There is a tipping point engineering teams can reach with internal thermal simulation capabilities. Product complexity or high accuracy demands may have begun to outpace what approximations in legacy tools can support. There are other factors that force a decision, too, like electrification (batteries care a lot about heat), higher power densities, or tighter performance targets.
So, do you build a thermal solver internally or integrate an external one into your existing software stack? That choice will carry long-term consequences for engineering hours and product timelines. Let’s explore each scenario to compare the costs — both direct financial impacts and other costs in the form of resources and time.
What Does It Take to Build a Thermal Simulation Solution?
Building (or upgrading) an in-house thermal simulation tool is not a short project or a side initiative. It requires deep expertise in physics modeling, numerical methods, and software development, often across multiple teams.
For example, a full-featured thermal modeling engine could include:
- Solvers for conduction, convection, and sometimes radiation
- Material property handling and temperature-dependent behavior
- Scalable performance across large models
- Robust meshing and discretization methods
- Validation and benchmarking against real-world data
It’s realistic to expect development timelines to stretch five to ten years before reaching maturity. Even then, the work does not stop. Ongoing investment is required to improve the software’s accuracy and stability. You may want to expand its capabilities to meet new use cases, and other changes will be necessary over time to maintain compatibility with evolving hardware and software environments. There’s support users and documentation to consider, too, which must also stay updated as the product changes.
For many organizations, this substantive effort grows into a parallel product line that can feel like it’s competing with core business priorities.
Hidden Maintenance Costs of an In-House Thermal Engine
The upfront investment needed to build a thermal solver is substantial, but it’s only part of the overall “cost” equation. Even for teams that already use a custom in-house solution, maintenance introduces a second layer of costs that are harder to quantify, but just as impactful:
- Specialized talent dependency: Key contributors hold critical knowledge of the solver’s architecture and physics models. Turnover becomes a larger issue.
- Aging codebase: Updates become slower and riskier as systems grow more complex.
- Limited scalability: Performance constraints may emerge as model sizes and expectations increase.
- Feature gaps: New requirements, such as advanced cooling strategies or electrification use cases, could require additional development cycles and adjustments.
How do you know if these hidden costs have become too burdensome? Engineering leaders may even know that the solver has delivered value for years. But, if they find that it hasn’t kept pace with current needs, the question shifts from “how do we maintain this?” to “should we still be maintaining this?”
Why Do In-House Thermal Solvers Break Down Over Time?
Even well-built internal tools are bound to face structural limits over time that require re-investment. Here are the four stressors that tend to build up to a breaking point:
| Not a Core Business Function
Most companies that provide an engineering simulation environment do not compete purely based on their thermal solver. The effort it takes to maintain one diverts resources from product development and innovation for the core product offering. |
Knowledge Concentration
As experienced engineers move on or retire, continuity gets harder and harder to maintain. There’s already a talent shortage in the labor market, and new hires will take time to learn and master complex aging codebases of custom software that pre-existed their tenure. |
| Rising Expectations
Broader industry movement towards electrification has increased the importance of thermal simulations in battery system development. High-performance components of any kind demand higher fidelity and faster results. |
Integration Friction
It’s not always easy to connect a legacy solver to modern software platforms or user interfaces. Constant work is needed to maintain compatibility and seamless integration. |
There may be a point at which continued investment in a legacy solver will yield diminishing returns. You may begin to evaluate whether it’s worth it to pivot and integrate a purpose-built solution.
Build vs. Buy: A Practical Comparison
| Factor | Build In-House | Buy / Integrate (TMG-SDK) |
| Time to deploy | Multi-year development | Immediate to short-term integration |
| Engineering effort | High, ongoing | Focused on integration only |
| Accuracy and validation | Requires internal validation | Proven and validated solver |
| Maintenance | Continuous internal burden | Managed externally |
| Scalability | Limited by internal resources | Designed for performance and growth |
| Integration | Fully custom, but complex | Built for embedding into applications |
When Should You Buy a Thermal Simulation Solution?
Some teams may find a more direct path to value in the integration of a proven, purpose-built thermal simulation solution. A mature option from an external partner allows your business to incorporate accurate thermal modeling without the overhead and upkeep of a DIY solution.
Still, it’s an investment in an outside product. When does it make the most sense? You are likely to save on long-term costs and improve your product when:
- Thermal simulation is not a core differentiator for your business.
- Timelines do not allow for the long development cycles associated with creation and maintenance of internal tools.
- Existing in-house tools cannot support current or upcoming product requirements.
- Teams need to scale capabilities but don’t want to expand headcount or devote a full team to maintaining the thermal solver.
Solutions like the self-contained TMG™ SDK from MayaHTT allow organizations to embed a thermal simulation solution already trusted by thousands of major engineering and manufacturing organizations directly into their platforms.
Buying a tool accelerates deployment of thermal capabilities and improves the consistency and reliability of results. The TMG SDK integrates seamlessly with existing engineering environments and provides a better user experience for internal teams or end users.
If you have a team starting from scratch or expanding into thermal simulation, this represents a smooth next step that will advance your system capabilities immediately.
How to Choose the Best Thermal Simulation Software
Your decision — to build or buy a thermal solver — ultimately comes down to focus.
If you build a solver, it offers your team complete control, but it also requires sustained investment and long timelines.
Teams that buy or integrate proven solutions shift that burden away from internal teams and allow them to focus their efforts on product innovation.
There’s no one right answer, but for most organizations, the goal is not to become experts in solver development. The goal is to use thermal simulation to design better products, faster.
If you would like to discuss your options and find the best path forward for your company, reach out to Maya HTT for information on the TMG SDK. We’re happy to show you how it works so you can evaluate whether it’s a good fit for your needs.
