Should You Build Workflow Automation In-House or Buy an Existing Solution?

Adding workflow automation to your SaaS product used to mean committing months of engineering time to building a workflow engine, designing an editor, and connecting everything to your existing application.

AI-assisted development has changed that calculation. As you can imagine, this is something we have really paid attention to at Embed, as our fundamental business proposition is an embedded workflow builder.

Today, developers can use AI coding tools to build functional workflow prototypes much faster than before. A basic system with triggers, actions, and conditional logic might take weeks rather than months.

But there’s an important distinction between building a workflow system and operating one reliably for thousands of users. And trust us, we know what it’s like to operate one!

For SaaS product managers, the build-versus-buy decision increasingly comes down to a different question: Do you want your engineering team to maintain a workflow automation platform indefinitely?

AI Has Made Building Workflow Automation Easier

A few years ago, developing workflow automation in-house was a substantial undertaking, even for a relatively simple implementation.

You needed engineers to build the execution engine, create a user interface, handle data passing between steps, and implement the infrastructure required to run workflows reliably. This problem is why I started Embed Workflow in the first place.

AI coding assistants have significantly reduced the effort involved in writing much of this code. For example, a small development team can now move considerably faster when implementing:

• A visual workflow editor with drag-and-drop steps.
• Basic triggers and actions.
• Conditional branching and simple execution logic.
• Connections to internal application APIs.
• Workflow configuration and management screens.
• Basic execution logs and error reporting.

For a SaaS product that needs only a handful of predefined automations, building in-house may now be the most economical option.

If your customers simply need to send an email when a record changes status, you probably don’t need a complete workflow automation platform.

However, the calculation changes when customers need to create and manage their own automations.

The First Version Is Usually the Easiest Part

Consider a SaaS company developing project management software. Its product manager wants customers to create workflows such as:

Task Completed → Notify Manager → Update Project Status

The development team uses AI-assisted coding tools to build an initial workflow builder. Within a few weeks, customers can create simple automations inside the application.

The feature works, and the initial development cost seems reasonable. Then customers start asking for more.

One customer wants workflows that wait three days before continuing. Another needs conditional branches. A third wants to connect their Slack account. Someone else wants a failed workflow to retry automatically.

Before long, the team is no longer maintaining a small product feature. It’s maintaining a separate automation platform.

And unlike the initial development work, these responsibilities don’t disappear once the feature launches.

The Ongoing Costs of Maintaining Workflow Automation

There are several areas where workflow automation becomes significantly more complicated after the initial implementation.

1. Maintaining Third-Party App Connections

This is one of the most easily underestimated costs.

Suppose your customers want to connect their workflows to Slack, Google Sheets, HubSpot, Salesforce, and Microsoft Teams.

Each integration introduces its own requirements.

Your engineering team must handle authentication, API requests, permissions, rate limits, error responses, and data formats.

And these integrations aren’t static.

Third-party providers regularly change their APIs, introduce new authentication requirements, deprecate endpoints, and modify available functionality.

An integration that works perfectly today may require engineering attention six months from now.

AI can help developers write integration code faster. It cannot eliminate the responsibility of keeping those integrations operational.

For a SaaS company with dozens of integrations, maintenance can become a permanent engineering responsibility.

At Embed, we have a large library of integrations that are maintained for our clients, so they don’t have to worry about them.

2. Handling Workflow Failures

Simple workflows are relatively easy to execute. Reliable workflows are considerably harder. Imagine a customer creates this automation:

New Customer → Wait 7 Days → Check Account Status → Send Follow-Up Email

What happens if your server restarts during the seven-day wait? What if the email provider is temporarily unavailable? What if the workflow executes twice and sends duplicate emails? What if the customer changes the workflow while an earlier execution is still running?

You get the idea – a lot can go wrong.

A production workflow engine needs mechanisms for handling these situations. That includes persistent execution state, retries, idempotency, execution history, and recovery from failures. These requirements aren’t especially visible in a product demo, but they determine whether customers can depend on the feature.

3. Supporting Increasingly Complex Workflows

Customers rarely stop at simple trigger-action automations. Once they understand what’s possible, they begin requesting more sophisticated functionality.

They may want workflows that:

• Branch into different paths depending on conditions.
• Wait hours, days, or weeks between actions.
• Process multiple records through loops.
• Call external APIs and process their responses.
• Pass data between different workflow steps.
• Execute different actions depending on previous results.

Each capability adds complexity to the execution engine, user interface, and testing process. The difficulty isn’t necessarily implementing any individual feature. It’s ensuring that every feature works reliably alongside the others.

4. Managing Multiple Customers and Users

An internal automation tool can operate under relatively simple assumptions. An embedded workflow builder serving hundreds of SaaS customers cannot.

Each customer needs access to their own workflows, credentials, execution history, and configuration. Depending on your product, you may also need user permissions, account-level limits, usage tracking, and separate environments.

A workflow created by one customer must never accidentally access another customer’s data or connected applications. These requirements introduce security and operational responsibilities that extend far beyond the workflow editor itself.

5. Maintaining the Workflow Builder Interface

The visual editor also requires ongoing development. Customers need to configure actions, select data fields, understand errors, and modify existing workflows without breaking them.

As new actions and integrations are introduced, the editor needs to accommodate them. AI-assisted coding can accelerate interface development, but someone still needs to design, test, and maintain the overall user experience.

For SaaS companies, this work competes directly with improvements to their core product.

When Does Building In-House Make Sense?

Buying an existing workflow automation solution isn’t always the right decision. There are several situations where developing your own system may be preferable.

Your automation requirements are limited.

If you only need a few predefined automations, a complete workflow platform may introduce unnecessary cost and complexity.

You don’t need third-party integrations.

A system that only connects actions within your own application is substantially easier to maintain than one that communicates with numerous external services.

The key is distinguishing between a feature that happens to use automation and a product that needs a general-purpose workflow engine.

When Does Buying an Existing Solution Make More Sense?

An existing workflow automation platform becomes more attractive when your requirements extend beyond a few internal processes.

For example, your SaaS might need to let customers:

• Build their own workflows through a visual interface.
• Connect workflows to external applications.
• Create conditional branches and multi-step processes.
• Run workflows that continue over long periods.
• Monitor execution history and troubleshoot failures.
• Manage workflows across multiple users or accounts.

At this point, you’re effectively adding an automation product inside your existing SaaS.

Building all of this internally means taking responsibility for an additional software platform, including its infrastructure, reliability, security, and integrations.

Buying an embedded solution lets your engineering team concentrate on connecting automation to your product’s existing data and functionality.

But Can’t You Just Build This With AI?

If AI can generate much of the code required to build a workflow engine, why pay for an existing solution? The answer depends on what you’re actually purchasing.

If a vendor only provides a basic workflow editor and simple trigger-action functionality, AI-assisted development may substantially weaken its value proposition.

But a mature workflow platform provides more than source code. It provides an existing execution architecture, established operational behavior, maintained integrations, and infrastructure designed around workflows.

AI can reduce the cost of developing these systems. It doesn’t automatically provide years of production testing or remove the need for ongoing engineering ownership.

And while AI can help identify and repair problems, your team still needs to detect failures, validate fixes, deploy changes, and support affected customers.

The cost of writing software is decreasing. The cost of being responsible for that software hasn’t disappeared.

A Third Option: Embedded Workflow Automation

There’s an important distinction between purchasing an external automation tool and embedding automation directly into your SaaS.

External platforms can allow customers to connect your application to other services, but they often require users to leave your product to configure and manage their workflows.

An embedded workflow solution takes a different approach.

For example, here at Embed Workflow we provide a workflow builder that SaaS companies can integrate directly into their existing applications. Customers can create and manage automations without switching to a separate automation platform. That means they never actually leave your application – or even know that they’re using a third-party builder at all.

The SaaS company retains control over how the functionality appears inside its product, while the underlying workflow platform handles capabilities such as execution, branching, delays, and workflow management.

This doesn’t eliminate integration work entirely. Your team still needs to define the triggers, actions, and data that your application exposes. However, it removes much of the need to build and maintain the underlying automation infrastructure yourself.

For product managers, that changes the scope of the project from developing an entire automation platform to integrating an existing capability.

How to Make the Build-or-Buy Decision

Before committing to either approach, product managers should answer five questions.

1. How complex will our customers’ workflows become?

Don’t evaluate only the workflows needed at launch. Consider what customers are likely to request after six or twelve months.

2. How many external applications will we need to support?

Every additional integration introduces development and maintenance obligations.

3. Who will maintain the system after launch?

Estimate the engineering time required for monitoring, troubleshooting, security updates, integration changes, and customer support.

4. Is workflow automation a competitive differentiator or a supporting feature?

If your product’s main advantage is its specialized industry functionality, maintaining a general-purpose workflow engine may not be the best use of engineering resources.

5. What is the total cost over three years?

Compare more than initial development costs. Include ongoing engineering time, infrastructure, incident response, integration maintenance, and the opportunity cost of delaying other product improvements. A solution that costs less to launch may ultimately cost considerably more to operate.

Conclusion: AI Changes the Calculation, but Not the Entire Decision

AI-assisted development has made building workflow automation more accessible than ever. For SaaS companies with straightforward requirements, developing a small automation system internally can be a sensible choice.

But when customers need configurable, multi-step workflows, third-party integrations, and reliable long-running executions, the challenge extends well beyond writing the initial code.

The central question for product managers is no longer simply whether their team can build workflow automation. It’s whether maintaining that system is the best use of their engineering resources over the next several years.

If your SaaS needs customer-facing automation without taking on the responsibility of developing a complete workflow platform, Embed Workflow offers an alternative.

You can add a customizable workflow builder directly to your application while keeping your development team focused on the features that differentiate your product.