RAG systems, which have shown great promise in development environments, can be particularly challenging to deploy in production environments. One major hurdle is ensuring access and security. In production, RAG systems must handle a large volume of user requests while maintaining the security and integrity of the data. This requires robust access controls, encryption, and monitoring, which can be difficult to implement and maintain. In contrast, development environments often have more relaxed security settings, making it easier to test and iterate on RAG systems without the added complexity of security protocols.
“The case for production-grade RAG systems in enterprises warrant much deeper scrutiny over system design, given performance, cost and security considerations.”
In this 9 part series, we discuss various system design considerations that directly impact RAG system performance, cost and security which serves as a guide for CTOs, CISOs and AI Engineers.
Download the complete guide on Advanced RAG for Enterprises
Choice of model and precision as a trade-off between performance and cost
When designing a Retrieval Augmented Generation (RAG) system, selecting the right model is crucial. Different models excel in various scenarios, such as coding, general question-answering, or image interpretation. The use case should guide the model decision, as each model’s strengths and weaknesses vary. For instance, some models may prioritize accuracy, while others focus on speed (tokens per second). Carefully evaluating the performance metrics that matter most to your application is essential. To complicate things further, with multiple versions of any given open source model available, how should organizations choose between different formats or quantized models?
Legal and Ethical Considerations
Beyond performance, it’s essential to consider the legal foundations of each model. Some models are available for commercial use, while others are not. Additionally, some models are explicitly screened to remove inappropriate or copyrighted content, while others may not be. Ensuring the chosen model aligns with your organization’s legal and ethical standards is vital.
Resource Requirements and Quantization
When self-hosting models, different models have varying requirements in terms of VRAM. Some models require significant resources, while others are more efficient. Additionally, some models are available in full precision, while others are available in quantized formats that use fewer resources and have lower running costs. Quantization can significantly reduce the computational requirements of a model, making it more feasible for deployment on resource-constrained infrastructure. This however inevitably reduces model performance.
Model precision and hardware alignment
Running a self-hosted model in full precision versus lower precision (half/16fp, 8-bit, 4-bit) significantly impacts performance, speed, and VRAM requirements. Full precision models utilize 32-bit floating-point numbers, providing high accuracy but requiring more computational resources and memory. Lower precision models, on the other hand, use reduced numerical precision, resulting in faster inference speeds and reduced VRAM requirements. For example, 8-bit precision models can run up to 4 times faster and require half the VRAM compared to full precision models. However, this comes at the cost of slightly reduced accuracy. It is also crucial to align hardware choice with model and precision decisions, as different GPUs support different computation types. For instance, NVIDIA’s Tensor Cores support 16-bit and 8-bit precision, while Google’s TPUs support 8-bit precision. Ensuring compatibility between hardware and model precision is vital for optimal performance and efficiency.
Cost Structure Considerations
The cost structure of RAG systems varies widely depending on the model and deployment approach. Cloud-based APIs often charge per input token and per output token. For this reason and depending on the use-case, managing cost can become extremely challenging since it is often hard to anticipate the types of request and how long inputs or outputs can be.
The costs involved in self-hosted model tend to be more fixed in nature and vary depending on the resource requirement of the model, configurations applied as well as overall scale of the user base. Quantized models can reduce resource needs and thereby the running costs, but tend to have lower performance and hence require additional optimization and fine-tuning. Understanding the cost structure and its implications on the overall budget is therefore crucial for sustainable and scalable RAG system deployment.
By carefully evaluating these factors, organizations can strike a balance between performance and running cost, ensuring their RAG system meets their specific needs while minimizing expenses.
Overall, production environment RAG systems presents the following key questions. Dive into each of the subtopics through the links below:
- API model access vs hosted model on self-managed instances
- Choice of model and precision as a trade-off between performance and running cost
- Choice of vector databases based on types of supported search algorithm and security options
- Data pipeline design for reliability, content safety and performance-focused data pre-processing
- Choice of chunking approach based on type of content: length, sentences or logical chunks
- Pre-retrieval filters and transformations for security and retrieval performance optimization
- Post-retrieval ranking and stacking approaches for performance and cost optimization
- Guardrail implementation with consideration for different modalities of inputs and outputs
- Logging mechanisms to facilitate performance, cost and security analyses
Download the complete guide on Advanced RAG for Enterprises
