Enterprise software is entering a new phase. After years of adopting proprietary low-code platforms to accelerate application development, many organisations are rethinking the long-term tradeoffs of those platforms.

Open-source low-code adoption is increasing because enterprises want predictable costs, ownership of application source code, and the ability to integrate private AI systems within their own infrastructure.

In 2026, the discussion is no longer just about building applications faster. It is increasingly about who controls the infrastructure, the data, and the future direction of the applications being built.

Platforms like Mendix, OutSystems, and Power Apps helped enterprises modernise internal operations over the past decade. Organisations used them to build approval workflows, operational dashboards, customer support tools, inventory management systems, and countless other internal applications.

Today, many of those same organisations are evaluating a different approach: moving from proprietary SaaS-based low-code platforms to self-hosted open-source alternatives.

This shift is not a rejection of low-code development. It is a response to changing enterprise priorities around total cost of ownership, vendor independence, AI governance, and data sovereignty.

The challenge many enterprises encounter is that the economics of proprietary low-code platforms often change as adoption grows.

A platform that appears affordable for a single department can become significantly more expensive once hundreds of users, dozens of applications, and multiple business units are involved.

Common pricing models include:

• Per-user licensing
• Per-application pricing
• Consumption-based billing
• Premium connector fees
• Enterprise feature add-ons

As a result, the organisations receiving the most value from the platform are frequently the ones experiencing the largest cost increases.

Many IT leaders now describe this phenomenon as a “success tax” because costs rise alongside successful adoption.

Most enterprises are not abandoning low-code. They are becoming more selective about where low-code platforms fit into their long-term technology strategy.

Several trends are contributing to renewed interest in open-source alternatives:

• Growth of self-hosted AI infrastructure and private LLM deployments
• Increased focus on digital sovereignty and data residency requirements
• Pressure to reduce SaaS sprawl and unpredictable software costs
• Platform engineering initiatives that prioritise standardisation
• Demand for tools that integrate with existing development workflows

The enterprise low-code shift is primarily about ownership and control, not dissatisfaction with low-code development itself.

Organisations still want rapid application delivery. The difference is that they increasingly want that speed without sacrificing portability, flexibility, or infrastructure independence.

Several structural factors are accelerating enterprise interest in open-source low-code platforms.

For many organisations, cost predictability has become one of the strongest arguments for evaluating open-source alternatives.

Proprietary platforms often charge based on user counts, application counts, workflow executions, API usage, or consumption metrics. While these pricing models can work well initially, they frequently become more difficult to justify as adoption expands.

Open-source commercial models generally take a different approach.

Organisations can self-host the platform and pay primarily for support, managed hosting, enterprise features, or service-level agreements rather than paying for every additional user.

Open-source low-code platforms separate platform growth from per-seat licensing growth.

This distinction is particularly valuable for enterprises planning to scale internal tooling across departments and regions.

ToolJet is one example of this model. Organisations can self-host ToolJet using its open-source edition while also choosing managed cloud deployments when operational convenience is a priority.

The result is greater flexibility in balancing cost, control, and operational responsibility.

Historically, many enterprises viewed low-code platforms as productivity tools.

Today, they are increasingly viewed as application platforms.

That distinction matters because application platforms become part of an organisation’s long-term technology stack.

When critical workflows depend on a proprietary platform, organisations become dependent on that vendor’s pricing decisions, hosting model, roadmap priorities, and feature availability.

Unlike proprietary platforms that compile applications into black boxes, open-source low-code tools give enterprises full access to application source code and data schemas.

This allows teams to:

• Inspect application logic
• Export configurations
• Modify platform behavior
• Integrate custom functionality
• Migrate infrastructure when necessary

Source code ownership gives enterprises an exit strategy.

That flexibility becomes increasingly important as applications evolve from departmental tools into business-critical systems.

The rise of platform engineering has also changed expectations around application development.

Modern development teams expect tools to integrate seamlessly with existing engineering workflows rather than operating as isolated ecosystems.

Developers increasingly expect:

• Git version control
• CI/CD compatibility
• Infrastructure-as-Code alignment
• Local development workflows
• API-first architectures
• Support for JavaScript, Python, and SQL

Developers increasingly prefer low-code platforms that integrate with existing engineering workflows rather than replacing them.

This is one reason open-source low-code platforms continue to gain traction among technical teams.

ToolJet, for example, supports Git synchronisation, allowing teams to manage applications through familiar software development processes. Developers can combine visual development with JavaScript, SQL queries, APIs, and custom business logic without sacrificing engineering standards.

A pattern we frequently see is that low-code platforms start as solutions for a single team and gradually become systems that multiple departments rely on every day.

A support dashboard becomes an operational platform. A workflow app becomes part of a core business process.

As adoption grows, the conversation changes.

Teams begin asking:

• How will costs change as usage expands?
• What happens if pricing changes?
• Can applications be migrated if requirements evolve?
• How well does the platform fit existing engineering workflows?
• Where does application data reside?

The challenge is not that proprietary low-code platforms stop delivering value. The challenge is that organisations start evaluating them as long-term application platforms rather than productivity tools.

That shift is one reason many enterprises are exploring open-source alternatives.

At ToolJet, conversations with engineering leaders, platform teams, and IT departments increasingly revolve around three recurring themes.

The first is cost predictability. Many organisations that initially adopted proprietary low-code platforms for a small number of use cases are now supporting dozens or even hundreds of internal applications. As adoption grows, licensing models often receive greater scrutiny.

The second is AI governance. Teams building AI-powered workflows need more control over model deployment, data handling, and compliance than vendor-hosted environments can always provide.

The third is workflow compatibility. Organisations increasingly want low-code platforms that fit naturally into existing Git repositories, CI/CD pipelines, cloud infrastructure, and platform engineering initiatives.

Enterprise buyers increasingly evaluate low-code platforms through the lens of platform strategy rather than application-building features alone.

The rise of enterprise AI is creating new requirements for internal application platforms.

Organisations are no longer evaluating only how applications are built. They are evaluating where data flows, where models run, and who controls the infrastructure.

Highly regulated industries face strict requirements around data processing and governance.

Organisations operating in finance, healthcare, government, and critical infrastructure environments frequently need to comply with standards and frameworks such as:

• GDPR
• SOC 2
• Internal governance policies
• Data residency requirements

In these environments, routing sensitive operational data through external vendor systems may introduce compliance, security, or governance concerns.

Before the rise of enterprise AI, infrastructure decisions were largely driven by cost, scalability, and operational efficiency.

Today, organisations must also decide where AI models run and where inference data is processed.

Organisations deploying private AI infrastructure often prefer low-code platforms that can operate within the same security boundary as their AI systems.

This requirement is accelerating interest in self-hosted application platforms.

A growing number of enterprises are deploying models such as Llama within private cloud environments or on-premises infrastructure.

These organisations need internal tools capable of securely interacting with those models.

Open-source low-code platforms can connect directly to self-hosted LLM endpoints while keeping application data inside the organisation’s infrastructure boundary.

This architecture gives organisations greater control over:

• Security
• Compliance
• Auditing
• Data residency
• AI governance

Rather than being limited to vendor-specific AI offerings, teams can select whichever models best align with their operational requirements.

Platforms such as Power Apps, Mendix, OutSystems, and Retool helped establish the modern low-code category and continue to power thousands of internal applications.

However, as low-code adoption expands, many organisations begin evaluating broader architectural considerations.

Common drivers include:

The ability to deploy applications in private cloud, on-premises, or regulated environments.

More visibility into how costs scale as users, applications, and workflows grow.

Greater control over application logic, configurations, and long-term portability.

The freedom to connect internal tools to self-hosted models and private AI infrastructure.

For many enterprises, the decision is no longer low-code versus traditional development. It is increasingly a choice between managed convenience and long-term control.

ToolJet can be deployed on-premises or within private cloud environments.

Because it connects to REST APIs, GraphQL endpoints, databases, and internal services, organisations can integrate self-hosted AI models without routing requests through ToolJet-managed infrastructure.

This allows enterprises to build AI-powered internal applications while maintaining control over data flows and deployment architecture.

Not all open-source low-code platforms provide the same level of flexibility or enterprise readiness.

When evaluating solutions, IT leaders should assess five key areas.

Can the platform run:

• On-premises?
• In a private cloud?
• Across multiple clouds?
• In air-gapped environments?

Can applications and platform components be inspected, modified, and exported?

Does the platform support:

• Git version control
• CI/CD workflows
• JavaScript
• Python
• SQL
• API-first development

Does it provide:

• RBAC
• SSO
• SAML
• Audit logs
• Data residency controls

Can it connect to:

• OpenAI
• Anthropic
• Azure OpenAI
• Self-hosted Llama deployments
• Internal AI services

The best open-source low-code platforms combine visual development with modern software engineering practices.

ToolJet checks every box on this list. Start your free trial and evaluate it yourself

Criteria	Proprietary Low-Code	Open-Source Low-Code
Licensing	Per-user, per-app, or consumption pricing	Open-core; pay for support or cloud hosting
Deployment	Vendor SaaS environments	Self-hosted, on-premises, private cloud, air-gapped
Source Code Access	Proprietary and typically unavailable	Full source code access
Data Ownership	Vendor-managed environment	Enterprise-controlled environment
Exit Strategy	Vendor dependent	Applications remain under enterprise control
AI Integration	Vendor-provided AI ecosystem	Connect to any local or cloud-based LLM
Developer Workflow	Platform-specific tooling	Git, CI/CD, JavaScript, Python, SQL
Example Platforms	Mendix, OutSystems, Power Apps	ToolJet, Appsmith, Budibase

The primary difference between proprietary and open-source low-code platforms is ownership. Open-source platforms prioritise control and portability, while proprietary platforms prioritise vendor-managed convenience.

The enterprise conversation around low-code has matured.

A decade ago, the primary question was whether low-code could accelerate software delivery. In 2026, the question is how organisations can accelerate delivery while maintaining ownership of applications, infrastructure, and data.

The resurgence of open-source low-code is being driven by three forces: cost predictability, source code ownership, and private AI infrastructure.

Organisations evaluating their next generation of internal tooling should begin with a simple audit:

1. Inventory applications currently built on proprietary low-code platforms.
2. Identify one or two low-risk internal tools suitable for an open-source pilot.
3. Evaluate platforms such as ToolJet based on deployment flexibility, developer experience, security controls, and AI readiness.

The goal is not to replace low-code.

The goal is to ensure that low-code remains sustainable as organisations scale, modernise, and adopt AI-driven workflows.