The Build-vs-Buy Dilemma in Document Processing

You've got the Unstructured document processing open source library humming along nicely. It's parsing your documents, extracting clean text from those gnarly documents, extracting clean text from those gnarly PDFs, Word Docs, Emails and other documents, and your RAG pipeline is finally working the way you dreamed it would. You installed our package, and with a few lines of code, you're turning unstructured documents into structured, actionable data that can power semantic-search, summarization and many more AI applications that can reason about your content.

We love seeing this. It's exactly why we open-sourced the library in the first place.

But here's what we've learned from seeing thousands of users over the past three years: success has a funny way of creating new problems. That POC that impressed everyone? It's now dealing with 10x the volume but struggles to keep up. Maybe it's the scanned documents that aren't extracting as cleanly as you'd like. Or the complex layouts that need more sophisticated parsing. Or your team asking about semantic chunking strategies to squeeze more out of your RAG pipeline. So you start building custom solutions on top of and around our OSS library: adding your own orchestration layer here, a custom retry mechanism there, maybe some performance optimizations, new datastore integrations and monitoring dashboards. Each addition makes sense individually, but together they create a rat's nest of custom code that needs to be maintained.

What started as a simple document processing component gradually becomes a significant engineering project in its own right. Your team is spending more time wrestling with dependencies and long-running jobs than building the main product.

Sound familiar? You're not alone, and you're definitely not doing anything wrong. These are the growing pains we see with almost every team that opts to build in-house when going from prototype to production with document AI. The question isn't whether you'll hit these walls. It's recognizing when it's time for an honest cost-benefit analysis: is maintaining your homegrown solution more economical than switching to a managed platform?

Why We Know This Pattern

Here's the thing: OSS can be great if you're willing to go through the work of creating all the orchestration between different libraries yourself. You absolutely can build production document processing around our open-source package (in fact, we ourselves have done exactly that!). For that reason, we know better than anyone what it takes to build a production-grade document processing system: Kubernetes scaling; adding support for newly released Vision Language Models; prompt optimization; third-party integrations; compute provisioning; pod optimization; memory optimization; CPU/GPU utilization management; etc. These are all critical features that you just don't get with any OSS solution.

As teams start growing their business, their company, their workloads, an open source solution can become extremely difficult to scale with them. We see this in forums constantly: "Hey, your open source is cool, but we're having difficulty scaling it.”

That's exactly why we built the Unstructured platform. After watching hundreds of teams encounter these same challenges, we decided to tackle them systematically. The UI, managed API, scaling infrastructure, advanced models as our response to problems we see repeatedly, so you can skip straight to building your actual product.

We've had this conversation enough times that we can predict the four areas where teams start exploring what the Unstructured platform offers.

Four Signals that OSS May No Longer Be Enough

1. The Scaling Decision

When you're processing thousands of documents, the difference between Unstructured OSS and platform becomes stark. With the Unstructured platform, there's an entire team managing the infrastructure, scaling, and optimization behind the scenes so you don't have to. You connect your data source, choose your destination, hit run, and the documents flow through our processing pipeline automatically.

That same 50,000-document batch that required you to babysit the infrastructure? With the Unstructured platform, you throw them into a source, schedule it to run, and check back later to find clean, structured data waiting in your vector database or data warehouse. No capacity planning, no monitoring for failures, no troubleshooting memory issues at 2 AM.

And when something does need attention, you have dedicated support from the team that built and maintains the system. Your document processing becomes our problem to solve.

2. The Core Product Dilemma

Here's a diagnostic question: What did your team spend more time on last month—improving your core product features or optimizing document extraction accuracy? If you had to think about that answer, you might already know where this is going.

It starts innocently. Document processing was supposed to be a supporting component for your smart contract analyzer, your automated invoice system, or your research Q&A platform. But successful products create their own demands. Your users want better accuracy on complex layouts. They need faster processing for larger volumes. They're asking about edge cases your initial implementation didn't handle.

Each improvement makes perfect sense in isolation but six months later, your sprint planning sounds more like a document processing company's roadmap than whatever you originally set out to build.

The insidious part is that this drift feels productive. You're solving real problems, building valuable capabilities, responding to user needs. But every cycle spent perfecting document extraction is a cycle not spent on the features that differentiate your product in the market—the intelligent analysis, the unique workflows, the domain expertise that only your team can provide.

What you need isn't better document processing tools. You need document processing to stop being your problem entirely.

3. Enterprise Requirements

As your customer base grows, you'll inevitably encounter enterprises that require SOC 2 compliance, GDPR adherence, or HIPAA safeguards. OSS puts the entire compliance burden on your shoulders—implementing audit trails, ensuring data encryption, managing access controls, and documenting security procedures that would satisfy enterprise security teams.

Building compliant infrastructure from scratch is expensive and time-consuming. You need dedicated security protocols, regular audits, proper data handling procedures, and documentation that proves you're meeting regulatory standards.

The Unstructured platform is built with enterprise compliance in mind from day one. We handle SOC 2 requirements, implement GDPR-compliant data processing, maintain proper audit trails, and provide the security documentation that enterprise customers expect. Instead of building compliance infrastructure yourself, you inherit it through our managed service.

4. Advanced Capability Needs

This is often the most expensive wall to hit, because it's where teams realize they're trying to rebuild capabilities that already exist. OSS gives you solid document partitioning and basic text extraction, but modern LLM applications have evolved to require much more: advanced chunking strategies, embedding generation, multi-modal processing, and intelligent document routing.

Building these features on top of OSS means either accepting limitations or embarking on significant custom development. Want semantic chunking that understands document structure? You're building that yourself. Need embeddings generated alongside your extraction? Custom code. Want to route each page through different parsing strategies based on their complexity? More custom logic.

The Unstructured platform includes advanced chunking strategies (by-page, by-similarity, semantic), embedding generation with your choice of models, and intelligent routing that automatically selects the optimal processing strategy for each page so you spend less. But it's not just about having these features, it's about having access to capabilities that are constantly evolving.

We integrate the latest vision language models as they become available, giving you access to cutting-edge document understanding without having to research, evaluate, and integrate new models yourself. When better OCR models are released, when new VLMs emerge that excel at table extraction, when more sophisticated multimodal processing becomes available—platform users get these improvements automatically.

We have dedicated teams continuously evaluating new models, running benchmarks against our document processing workflows, optimizing strategies for better extraction accuracy, and curating datasets to improve performance.

These aren't one-time features, they're ongoing R&D investments that your team benefits from without having to build the expertise in-house.

When to Use What

The reality is that both OSS and platform have their place. OSS genuinely works great for many use cases, and some teams should absolutely stick with it.

The question isn't whether one is "better" than the other. It's about finding the right tool for where your team is right now and where you're headed. If you're hitting multiple walls from the list above, the platform probably makes sense. If you're not, OSS might be perfect for your needs.

Where Do You Go from Here?

We're genuinely proud of what our OSS library has enabled. Thousands of teams have built incredible AI applications on top of it, and that's exactly what we hoped would happen when we open-sourced it. OSS will always be there for teams that want to build document processing infrastructure themselves.

But if you're hitting the walls we described, or if you'd rather focus your engineering resources on your core product, the Unstructured platform offers something different. Beyond just managed infrastructure and advanced features, you're getting a team that's constantly working to make document processing better. We're continuously experimenting with different parsing strategies, optimizing infrastructure performance, and addressing edge cases as they emerge. You get more than just the product: you get a team ensuring this module in your pipeline keeps evolving and improving.

Your document processing capabilities improve without you having to become experts in the latest AI research or spend cycles optimizing infrastructure.

The easiest way to see if that's worth it for your team is to try the Unstructured platform with your actual documents and workflows. Start a free trial today and process up to 1,000 pages daily for seven days, or schedule a demo to see how Unstructured handles your documents.