Introduction

"Build your own" Retrieval-Augmented Generation (RAG) pipelines might seem like the obvious path for independent software vendors (ISVs) looking to quickly deliver generative AI (GenAI) capabilities to customers. This DIY route — whether in the form of custom code from scratch, open-source frameworks like LangChain or LlamaIndex, or AI microservices from cloud providers like Vertex AI or Amazon Bedrock — appears attractive at first glance.

But here's the hard truth: this strategy often backfires. ISVs funnel their limited and valuable AI developer resources into building commoditized features, such as PDF parsing, document chunking, and AI-driven search, that are table stakes instead of true differentiators. In the process, they unknowingly pile on technical debt. Hidden in the PoC stage, this debt soon turns into a heavy anchor that drags down agility, slows innovation, and stalls growth.

This post will break down the hidden complexities and common pitfalls that ISVs inevitably encounter when trying to build and sustain their own RAG pipelines. We'll highlight specific forms of technical debt that build up over time and explain why the short-term gains of going it alone rarely outweigh the long-term risks. By shedding light on these technical complexities and risks, our goal is to help ISVs reconsider the "build your own" approach, steering them toward solutions that are purpose-built to avoid these common, costly pitfalls.

Why RAG often proves challenging

Typically, we see two outcomes when ISVs tackle RAG themselves.

First, and more commonly, there’s failure. The ISV sprints to PoC, gets initially promising results, but struggles to achieve production-grade, stable, scalable GenAI capabilities, running into unforeseen complexities and constant technical hurdles.

Sometimes, there’s success, but comes with a catch. The ISV manages to deploy working GenAI features, but becomes bogged down by mounting technical debt. This severely limits their ability to innovate and respond swiftly to evolving market demands.

Here are some specific examples of technical debts that many ISVs that try to build RAG themselves must cope with.  

1. Fragile Search & Indexing Layer

• Risk: Most DIY RAG builds lean on an open-source search engine (Elasticsearch / OpenSearch / Solr, Weaviate, etc.) tuned for a single workload. Under actual traffic, this search engine becomes the single point of failure due to scale bottlenecks, cluster split-brain, index corruption, hot shards, version mismatches, and costly re-ingest cycles.

• What it means for builders: Outages invalidate any promise of “always-on answers,” and search tuning becomes a full-time ops function.

2. Thin Observability & Release Hygiene

• Risk: RAG systems stretch across ingestion → chunking → embedding → indexing → retrieval → generation. Without end-to-end instrumentation and synthetic tests, silent failures (e.g., a broken embedder or mis-scoped ACL) surface only as “the answer looks wrong.” Debugging hops through multiple repos, clouds, and data stores.

• What it means for builders: The hidden labor cost of debugging dwarfs any license savings; incident response SLAs become unachievable.

3. Reactive Security & Compliance

• Risk: Security fixes are patched after CVEs hit Hacker News instead of being baked into CI/CD. OSS dependencies pile up with unknown licensing and FedRAMP/SOC 2 gaps.

• What it means for builders: Every new customer or compliance audit triggers a scramble. The penalty for one missed patch is business-critical in regulated industries — often the customers with the greatest need to ground GenAI in trusted data.

4. Evolving Ingestion & Retrieval Schemas

• Risk: As data formats, chunking heuristics, embedder versions, and retrieval strategies evolve, legacy pipelines hang around (“long tail ingestion”). Every breaking change requires full re-embedding, reindexing, and reverification — often with no UI to orchestrate or roll back.

• What it means for builders: Feature velocity stalls; even small format tweaks trigger full reprocessing jobs, consuming time — and often GPU credits — for zero visible value.

5. Connector Sprawl & Ownership Gaps

• Risk: Most enterprise data lives behind SaaS APIs (SharePoint, Confluence, Jira, Salesforce, etc.). Each API version bump, rate limit change, or auth flow update breaks ingestion. If a third-party integration vendor owns the code, you wait for their fix; if you own it, you maintain dozens of idiosyncratic clients.

• What it means for builders: Every connector outage blocks ingestion, knocks documents out of sync, and erodes trust in answers. Maintaining dozens of integrations quickly eclipses core product work. Lack of reliability here undermines trust in the entire RAG stack.
  

Conclusion

Ultimately, while building your own RAG pipeline may initially appear as the most direct path to delivering GenAI capabilities, it often transforms into a costly detour laden with hidden complexities and mounting technical debt.  

Rather than getting stuck maintaining foundational components that won't distinguish you from competitors, ISVs should consider specialized, purpose-built RAG solutions. This approach frees your team to focus your limited AI talent on innovation and strategic differentiators—ensuring you're equipped not only for today's market but also positioned to quickly adapt to tomorrow’s opportunities.

Want RAG without the drag? Check out our docs and discover how to dodge DIY pitfalls and start delivering real AI value, fast.

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