For many developer-led teams, the ‘build vs buy IDP system’ decision comes down to control. Building in-house feels safer, right? You own the code, you can tweak every function and you’re not locked into vendor roadmaps.
But intelligent document processing isn’t a one-and-done project. It’s a complex, evolving capability that requires constant tuning to keep up with accuracy demands, document variability and integration needs.
What you need is a checklist to evaluate whether to build or buy. No sales talk, just practical criteria that matter to your business.
Building in-house can give you more control over architecture and data flows, but it also comes with specific challenges that are often overlooked. This checklist focuses on those challenges so you can make a well-informed decision based on what will work best for your team.
1. Scalability and future-proofing
The challenge: An IDP system that works today may struggle tomorrow as document volumes, formats and workflows increase in complexity.
Building in-house:
- Your team must design and maintain infrastructure for scaling.
- Performance tuning and latency management sit entirely on your shoulders.
- Each new document type adds more development effort.
Buying a solution:
- Vendors of the most reliable intelligent document processing solutions have already optimized for enterprise scale.
- Features like adaptive processing and high-volume resilience are (or should be) included out of the box.
Key question: Do you want your developers focused on core business projects, or firefighting scaling issues in a custom IDP build?
2. Accuracy and reliability benchmark
The challenge: True automation depends on extremely high accuracy. Anything less than 99% often creates high volumes of exception handling, manual rework and downstream errors.
Building in-house:
- Achieving 90 to 95% accuracy is feasible, but surpassing that requires years of model refinement and ongoing retraining.
- Small changes in document formats can break extraction logic or create unpredictable reliability issues.
Buying a solution:
- Leading vendors offer the most reliable intelligent document processing solutions, built on years of R&D and real-world experience handling edge cases.
- Look out for platforms that feature instant learning and domain-tuned models that improve accuracy without developer intervention.
Key question: Can your team realistically build and maintain accuracy levels that match specialist vendors, while also delivering other critical engineering priorities?
3. Integration complexity
The challenge: IDP doesn’t work in isolation. It needs to plug into your existing ingestion pipelines, validation systems and data exports, seamlessly and reliably.
Building in-house:
- Developers must build and maintain every connector manually.
- Each new integration increases complexity and creates potential failure points.
- Updates to downstream systems often require rework in your IDP build.
Buying a solution:
- The best intelligent document processing software for developers provides REST APIs, prebuilt connectors and flexible configuration options.
- Integration is faster, tested for stability and easier to adapt as your stack evolves.
Key question: Do you want your team investing time in maintaining integrations forever, or deploying automation with minimal engineering overhead?
4. Team expertise and opportunity cost
The challenge: Building and maintaining an IDP system demands specialist skills in machine learning, data science, security and compliance, not just development.
Building in-house:
- Recruiting or reallocating talent for long-term model training and upkeep can drain your team’s bandwidth.
- Every hour spent debugging IDP pipelines is time not spent on core product development or innovation.
- Over time, technical debt grows, making it harder to pivot or upgrade the system.
Buying a solution:
- Shifts the burden of ongoing maintenance, compliance updates and new feature development to a dedicated vendor team.
- Lets your developers focus on building technology that differentiates your business, not infrastructure that already exists elsewhere.
Key question: Does your team have the expertise, and the appetite, to manage the permanent workload of an IDP build?
5. Transparency and control
The challenge: Many teams hesitate to buy IDP software because they fear losing visibility or flexibility over how their data is processed.
Building in-house:
- Gives you full access to the architecture, codebase and data flows.
- But transparency comes at the cost of responsibility: you’re on the hook for every decision, bug fix and model update.
Buying a solution:
- Leading intelligent document recognition software now offers explainable AI, configurable extraction rules and detailed audit trails.
- Modern platforms give developers deep control over workflows without requiring them to rebuild every component from scratch.
Key question: Is complete ownership worth the long-term burden, or can you achieve transparency and control with a proven vendor solution?
Download the full build vs buy IDP playbook
Download our free whitepaper for a clear, structured framework to compare building in-house vs buying an IDP platform. See the trade-offs in accuracy, scaling, integration effort and long-term maintenance so you can make the right call for your team.
Build vs buy: make the right call for your team
Deciding whether to build or buy an IDP system isn’t just a budget question; it’s about scalability, reliability, long-term maintenance and where you want your team’s focus to be. Building gives you full ownership, but it also comes with permanent technical debt, integration headaches and the ongoing challenge of maintaining high accuracy.
Buying from a specialist vendor delivers proven performance, scalability, developer-friendly controls and freedom from a never-ending engineering burden.
Leaning towards buying? Explore how our IDP platform gives developers the control they want with the performance their business needs. It lets you start small and scale easily and helps you see ROI fast. Start your free trial or talk to us today.
