Designing an LLM-based system for adaptability and maintainability

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Table of Contents

• Designing an LLM-based system for adaptability and maintainability

• Introduction

• Modular Architecture and Standard Interfaces

• Pipeline Flexibility for Emerging Models and Techniques

• Scalability Challenges and Solutions

• Cost Optimization Considerations

• Technical Adaptability and Future-Proofing

• Conclusion

Introduction

Large Language Models (LLMs) are increasingly woven into software systems for tasks like natural language understanding, content generation, and decision support. However, building an LLM-based system that remains adaptable and maintainable poses significant engineering challenges (A Functional Software Reference Architecture for LLM-Integrated Systems This research work has been funded by the Swedish Knowledge Foundation through the MoDEV project (20200234) , by Vinnova through the iSecure project(202301899), and by the KDT Joint Undertaking through the MATISSE project (101140216).) . Unlike traditional software, LLM applications often lack a standard reference architecture, making it difficult to ensure modularity, interoperability, and scalability as they evolve . This report presents a comprehensive literature review on designing LLM-based systems for long-term adaptability and maintainability. We focus on modular components, standard interfaces, and flexible pipelines that allow integration of new models and techniques as they emerge. Key areas addressed include scalability challenges, cost optimization, and technical adaptability to future architectures.

Modular Architecture and Standard Interfaces

Adopting a modular architecture is crucial for maintainability in LLM systems. By breaking the system into well-defined components (e.g. data preprocessing, model inference, postprocessing), developers can update or replace parts of the pipeline without affecting others . A layered design helps separate concerns: for instance, a recent study proposes a three-layer decoupled architecture that isolates application logic, communication protocols, and hardware execution into separate layers (The Next Frontier of LLM Applications: Open Ecosystems and Hardware Synergy). This kind of separation, inspired by classic software engineering (e.g. microservices and layered systems), enhances modularity and cross-platform compatibility . It mirrors the move away from monoliths in favor of services that can be independently developed, scaled, and maintained .

Standard interfaces between these components further improve adaptability. Using well-defined APIs or protocols ensures that modules can interact in a consistent way even as internals change. For example, OpenAI’s GPT services expose a RESTful API, decoupling the client application from the model’s implementation (A Functional Software Reference Architecture for LLM-Integrated Systems This research work has been funded by the Swedish Knowledge Foundation through the MoDEV project (20200234) , by Vinnova through the iSecure project(202301899), and by the KDT Joint Undertaking through the MATISSE project (101140216).). This means an application can leverage improved or entirely new models via the same interface, avoiding tight coupling. Similarly, open standards like ONNX (Open Neural Network Exchange) provide a common model format and operator set to enable model portability across frameworks (ONNX | Home). ONNX or other standardized model representations allow an LLM trained in one framework to be deployed in another, or optimized on specialized inference runtimes, without redesigning the entire system. Frameworks like Hugging Face Transformers also define uniform model classes and tokenization pipelines, so one can swap in a new model (say a 2025 architecture) in place of an older one with minimal code changes, as long as it adheres to the same interface conventions.

Modularity extends beyond software layers to the model level as well. Research suggests designing LLMs themselves with modular components can aid adaptability. For instance, hybrid or two-module architectures have been proposed where a large pre-trained “universal” representation module is paired with a smaller task-specific module that can be fine-tuned independently (A Comprehensive Overview of Large Language Models). This way, new tasks or updates only require training the lightweight module, preserving the core model. Such approaches echo the use of adapters or LoRA modules in practice – small add-on weight matrices that adapt a frozen LLM to new domains at low cost. In general, maintaining clear separation of concerns – whether via microservice APIs, plugin architectures, or model submodules – is a recurring theme in the literature for building maintainable LLM systems . Modular design simplifies updates, as each component can be extended or replaced (for example, upgrading the model or adding a new pre-processing step) without overhauling the whole pipeline . It also improves testing and debugging, since individual pieces can be validated in isolation (The Next Frontier of LLM Applications: Open Ecosystems and Hardware Synergy).

Pipeline Flexibility for Emerging Models and Techniques

Given the rapid pace of LLM innovation, an effective system must support pipeline flexibility – the ability to plug in new models, libraries, or techniques with minimal friction. Industry trends illustrate this clearly. Cloud providers now offer platforms that are model-agnostic by design. For example, Amazon Bedrock provides a unified managed service to access many different foundation models (from AWS Titan to Anthropic Claude to Stability AI’s models) under one API (Amazon Bedrock offers access to multiple generative AI models - Amazon Science). The rationale is that “the world is moving so fast on [foundation models], it is rather unwise to expect that one model is going to get everything right,” so the platform focuses on customer choice and easy integration of the latest models . This model hub approach ensures that as new LLMs emerge, they can be integrated as interchangeable components in the application pipeline. Similarly, open-source LLM orchestration frameworks like LangChain and Hugging Face’s Transformers pipelines enable developers to switch out language models or tool modules without changing the surrounding code – for instance, swapping a text generation model from GPT-3 to Llama 2 by changing a configuration, thanks to a standard pipeline interface. These frameworks underscore the importance of abstraction: developers program against a generic LLM interface (for generation, question-answering, etc.), and the framework handles the specifics of whichever model is selected.

Modern inference serving systems also prioritize flexibility to incorporate new optimization techniques. Hugging Face’s Text Generation Inference (TGI) server is a notable example: it introduced a pluggable backend architecture to support multiple inference engines (like vLLM, NVIDIA TensorRT, DeepSpeed, etc.) behind a common API (Introducing multi-backends (TRT-LLM, vLLM) support for Text Generation Inference). TGI acts as a unified front-end, so depending on the model or hardware, one can choose the optimal backend implementation without altering client code or deployment logic . This design acknowledges that different models or workloads may benefit from different inference libraries – e.g. one LLM might run fastest on a GPU with TensorRT optimizations, while another uses a CPU-efficient runtime – and it future-proofs the system by making it easy to “plug in” new backends as they arise. In the TGI architecture, a new backend only needs to implement a defined Backend trait (interface) in Rust, and then it can be integrated as a drop-in option for serving models . The result is extensible serving infrastructure: as research brings new decoding algorithms or memory management techniques, they can be added to the system and immediately leveraged by all models using that interface.

This pipeline flexibility extends beyond model serving. New techniques like retrieval-augmented generation (RAG), prompt orchestration, or tool use by LLM “agents” can be incorporated if the system is built with a modular pipeline. For example, an enterprise might start with a straightforward prompt-response LLM service, then later integrate a retrieval module (database lookup) in front of the model to provide contextual knowledge. If the original design decouples the knowledge retrieval component and the model inference component, this addition is straightforward. In practice, many developers use pipeline frameworks (e.g. KubeFlow pipelines, Airflow, or custom DAGs) to string together LLM steps and external tools in a flexible manner. The academic community also emphasizes avoiding hardwired assumptions so that new capabilities can be inserted. A 2024 “open ecosystems” paper argues for standardized plugin interfaces for LLMs to interact with tools and data, to avoid the fragmentation seen in current agent frameworks (The Next Frontier of LLM Applications: Open Ecosystems and Hardware Synergy) . In essence, designing for extensibility – by using standard connectors and loosely coupling each stage of the LLM workflow – ensures the system can continuously adopt state-of-the-art models or techniques (from better fine-tuning algorithms to novel prompt strategies) without a complete rewrite.

Scalability Challenges and Solutions

Scalability is a major consideration for LLM-based systems, which must handle the heavy compute and memory demands of large models across varying workloads. One challenge is scaling training and fine-tuning of models. Modern LLM training can involve hundreds of billions of parameters, which far exceed the memory of a single device. For instance, training the 530B parameter Megatron-Turing NLG model required over 4,000 NVIDIA A100 GPUs working in parallel (DeepSpeed ZeRO++: A leap in speed for LLM and chat model training with 4X less communication - Microsoft Research). Efficiently utilizing such clusters calls for complex distribution strategies to partition the model and workload. Techniques like data parallelism, tensor/model parallelism, and pipeline parallelism are combined to split the training load across GPUs. Microsoft’s DeepSpeed library and others implement the ZeRO (Zero Redundancy Optimizer) approach, which partitions model states (optimizer, gradients, etc.) across workers to reduce memory duplication . This has enabled training of models on the order of Hundred+ billion parameters (Bloom-176B, etc.) within practical resource limits . Continual improvements are being made – e.g. ZeRO++ (2024) reduces communication overhead by 4× via quantization and communication remapping, yielding over 2× throughput improvement in large-scale training scenarios . For a system designer, leveraging these frameworks (DeepSpeed, Megatron-LM, PyTorch FSDP, etc.) is key to ensuring the training pipeline scales efficiently as models grow: the system should allow easy enabling of distributed training strategies and use of high-performance computing resources when needed.

Scaling inference and deployment presents a different set of challenges. LLM inference is memory-intensive due to the need to store activation maps or attention key-value caches for potentially long generation sequences. Serving many concurrent requests can quickly exhaust GPU memory. A specific bottleneck identified is the key-value (KV) cache used in transformers during text generation: each request’s KV cache can consume hundreds of MBs and grows with the sequence length ( Efficient Memory Management for Large Language Model Serving with PagedAttention). Naively handling this memory can lead to fragmentation and redundancy, limiting how many requests can be batched together . State-of-the-art serving systems are addressing this with innovative memory management. The vLLM framework (SOSP 2023) introduced PagedAttention, treating the KV cache like a virtual memory system with paging . This allows nearly zero wasted memory by flexibly sharing and reusing cache pages across requests. As a result, vLLM can batch far more requests for throughput without running out of memory, improving LLM inference throughput by 2–4× with the same hardware and latency compared to prior optimized systems . These advances illustrate that to scale under heavy workloads, an LLM system likely needs to incorporate specialized optimizations – either by using such advanced runtimes or by carefully engineering memory and compute usage (e.g. quantizing activations, caching partial results, etc.).

Another aspect of scalability is concurrency and deployment architecture. Systems must scale out (horizontally) to serve increasing request volumes. A common approach is a stateless microservice model for the LLM inference component, which can be replicated across many instances behind a load balancer. This is facilitated by container orchestration (Kubernetes, etc.) and inference servers. Tools like KServe (formerly KFServing) provide cloud-agnostic deployment of ML models on Kubernetes and handle autoscaling, health checking, and request routing out-of-the-box (GitHub - kserve/kserve: Standardized Serverless ML Inference Platform on Kubernetes). KServe, for example, supports request-based autoscaling (including scale-to-zero for idle periods) and even GPU pool autoscaling, so the system can dynamically add or remove inference pods as load fluctuates . It also offers a standardized inference protocol (compatible with the OpenAI API for generative models) to simplify integration . For high-scale scenarios, techniques like dynamic batching (grouping multiple requests on the fly to amortize GPU overhead) are used – available in servers like NVIDIA Triton, which is optimized for scalable, multi-model serving (LLM Inference: Techniques for Optimized Deployment in 2025). Triton and similar systems allow deploying many model variants simultaneously and can route requests intelligently, helping serve different model sizes or tasks under one platform. In summary, to ensure scalability, an LLM system should leverage: (1) Distribution – split large computations across machines effectively; (2) Optimized inference engines – to maximize throughput per hardware unit; and (3) Elastic deployment – so the system can scale-out with demand and back down to save cost. Designing with these in mind (and integrating the appropriate libraries/services) is key to efficient operation across different workloads, from low-latency interactive use to high-throughput batch processing.

Cost Optimization Considerations

LLMs are computationally expensive, so designing for cost-efficiency is as important as raw performance. A striking industry trend is the rapid decline in the cost of LLM inference – often referred to as “LLMflation” – where the price per output token for a given performance level is dropping by an order of magnitude per year (Welcome to LLMflation - LLM inference cost is going down fast ⬇️ | Andreessen Horowitz) . For example, what cost $60 per million tokens with GPT-3 in 2021 now costs around $0.06 with newer models of similar capability . This 1000× cost reduction in three years is driven by several factors that inform system design :

• Model and hardware efficiency: Each generation of hardware (GPUs, TPUs) offers better price/performance, and new model architectures are far more efficient. Notably, today a 1B-parameter model can achieve the same task performance as a 175B model from a few years ago by training on vastly larger datasets and using improved training techniques . Smaller models with smarter training (beyond the original Chinchilla scaling predictions) drastically cut inference cost because they require less compute per token. System designers should thus remain open to replacing models with newer, smaller variants that offer the needed accuracy – the infrastructure should make it easy to deploy such a model swap when appropriate.

• Quantization and compression: Reducing precision of model weights is a highly effective cost optimization. In practice, moving from 16-bit floating point to 8-bit or 4-bit integers can shrink memory and compute requirements by 2×–4× (or more) with minimal performance loss (Welcome to LLMflation - LLM inference cost is going down fast ⬇️ | Andreessen Horowitz). Modern accelerators and libraries support 4-bit inference for transformers, effectively doubling throughput per GPU generation over generation. Therefore, systems should be designed to support low-bit quantized models. This might mean choosing inference frameworks that natively handle quantized weights or using toolkits like TensorRT-LLM, ONNX Runtime, or Intel Neural Compressor which provide quantization support (LLM Inference: Techniques for Optimized Deployment in 2025). The literature also explores structured pruning and knowledge distillation as means to compress LLMs – removing redundant parameters or training a smaller model (student) to imitate a larger one (teacher) – which can maintain accuracy while cutting costs (Mastering LLM Optimization: 10 Proven Techniques). From a maintainability perspective, it’s wise to track and incorporate such compression techniques into the model lifecycle to continually reduce serving costs.

• Optimal resource utilization: Cloud deployment offers many levers for cost control. One best practice is using autoscaling and scale-to-zero for serving workloads so that you don’t pay for idle capacity. As noted, frameworks like KServe and serverless GPU services can automatically spin down instances when demand is low (GitHub - kserve/kserve: Standardized Serverless ML Inference Platform on Kubernetes). This is especially important for LLMs, since keeping a large GPU instance running 24/7 can be costly if usage is sporadic. Another strategy is mixed infrastructure: using cheaper hardware for certain workloads (e.g. CPU or inferior GPUs for small models or non-time-critical jobs) and reserving expensive GPUs for when they are truly needed (such as real-time chat with a large model). Some systems employ a tiered approach – e.g. try an inexpensive smaller model first for a query, and only escalate to a large model if confidence is low – thereby saving cost on easy tasks. Google’s Sparsely-Gated Mixture-of-Experts is an architectural analogue of this idea: a gigantic model is composed of many small expert networks, and only a few are activated per input, reducing the computation (and cost) per inference while retaining high capacity (A Comprehensive Overview of Large Language Models). This idea of conditional computation can be applied in system design by routing requests intelligently (simple requests to cheaper models, complex ones to powerful models), using confidence estimations or other heuristics.

• Efficient fine-tuning and updates: Maintaining an LLM system often involves updating models for new data or tasks. Full model retraining is extremely expensive, so techniques that adapt models efficiently can save enormous cost. Low-Rank Adaptation (LoRA) is one such technique that has gained popularity in 2024 for enterprise use. LoRA adds small learned matrices to each transformer layer, allowing the model to be fine-tuned by training only a tiny fraction of the parameters. This offers “a scalable, low-cost alternative to full fine-tuning, allowing enterprises to adapt models efficiently without heavy resource investment” (LoRA: The Underrated Key to Enterprise AI Efficiency - Medium). By designing the system to support plugin modules or adapter weights (e.g. the architecture can load LoRA weights on top of a base model at runtime), new capabilities can be added at a fraction of the GPU-hours that full training would require. Similarly, techniques like QLoRA combine 4-bit quantization with LoRA fine-tuning, enabling even 30B–65B parameter models to be fine-tuned on a single GPU (@philschmid on Hugging Face: "What's the best way to fine-tune open LLMs in 2024? Look no further! I am…"). Incorporating these advances means the organization can keep models up-to-date or customer-specific without prohibitive cost, greatly improving long-term maintainability.

In practice, cost optimization is a balancing act – one must monitor model performance while trimming expenses. It is advisable to set up proper logging and perhaps A/B testing when introducing compressed models or cheaper substitutes, to ensure the quality remains acceptable. Many industry teams also continuously evaluate new open-source models: open models can be self-hosted at cost of infrastructure, which for high usage volumes can be cheaper than per-call fees of proprietary APIs (Welcome to LLMflation - LLM inference cost is going down fast ⬇️ | Andreessen Horowitz). Indeed, the emergence of high-quality open LLMs (Meta’s LLaMA family, etc.) has introduced price competition and flexibility, driving down serving costs across the board . Therefore, a maintainable LLM system should avoid lock-in to a single expensive model – instead, it should be architected to allow swapping in an open-source alternative if it meets requirements, or switching cloud providers if better pricing is available. By combining model-level optimizations (smaller, quantized models) with system-level policies (autoscaling, multi-model routing) and staying agile in model selection, organizations can minimize cost while maintaining performance. This is critical for the sustainability of deploying LLMs at scale.

Technical Adaptability and Future-Proofing

Designing for technical adaptability means ensuring the LLM-based system can embrace new architectures, techniques, and usage patterns over time without major refactoring. The field of LLMs is evolving rapidly – new network architectures (e.g. state-space models, transformer hybrids), new training paradigms (reinforcement learning with human feedback, retrieval augmentation), and even new modalities (multimodal models handling images, audio, etc.) are emerging. A future-proof system therefore must be built on extensible foundations. Many of the practices discussed – modularization, standard interfaces, and flexible pipelines – inherently support adaptability. For example, using a high-level abstraction for models (such as a generate(text)) means if a radically different model type (say, one that uses a neural state-space instead of attention) comes out in 2025, it can be integrated as long as it implements the same interface. The system doesn’t need to care how the model is implemented under the hood. This underscores the importance of having clear contracts for each component. An organization might define an interface for “LLM model” with methods for generation, fine-tuning, evaluation, etc. – and any new model must adhere to it. This way, experimentation with new models or libraries is low-friction: one can spin up a new component that conforms to the interface and plug it into the pipeline to test or deploy.

Another key to adaptability is avoiding hard-coded dependencies on specific frameworks or hardware. If the entire pipeline is written for only one library (say TensorFlow 2.x), it may become difficult to integrate innovations from another ecosystem (like a new PyTorch-based model). Using interop tools like ONNX as mentioned, or containerizing model runtimes, can decouple the system from a single framework. Cloud-native practices (Docker images, Kubernetes deployments) also aid here – they allow mixing and matching components possibly written in different stacks (e.g. one microservice might use PyTorch, another uses JAX) as long as they communicate via standard APIs. This heterogeneity can be a strength when adapting to the best tool for each job. Enterprise AI platforms such as those by Microsoft and Google are increasingly containerizing LLM components and leveraging technologies like Kubernetes to manage them, which naturally supports an evolving mix of models and services.

Adaptability also involves monitoring and iteration. A maintainable system includes observability to know when model performance is degrading or when new user needs are not met, so that new solutions can be integrated. In 2025, LLMOps (Large Model Ops) practices are being discussed (The State of Global LLM Inference: A 2025 Market ... - Inference.net), emphasizing continuous evaluation, logging of model decisions, and feedback loops. Designing the system with points where new models can be A/B tested or periodically retrained on fresh data will make it easier to incorporate improvements. For example, one could maintain a shadow deployment of a new model version receiving sample traffic to gauge its quality and resource usage before fully switching over – the architecture needs to support routing and comparing outputs. Some industry solutions (like OptLLM mentioned in blogs (Balancing LLM Costs and Performance: A Guide to Smart Deployment)) even automate choosing the cheapest model that meets a performance target for each query type, effectively orchestrating adaptability by dynamically selecting among available models. While such automation is cutting-edge, the underlying idea is that a truly adaptable system might not rely on a single static model at all, but rather a portfolio of models/experts that can be updated independently.

In summary, technical adaptability comes from architectural foresight: using modular, interface-driven design; staying agnostic to specific ML frameworks/hardware; and building in evaluation mechanisms. By following these principles, one can evolve an LLM system in step with the fast-moving AI landscape. New model releases, better algorithms, or changes in user requirements can be integrated as part of the normal maintenance cycle, rather than forcing a ground-up rebuild. The literature and industry examples consistently point to this decoupling and modularity as the way to “future-proof” LLM deployments (The Next Frontier of LLM Applications: Open Ecosystems and Hardware Synergy). As Hou et al. (2025) put it, the goal is to foster open, interoperable LLM ecosystems rather than closed, rigid solutions . An adaptable system not only extends its lifetime and ROI but also can capture opportunities by quickly leveraging the latest AI advancements as they emerge.

Conclusion

Designing an LLM-based system for adaptability and maintainability requires marrying solid software engineering practices with awareness of ML-specific challenges. Modular components with standard interfaces are the backbone of a maintainable architecture, enabling pieces to be improved or replaced independently. A flexible pipeline that can accommodate new models, whether from academic breakthroughs or industry releases, ensures the system stays relevant as the state-of-the-art evolves. At the same time, practical concerns of scalability and cost must be built into the design – through distributed computing strategies, optimized inference servers, autoscaling infrastructure, and efficient use of resources like quantization and smaller specialized models. The 2024–2025 research and industry developments highlight that these systems can be both high-performing and nimble: we see examples of decoupled multi-layer architectures , unified platforms hosting many models (Amazon Bedrock offers access to multiple generative AI models - Amazon Science), and frameworks that drastically cut inference costs (Welcome to LLMflation - LLM inference cost is going down fast ⬇️ | Andreessen Horowitz). By synthesizing these lessons, architects of LLM solutions can create systems that not only meet today’s needs but gracefully adapt to tomorrow’s innovations. In a field as fast-moving as generative AI, such foresight in design is indispensable for any enterprise looking to harness LLMs at scale in a sustainable way.