Proprietary vs Open-Source Technologies: Strategy, Freedom, and Dependency

Hello, I’m Igor Brandão, developer and founder of IBTI. Throughout my experience building systems and SaaS platforms, I’ve learned that the choice between proprietary and open-source technologies goes far beyond technical preference — it directly impacts cost, scalability, and long-term autonomy. This article comes from that practical and strategic perspective.

The debate between proprietary and open-source technologies is not ideological.

It is strategic.

This is not about “Microsoft vs Linux” or “.NET vs JavaScript.”
It’s about control, cost, scalability, and long-term autonomy.

The real question is:

Does your company control its technology — or is it controlled by it?

What Is Really at Stake?

When choosing a technology stack, we are defining:

🔒 Level of vendor dependency
💰 Future cost structure
🔐 Auditability and security capacity
🚀 Innovation speed
🧩 Freedom to evolve

Technical decisions directly impact business strategy.

Case 1 — Linux Servers vs Windows Server

Linux (Open-Source)

Pros

• No licensing cost

• High stability

• Dominant standard in cloud environments

• Strong integration with Docker and Kubernetes

• Ideal for Infrastructure as Code

Cons

• Requires a skilled technical team

• Steeper learning curve for non-technical environments

Windows Server (Proprietary)

Pros

• Strong integration with the Microsoft ecosystem

• User-friendly graphical interface

• Facilitates legacy enterprise environments

Cons

• Licensing per server/core

• Version dependency

• Less flexibility in cloud-native environments

Today, most cloud infrastructure runs on Linux.
Not ideology — market standard.

Practical Use Case

Imagine a growing SaaS startup beginning with a small cloud setup.

Initially, the company chose Windows Server because the team is already familiar with the Microsoft ecosystem. Deployment is fast and functional.

As the product grows:

• Horizontal scaling becomes necessary

• Containers are required

• CI/CD pipelines grow more complex

• Licensing costs per instance increase

Each new machine adds licensing cost.

After migrating to a Linux-based architecture:

• Cost per instance decreases

• Docker and Kubernetes integration becomes natural

• Infrastructure as Code becomes simpler

• The environment aligns with cloud-native standards

The result is not just savings.

It’s flexibility.

In scalable SaaS environments, operating system choice becomes a strategic decision — not a technical detail.

Case 2 — PostgreSQL/MySQL vs SQL Server

PostgreSQL / MySQL (Open-Source)

Pros

• No licensing cost

• High performance and scalability

• Active community

• Advanced extensibility

• Easy portability

Cons

• Requires technical administration

• Support depends on the team or partner

SQL Server (Proprietary)

Pros

• Robust integrated tools

• Strong integration with .NET

• Official enterprise support

Cons

• Expensive per-core licensing

• Scalability can become financially heavy

• Ecosystem lock-in

In scalable systems, per-core licensing can become a financial bottleneck.

Practical Use Case (Industrial Company)

Imagine an industrial company modernizing its internal operational management system — inventory control, logistics, reporting, and order management.

Initially, SQL Server makes sense due to the existing Microsoft infrastructure.

Over time:

• Data volume grows significantly

• External integrations increase

• Analytical reporting expands

• Additional environments are required (staging, testing, disaster recovery)

Each new environment requires additional licenses.

Each capacity increase impacts per-core cost.

By migrating to PostgreSQL:

• Licensing costs are eliminated

• Environments can be replicated freely

• Cloud portability improves

• Full control over the data structure is maintained

The shift is not just technical.

It allows the IT budget to focus on innovation rather than on maintaining licenses.

Case 3 — React / TypeScript / JavaScript vs .NET

Open Ecosystem (React / Node / TypeScript)

Pros

• Massive community

• Rapid evolution

• High architectural flexibility

• Multi-cloud friendly

• Industry standard in startups and SaaS

Cons

• Requires architectural discipline

• Standards can vary without governance

.NET Ecosystem

Pros

• Strong standardization

• Mature tooling

• High productivity for Microsoft-oriented teams

Cons

• Strong ecosystem dependency

• Less diversity outside the Microsoft environment

This is not about quality.

It’s about the level of dependency and future flexibility.

Practical Use Case — Educational App

A company builds a multi-platform application (web + mobile), including:

• Authentication

• Video streaming

• Real-time chat

• Push notifications

• Admin panel

• Payment and third-party API integrations

The app must handle gradual growth and seasonal spikes.

Scenario 1 — .NET + Windows Server

Architecture:

• ASP.NET backend

• SQL Server

• Windows Server hosting

• Azure ecosystem integration

Infrastructure:

• 2 Windows VMs (App + API)

• 1 SQL Server instance

• Windows Server licensing

• SQL Server per-core licensing

As the app scales:

• More instances → more licenses

• More cores → more cost

• Horizontal scaling increases proportional licensing expense

Deployment:

• Azure DevOps

• Often Azure-centric services

• Potential vendor lock-in

Scenario 2 — Open Stack (React + Node + PostgreSQL + Linux)

Architecture:

• React + TypeScript frontend

• Node.js backend

• PostgreSQL

• Linux infrastructure

• Docker containers

• Kubernetes orchestration

Infrastructure:

• 2 Linux VMs

• 1 PostgreSQL instance

• No OS license cost

• No database license cost

Scaling:

• Add containers

• Add Kubernetes nodes

• Pay only for compute and storage

Over 3–5 years of growth, accumulated cost differences can be substantial.

Deployment & DevOps

OpenStack enables:

• Infrastructure as Code (Terraform)

• CI/CD with GitHub Actions or GitLab

• True multi-cloud portability

• Container portability

Proprietary stacks tend to:

• Centralize pipelines within the vendor ecosystem

• Encourage vendor-specific services

Developer Availability

The JavaScript/TypeScript ecosystem has the largest global developer base
(Source: Stack Overflow Developer Survey 2025)

React dominates web frontend.
Node.js is widely adopted.

Availability affects hiring cost and team scalability.

Case 4 — Custom Development with AI vs Low-Code (Mendix and similar)

Low-Code Platforms (Proprietary)

Pros

• Fast initial delivery

• Lower initial technical demand

• Rapid prototyping

Cons

• Severe lock-in

• Architectural limitations

• Vendor-controlled scalability

• Growing costs

• Limited deep customization

Custom Development + AI (Open Stack)

Pros

• Full architectural control

• True scalability

• No structural lock-in

• Complete technical governance

• AI dramatically reduces development time

Cons

• Requires a skilled technical team

• Higher architectural responsibility

AI has significantly reduced the historical speed advantage of low-code.

Today, it’s possible to move fast without sacrificing control.

Real Experience

In a real project I followed, a client initially chose a low-code platform (Mendix) to accelerate internal application development.

At first:

• Fast delivery cycles

• Visual interface

• Reduced need for specialized engineering

But as systems evolved and became business-critical:

• Structured debugging became difficult

• Log visibility and monitoring were limited

• Performance bottlenecks were harder to diagnose

• Advanced custom flows were restricted

• Deep integrations were constrained

As the system became core to the business, the lack of technical control created operational risk.

With AI reducing custom development time, the client made a strategic decision to rebuild key systems using open-stack custom architecture with AI-assisted development.

The result:

• Full log control

• Deep debugging capability

• Complex rule customization

• Modular and scalable architecture

• Reduced technological lock-in

The decision was not ideological.

It was strategic.

When systems become core to the business, control outweighs initial speed.

Case 5 — Open-Source Infrastructure (Linux / OpenStack) vs Google Cloud / Amazon Web Services (AWS)

Infrastructure is one of the most structural decisions any digital company makes.

We are not simply comparing providers here.
We are comparing control models.

🔹 Open-Source Infrastructure (Linux + OpenStack)

What it is

• Linux servers

• Self-managed virtualization

• OpenStack for orchestration

• Full control over networking, storage, and compute

• Can run on-premise or in a contracted data center

Pros

✔ Full architectural control
✔ No vendor-specific lock-in
✔ Deep customization of networking and security
✔ Predictable costs after initial investment
✔ Ideal for regulatory environments and data sovereignty

Cons

✖ High initial investment (CAPEX)
✖ Requires a specialized infrastructure team
✖ Full responsibility for security and availability
✖ Scalability limited by installed physical capacity

🔹 Google Cloud / Amazon Web Services (AWS)

What it is

Infrastructure as a Service (IaaS) and Platform as a Service (PaaS), including:

• On-demand compute

• Managed databases

• Elastic storage

• AI services

• Serverless services

• Global CDN

Pros

✔ Instant scalability
✔ Automatic elasticity
✔ Managed services
✔ Strong SLA
✔ Simplified global expansion

Cons

✖ Structural vendor dependency
✖ Variable and sometimes unpredictable costs
✖ Increasing billing complexity
✖ Strong lock-in through proprietary services (Lambda, BigQuery, DynamoDB, etc.)

Practical Use Case — Real Experience with AWS vs Open-Source Infrastructure

In a real project I was involved in, a robotics technology company maintained a proprietary Linux package repository (PPA).

The business model required:

• Frequent build generation

• Compilation for multiple Linux distributions

• Architecture variations (x86_64, ARM)

• Automated CI/CD build pipelines

• Continuous versioning

The strategy adopted was to use AWS to:

• Provision on-demand instances

• Run build pipelines

• Store artifacts

• Distribute packages

Initially, the solution made sense:

✔ Fast provisioning
✔ Automatic scalability
✔ Seamless pipeline integration
✔ No need for internal infrastructure

The problem emerged at scale

As the product grew:

• The number of daily builds increased dramatically

• Frequent releases required full recompilation

• Parallel builds were automatically triggered

• Testing and validation consumed intensive computing resources

The billing model is based on:

• Instance time

• CPU usage

• Temporary storage

• Data transfer

began scaling alongside operations.

The result

The monthly AWS bill exceeded $100,000 solely for build generation.

Important clarification:

This was not production infrastructure.
It was not customer-facing.
It was exclusively for the build pipeline.

The cost was directly tied to the on-demand elasticity billing model.

Technical review

After reviewing the architecture, it became clear that:

• The workload was highly predictable

• Build volume was constant

• The workload was repetitive and automated

• It did not require global elasticity

In other words:

It was an ideal case for dedicated infrastructure.

Open Infrastructure Alternative (Linux + OpenStack)

An architecture based on:

• Dedicated Linux cluster

• OpenStack for internal provisioning

• Dedicated build nodes

• Orchestration via Kubernetes or dedicated runners

• Internal artifact storage

would enable:

✔ Predictable fixed costs
✔ Elimination of per-minute CPU billing
✔ Maximum utilization of provisioned hardware
✔ Independence from variable billing models

The initial investment (CAPEX) would be higher.

However, recurring operational costs could drop significantly — and in some scenarios, the marginal cost per build could approach zero after infrastructure amortization.

The Strategic Lesson

Cloud is excellent for:

• Unpredictable workloads

• Early-stage growth

• MVP development

• True elastic scaling

But not every workload requires infinite elasticity.

Predictable, compute-intensive workloads can become financially inefficient under purely elastic pricing models.

The Core Point

The problem was not AWS.

The issue was misalignment between:

The billing model
and
The workload model

Technology must serve strategy.

When variable costs begin scaling disproportionately relative to generated value, it is a clear sign that the architecture needs to be reassessed.

Vendor Lock-In: The Silent Risk

Vendor lock-in is not an immediate technical problem.

It is a structural risk that emerges over time.

It happens when architecture, data, and processes become dependent on vendor-exclusive characteristics.

Exclusive APIs

Vendor-specific APIs and SDKs:

• Have no direct equivalent elsewhere

• Require specific integration formats

• Depend on internal authentication flows

Short-term acceleration, long-term dependency.

Limited Data Export

Migration is not just copying data.

It requires preserving:

• Integrity

• Relationships

• Indexes (primary, secondary, composite, unique, full-text)

• Constraints

• Triggers

• History

If formats are controlled by the vendor, autonomy is reduced.

Migration Costs

Lock-in appears as:

• Rewriting cost

• Infrastructure restructuring

• Pipeline reconfiguration

• Team retraining

• Operational risk

Companies often stay because it’s not ideal, but because leaving is more expensive.

Roadmap Dependency

With proprietary systems:

• Vendor defines roadmap

• Vendor controls updates

• Innovation is limited by external priorities

Technology should expand strategic freedom — not restrict it.

Security: Myth or Advantage?

Closed source does not mean more secure.

Open-source allows:

• Public audit

• Faster community fixes

• Full transparency

Security depends more on governance than licensing.

When Proprietary Makes Sense

It can make sense when:

• No internal technical team

• SLA outsourcing is critical

• Legacy integration is dominant

• Product development is not the focus

Context matters.

When Open-Source Is Strategic

Open-source tends to be advantageous when:

• Building SaaS

• Scaling globally

• Seeking technological independence

• Operating in regulated environments

• Requiring rapid evolution

Here, autonomy becomes a competitive advantage.

Where IBTI Stands

At IBTI, we prioritize open-source technologies because we believe in:

• Technological autonomy

• Auditability

• Sustainable scalability

• Architectural freedom

It’s not ideology.

It’s a long-term strategy.

As a developer, I prefer building systems that clients truly own — not merely rent under restrictive contracts.

Conclusion

The choice between proprietary and open-source is not purely technical.

It is strategic.

The question is not which is “better.”

It is:

Are you buying short-term convenience — or building long-term independence?

Technology should serve your business.

Never imprison it.

🚀 Build Technology with Freedom and Long-Term Vision

Choosing the right stack is not just a technical decision — it’s strategic.

At IBTI, we build systems using modern architectures, open-source stacks and strong engineering practices to ensure:

• Technological autonomy

• Sustainable scalability

• Reduced lock-in

• Governance and security

• Continuous product evolution

If you want to build or modernize your software with independence, performance, and real growth in mind:

👉 Learn more about our software development services:
https://ibti.tech/en/services/software-development/

Technology should empower your business — not limit it.