added 10 best practices specifically for engineering data, starting with converting solver output into analytics-friendly formats as soon as it is produced and centralizing it in a shared, structured hub [8].
Manage the full data lifecycle (Best Practice 11). Capture, quality checks, deduplication, tiered storage, and governed deletion, so simulation results become AI-ready rather than archived and forgotten.
Use AI and ML for predictive insight (Best Practice 12). Surrogate models and reduced-order models trained on simulation data can cut solver runtimes for early design exploration. Once a surrogate model is in production, it needs the same versioning, monitoring, and retraining that MLOps provides.
SimOps vs. DevOps: Why DevOps Tools Alone Are Not Enough
It is tempting to take an existing DevOps toolchain and point it at a simulation cluster. Parts of it transfer well: Git, containers, workflow orchestration, and monitoring are all useful in SimOps. But some assumptions break.
A CI pipeline assumes a test runs in minutes and costs almost nothing. A full-vehicle aerodynamics run can occupy thousands of cores for a day. Running it on every commit is neither possible nor useful, so SimOps pipelines need tiers: coarse, fast checks early in the design process, and high-fidelity runs at defined gates. This is the "shift left" principle of Best Practice 2 adapted to simulation economics.
DevOps assumes compute is elastic and cheap. In simulation, the real bottleneck is often the license server or the queue. That is why SimOps treats license and hardware utilization (Best Practice 17), cost tracking (Best Practice 8), and the placement of workloads between on-premise and cloud (Best Practice 5) as operational disciplines. We cover the trade-offs in detail in Cloud HPC vs. On-Premise and GPU vs. CPU for Simulation Workloads.
Finally, DevOps tests check whether software does what its specification says. Simulation results must also agree with physics. Verification and validation against physical tests remain an engineering responsibility that no pipeline can automate away. SimOps automates the operations around that judgment so engineers have more time for it.
What to Measure in SimOps
DevOps has DORA. SimOps does not yet have a single standardized metric set, but the 20 SimOps Best Practices point clearly to the signals worth tracking [8]:
Queue wait time and time-to-result: how long an engineer waits between submitting a job and seeing results (Best Practice 16).
License and hardware utilization: idle tokens and stranded cores, correlated with job telemetry (Best Practice 17).
Cost per simulation: every workload tagged, tracked, and compared across CPU, GPU, on-premise, and cloud options (Best Practice 8).
Performance baselines: benchmarks per solver and hardware combination, re-run after upgrades (Best Practice 13).
Energy per simulation: kWh tracked per run, with non-urgent jobs scheduled when greener energy is available (Best Practice 19).
Organizations that track even two or three of these consistently gain a much clearer view of where simulation time and money actually go.
Where the Three Converge
In practice, many engineering organizations need all three disciplines at once. Consider a team designing an electric vehicle:
Its embedded software and cloud services are delivered through DevOps pipelines.
Its battery degradation models and driver-assistance features rely on MLOps to stay accurate as field data arrives.
Its crash, thermal, and aerodynamics simulations run through SimOps practices on HPC and cloud resources.
The boundaries are blurring. Simulation data trains AI surrogate models. Digital twins combine live sensor data with physics-based models (see Digital Twin vs. Simulation). AI agents are starting to help administer simulation clusters (see AI Agents Meet Modern SimOps-Compliant Cluster Administration). This convergence is the reason SimOps 2.0 focuses on engineering capability rather than simulation operations alone [10].
The practical takeaway is not to choose one "Ops" over another. It is to recognize which problems each one solves and to make sure simulation workloads get operational practices designed for their constraints.
Getting Started
If your organization already runs DevOps or MLOps, you have a head start: the culture of automation, version control, and shared ownership carries over. A practical first step, following the guidance on the SimOps Best Practices page, is to pick three to five high-impact gaps, run a small pilot that exercises them end to end, document what worked, and reassess every six to twelve months [8].
For individuals, the SimOps certification program offers a structured path from Fundamentals to Practitioner to Expert.
Key Takeaways
DevOps, MLOps, and SimOps all address the same pattern: a technical discipline that outgrew its operational practices.
In the SimOps vs. DevOps comparison, the difference is the unit of work: DevOps manages code changes, MLOps manages models and data, and SimOps manages simulation runs and the HPC operations behind them.
Simulation brings constraints that software and ML pipelines were not designed for: long runtimes, tightly coupled parallel jobs, license limits, large result files, and physical validation.
SimOps borrows automation, version control, self-service, and CI/CD integration from DevOps, and data lifecycle management and surrogate modeling from MLOps.
Most engineering organizations will need all three. The goal is to apply the right operational practices to each workload.
Frequently Asked Questions
What is SimOps?
SimOps (Simulation Operations) is a vendor-neutral framework of best practices for automating and managing engineering simulation workflows and the HPC infrastructure they run on, built around Technology, Process, and People.
Is SimOps just DevOps for engineering simulation?
DevOps was the model for SimOps, and many principles transfer directly. But simulation adds constraints that DevOps does not address, such as solver licensing, tightly coupled HPC jobs, multi-day runtimes, and validation against physical tests. SimOps adapts DevOps principles to those realities.
What is the difference between MLOps and SimOps?
MLOps manages the lifecycle of machine learning models, with a focus on data quality, model drift, and retraining. SimOps manages the lifecycle of physics-based simulations and the HPC infrastructure they run on. The two meet when simulation data is used to train AI surrogate models.
Do I need a DevOps background to work in SimOps?
No. SimOps practitioners come from simulation engineering, HPC administration, IT, and engineering management. Familiarity with automation and version control helps, but domain knowledge in simulation and HPC matters more.
Who is SimOps for?
Simulation engineers, HPC and IT teams, and engineering leaders who want simulations to run faster, cost less, and produce reproducible results.
What's Next in This Series?
This is part of SimOps' Simulation 101 series. Related reading:
References
Allspaw, J., and Hammond, P. (2009). 10+ Deploys per Day: Dev and Ops Cooperation at Flickr. O'Reilly Velocity Conference. slideshare.net
DevOps.com. The Origins of DevOps: What's in a Name? devops.com
Sculley, D., et al. (2015). Hidden Technical Debt in Machine Learning Systems. Advances in Neural Information Processing Systems 28. neurips.cc
DORA. DORA's Software Delivery Performance Metrics. dora.dev
Google Cloud. MLOps: Continuous Delivery and Automation Pipelines in Machine Learning. cloud.google.com
Gentzsch, W. (2025). SimOps, A New HPC Market Category. simops.com
SimOps. The Golden Triangle for SimOps. simops.com
SimOps. SimOps Best Practices. simops.com
SimOps. How to Reach SimOps Maturity for Your Organization's Engineering Simulation Operations. simops.com
SimOps. SimOps 2.0. simops.com
About SimOps
Software development was transformed when teams stopped treating infrastructure as an afterthought and started building shared practices around it. That's what DevOps did for code. SimOps is doing the same thing for simulation.
Simulation and HPC have long operated in silos: different tools, different teams, different workflows, with no shared language or common standards. SimOps exists to change that. We're building a framework and a community where simulation engineers, HPC specialists, and CAE teams can work from the same playbook, share best practices, and push the field forward together.
If that mission resonates with you, this series is a starting point. And our community is where the conversation continues.
