Why building your own RAG stack can be a costly mistake

Introduction

Imagine spending months and hundreds of thousands of dollars building an in-house generative AI system, only to discover it's slower, less secure, and more expensive than existing solutions. This isn't a hypothetical scenario—it's happening to companies right now, everywhere.

Jumping on the AI bandwagon, businesses are naively falling into a trap: thinking they can outdo existing RAG-powered stacks by cobbling together their own solutions from scratch. The reality? They're usually spending lots of time and resources on the infrastructure, instead of building their application to solve their specific use cases.

Would you build your own database system from scratch?

Of course not.

You would buy a database or data warehouse system from vendors like Oracle, Microsoft, Snowflake, or Databricks, and build your application on top of that. Why would you want to develop expertise in database indexing, or query optimization?

The same is true for RAG.

A typical starting point is this: I just need an embedding model, a vector database, a prompt, and an LLM, and I’m done. And yes, getting a first “chat-with-my-PDF” POC is pretty easy to do. But as you scale and move to production deployment - you quickly realize how much more is required.

Let’s dive into the details: we will review seven key challenges that make do-it-yourself (DIY) RAG systems a losing proposition for enterprise deployments.

Challenge 1: hallucinations and opaque responses

When building your RAG application, it's not just important—it's absolutely critical—to have mechanisms to detect, reduce and even correct hallucinations, as well as provide citations with every response.

This is even more critical in regulated industries like healthcare, insurance, or financial services.

Fighting hallucinations

Large language models are notorious for generating "hallucinations"—fabricated information that sounds convincing but is completely false. Without robust mechanisms to prevent and explain these inaccuracies, you very quickly lose user trust, damage your brand’s reputation, or may even create potential legal, regulatory, and financial risks.

The Air Canada incident provides a scary example: a passenger claimed to have been misled about the airline’s rules for bereavement fares when the chatbot hallucinated an answer inconsistent with airline policy. Canada’s small claims court found the passenger was right and awarded them $812.02 in damages and court fees. The amount here is not high, but imagine what would be the impact of a class action lawsuit with thousands of customers or even worse. Not to mention, the negative impact on brand reputation and customer loyalty.

The truth is, that most DIY RAG stacks suffer from a hallucination rate that is too high. To deal with this, the team would need to develop, implement, integrate, and test specialized models for hallucination detection (like Vectara’s HHEM), or hallucination correction (as demonstrated in this recent research).

This requires a very specific set of skills and expertise, which in many cases is beyond the in-house team’s expertise or bandwidth.

Explaining responses

Imagine receiving an important business recommendation or medical advice without knowing the source. You'd be skeptical, right? The same principle applies to AI-generated responses.

When a RAG application provides inline citations, it's essentially saying, "Don't just trust me—look at the evidence." This approach transforms AI from a black box into a collaborative research tool - users can evaluate the quality of source documents, cross reference information, and understand the context of the generated response.

This not only builds trust but also empowers users to verify and understand the context, reducing the risk of misinformation. When the RAG platform provides citations, it is easier for developers to troubleshoot their applications and easier for governance teams to understand the overall coverage of (or gaps within) their data set.

Challenge 2: security and compliance failures

Handling sensitive data is a minefield of legal and ethical obligations. One slip-up can result in hefty fines, lawsuits, and irreparable damage to your brand. Regulatory bodies aren't forgiving when it comes to mishandling data, and neither are your customers.

In-house RAG systems often lack robust access controls and a robust mechanism to deal with anonymization of PII or PHI, and implementing these mechanisms can be daunting, not to mention that it requires continuous effort and investment as privacy laws and data governance policies continue to evolve.

What’s more - you have to defend against the new threat of “prompt injection attacks” that may be used to override built-in safety measures to extract unauthorized information (as shown for example in this example). .

RAG stacks, whether they are deployed as SaaS, VPC, or on-premise, tend to prioritize security from the ground up, offering enterprise-grade access controls, end-to-end data encryption, strict anonymization processes that keep you compliant with regulations and integrated mechanisms to protect against prompt injection attacks.

Why risk going it alone when experts have already solved these problems, and continue to invest in ensuring the platform defenses are up-to-date with all the latest techniques and approaches?

Challenge 3: vendor chaos

Building your own RAG system often involves cobbling together components from multiple vendors—LLMs, vector databases, orchestration layers, or embedding models. Each component comes with its own infrastructure requirements, support channels, pricing models, and onboarding processes.

This approach means you have to deal with uncoordinated vendor relationships, which often lead to unexpected expenses, conflicting updates, and integration nightmares. Your team spends more time managing vendors than improving the RAG application you are building to implement your business requirements.

And what would you do when something goes wrong - be it a degraded response quality, high latency, or any other issue?

Troubleshooting becomes a finger-pointing exercise among different vendors, and you find yourself in the middle of this, instead of focusing on quick remediation and getting the system back online.

On the other hand, with RAG providers you get a unified platform with straightforward pricing, seamless onboarding, and responsive end-to-end support. By consolidating your needs with a single provider, you eliminate the headaches associated with vendor chaos - the integrated solution ensures that all components work harmoniously, allowing your team to focus on what matters most - your application.

Challenge 4: unsustainable upkeep

An in-house RAG system demands a diverse team of experts — software engineers, data scientists, ML engineers, security specialists, and a DevOps/MLOps team.

Finding and retaining this talent is not only expensive but also increasingly difficult in a highly competitive market, and the turnover in IT departments means that critical knowledge will regularly walk out the door, leaving projects in limbo.

Making things more complicated, your team must also grapple with integrating new technologies (like a vector database), retrieval techniques (like hybrid search), data sources and ETL flows, embedding models, re-rankers, hallucination detection models, and generative LLMs.

Each component may require specialized knowledge, and updates can break existing integrations. This complexity slows down development cycles and diverts focus from your core business objectives.

By leveraging RAG-as-a-service, you offload the upkeep to specialists who handle the heavy lifting, reducing the need for a large, specialized in-house team. This approach not only saves costs but also accelerates your time to market.

Challenge 5: scaling drives up costs

A successful RAG application deployment quickly grows in scale - more input data and documents from new sources, a higher volume of queries, and more advanced RAG techniques like Graph RAG or multi-modal RAG.

As your RAG stack grows in scale, so do the challenges.

For example, indexing large datasets while maintaining the accuracy and low latency of the retrieval step requires significant computational resources and technical expertise. This is especially true if you integrate some of the more advanced retrieval techniques required to achieve high accuracy like hybrid search or UDF reranking.

Even with increased investment, in-house RAG systems often struggle to deliver high-quality responses at scale. Latency increases, accuracy drops, and user satisfaction plummets.

When using RAG-as-a-service, advanced indexing techniques and state-of-the-art retrieval algorithms that maintain performance regardless of data size are already implemented, and are continuously improved, so you can be worry-free to know that the system will scale to your needs. The cloud-based infrastructure automatically adjusts to workload demands, ensuring consistent quality without escalating costs.

Challenge 6: high latency frustrates users

It is well known that these days users expect fast responses - not just in generative AI, but in general for any application they use. They expect that new data will be indexed and searchable immediately once it becomes available.

High latency isn't just about speed; it's about user experience. Delays erode user engagement, reduce conversion rates, and ultimately impact the value you deliver to your organization with the generative AI solution.

In-house RAG systems often suffer from high latency due to inefficient algorithms, inadequate infrastructure or resources, and suboptimal data retrieval implementations. High latency can also strain your system resources, leading to higher operational costs and potential downtime.

With RAG-as-a-service, the platform is optimized for speed. With globally distributed infrastructure, highly efficient retrieval implementations, and GPU inference for the various models that comprise the RAG stack (embedding, LLMs, re-rankers, and hallucination detection), you can expect rapid responses that keep users engaged and satisfied.

Challenge 7: dealing with multiple languages

Many in-house RAG implementations start with English because it’s the sensible thing to do. Once the first POC is working, the team discovers that scaling to other languages is far from trivial. In fact, it may require a complete redesign.

Let me explain why.

Supporting additional languages requires multi-lingual support in many components of your RAG stack:

Your input documents may be in multiple languages, requiring your chunking approach to be language-aware and the embedding models to perform well in non-English languages.
Advanced retrieval techniques such as hybrid search and reranking need to support these languages as well.
The generative LLM you use must be cross-lingual as well, and be able to both process input chunks and respond in multiple languages.
Any guardrail mechanisms you use must be cross-lingual.

Extending any one of these components to non-English is hard enough, not to mention all of them at the same time, and ensuring everything else stays the same in terms of accuracy, no hallucinations, and low latency.

This is why choosing a RAG-as-a-service platform that provides extensive language support across all the RAG stack components is critical. That way your application can cater to a diverse global audience and work with data sets in any language, all right out of the box.

Conclusion: stop reinventing the wheel

The evidence is overwhelming: building your own RAG system is costly, complex, and ultimately a flawed strategy.

The challenges are numerous—opaque responses, security pitfalls, vendor chaos, unsustainable upkeep, scaling issues, high latency, and inflexible language support. Each one is a stumbling block that diverts attention and resources away from what truly matters—delivering the value from GenAI to your organization.

Services like Vectara have already solved these problems. They've invested the time, expertise, and resources to create robust, secure, and scalable RAG solutions. By adopting their platforms, you sidestep the pitfalls of DIY systems and gain a competitive edge.

Make the smart choice. Leave RAG to the experts and propel your business forward.

To experience the full strength of Vectara’s Responsible Enterprise RAG-as-a-service platform, sign up for our free trial to try Vectara’s, and experience first-hand the benefits of using a RAG platform.