Choose Your Stack

CHOOSE YOUR STACK

Choose the right platform direction before you start building.

A practical decision tool for choosing between Mendix, AWS-native engineering, or a hybrid architecture.

Start assessmentView cost model

Mendix candidate

Business-process apps, workflow delivery, UI-driven solutions.

AWS-native candidate

Event-driven, integration-heavy, performance-sensitive workloads.

Hybrid candidate

Business-facing UI on Mendix, AWS-native for the technical backend.

This is not a platform verdict. It is a workload-fit recommendation based on the answers provided.

Framework

Workload-fit assessment

Answer the twelve workload, capability, and cost-model questions. Scoring is deterministic and shown alongside the result.

0 of 12 answeredNo signal yet
What type of solution is this?

Question 1 of 12

Weight 3

What type of solution is this?

Frame the workload before scoring anything else. The shape of the solution drives most of the platform fit.

How important is the UI?

Question 2 of 12

Weight 2

How important is the UI?

A rich business-facing UI usually pulls toward Mendix. A minimal UI usually pulls toward AWS-native engineering.

How complex is the business logic?

Question 3 of 12

Weight 2

How complex is the business logic?

Process rules tend to fit a low-code delivery model. Branching technical logic tends to fit cloud-native engineering.

What is the expected data or event volume?

Question 4 of 12

Weight 3

What is the expected data or event volume?

Volume shape and predictability are strong runtime signals. Spiky volume usually rewards elastic, usage-based design.

How strict are performance or latency requirements?

Question 5 of 12

Weight 3

How strict are performance or latency requirements?

Strict latency requirements usually need explicit architectural control over runtime, networking, and data paths.

How complex are the integrations?

Question 6 of 12

Weight 3

How complex are the integrations?

Standard integrations fit business-process platforms. Many event flows, retries, and orchestration usually fit cloud-native services.

How business-critical is the solution?

Question 7 of 12

Weight 3

How business-critical is the solution?

Higher criticality demands tighter resilience, observability, and explicit operational ownership.

What matters more: speed or technical flexibility?

Question 8 of 12

Weight 2

What matters more: speed or technical flexibility?

Speed of delivery favours platform standardisation. Long-term technical flexibility favours engineered control.

What capability is strongest in the domain or team?

Question 9 of 12

Weight 2

What capability is strongest in the domain or team?

Capability fit is a delivery reality, not a preference. The platform that the team can run reliably matters.

How important is runtime cost elasticity?

Question 10 of 12

Weight 2

How important is runtime cost elasticity?

Elastic runtime cost matters most when usage is high or spiky. Stable usage often fits a platform-capacity model.

How much platform lock-in is acceptable?

Question 11 of 12

Weight 2

How much platform lock-in is acceptable?

Platform standardisation reduces delivery cost. Custom control supports portability and long-term flexibility.

How deep do observability and testability need to be?

Question 12 of 12

Weight 2

How deep do observability and testability need to be?

Standard platform monitoring is enough for many internal apps. Deep SLOs and tracing usually need engineered runtimes.

Answers stay on this device. Final result may change as more answers are added.

Recommended direction

Workload-fit recommendation

Awaiting answers

Answer the questions to generate a platform-direction recommendation.

0 of 12 answered. The result appears once all questions are complete.

This is not a platform verdict. It is a workload-fit recommendation based on the answers provided.

Cost model

Runtime cost model

Different workload profiles expose different cost models. The goal is not to prove that one platform is always cheaper. The goal is to understand which cost model matches the solution.

AWS-native

Usage-based / elastic / architecture-sensitive

AWS-native costs are usually more usage-based. You pay across the services the architecture consumes, such as compute, queues, event routing, databases, storage, observability, networking, and security services.

What drives cost?

  • Compute duration and request volume
  • Queue, event, and message volume
  • Database reads, writes, storage, and provisioned capacity
  • API, load balancing, and data transfer
  • Logs, metrics, traces, alarms, and retention
  • Networking, NAT, security, KMS, secrets, and WAF
  • High availability and disaster recovery setup

What can make this expensive?

AWS is not automatically cheap. Poor architecture, overlogging, inefficient database access, NAT Gateway misuse, excessive data transfer, or overprovisioning can make AWS materially more expensive.

Mendix

Platform-capacity-based / fast for process apps / licensing-sensitive

Mendix costs are usually more platform, environment, and capacity-oriented. Costs are influenced by runtime resources, database capacity, storage, environments, HA/fallback, scaling, and enterprise licensing agreements.

What drives cost?

  • Application runtime capacity
  • Database size and capacity
  • File storage
  • Number of environments such as dev, test, acceptance, and production
  • High availability and fallback requirements
  • Horizontal scaling and headroom for peaks
  • Enterprise licensing and internal chargeback model

What can make this expensive?

Mendix can still be the better total-cost choice when fast delivery, business involvement, process fit, and available capability reduce build and change costs.

Hybrid boundary

Hybrid: separate UI/process cost model from event/backend cost model. A clean architecture boundary lets each layer use the cost model that matches its workload shape, instead of forcing one model across the full solution.

Key question

The key question is not “which platform is cheaper?” The key question is “which cost model matches this workload?”

Decision caveats

Next checks before final decision

  • This tool supports early architecture direction, not final approval.

  • The recommendation should be validated against enterprise pricing, team capability, security requirements, support model, and platform governance.

  • For business-critical workloads, review resilience, observability, testability, failover, data ownership, and operational support before committing.