This page helps technical evaluators assess how MDriven supports application evolution and what published evidence is currently available for scale planning.
Architecture and scalability assessment
MDriven keeps business logic, data structures, and UI in a shared model. The model is the maintained description of the application rather than documentation that must be manually kept aligned with implementation. This lets domain experts contribute to the description of the system and makes the business logic available when team members change.
For example, a team can model an Invoice and its relationships, define the rules and workflows that apply to it, and use that model as the basis for the application rather than distributing those rules across separate handwritten artifacts.
Evolving an application
MDriven separates the business description from specific programming languages and development tools. The documented evolution path is to update the model and let the platform calculate the required database schema changes.
MDriven features and benefits describes this approach as adapting a modeled system to new requirements without redesigning its core structure.
This is an evolution mechanism, not a capacity guarantee. You should evaluate each production change against your own data volume, integration behavior, availability requirements, and deployment environment.
Deployment and integration scope
MDriven Server is the deployment and hosting solution described for applications built with MDriven Framework and Turnkey. It supports cloud and on-premises deployment.
For a legacy-modernization scenario, this means you can keep an existing system in place while introducing modeled functionality around the business area being changed.
Published example of model complexity
This example demonstrates use with a large modeled domain. It does not establish a general performance limit, a transaction-throughput result, or a supported number of concurrent users.
What is not published
MDriven does not currently publish the following measurements as product-wide guarantees:
- Transactions per second or other throughput benchmarks.
- Maximum concurrent users.
- Latency targets or response-time benchmarks.
- A maximum number of modeled classes, associations, or schema objects.
- Comparative benchmarks for teams growing from 15 to 50 or more engineers.
- A documented rewrite or migration case study with measured before-and-after results.
Do not use the published model-size example to infer capacity for your workload. Plan a proof of concept that represents your data shape, workflows, integrations, deployment topology, and expected load. Record the acceptance criteria before testing; for example, measure response time for an invoice workflow while the expected number of users performs the same operation.