Many advisory and expert system tools in your smartphone commonly rely on Large Language Models (LLMs) trained to understand and chat in a human-like manner, serving answers or simply being helpful.
But from DevOps’ point of view, these AI systems are troublesome because of many aspects:
You can use a dedicated LLMOps (Large Language Model Operations) practice to address these challenges. This practice also covers connecting with interfaces or third-party tools and aligning with business goals. It refers to the best way to manage, monitor, secure, and optimize these massive models and provide reliable feedback for the training algorithm behind them.
Consider it similar to maintaining a self-driving car. Without ongoing updates, adjustments, and safety inspections, it could operate for just a few minutes before running into problems.
LLMOps ensures these AI systems remain efficient, scalable, and reliable throughout their lifecycle.
What is LLMOps in detail?
The scope of LLMOps’ influence in building generative AI applications. (Source)
LLM ops (or operations) accelerate the development, deployment, and management of AI models throughout their entire lifecycle. Careful about everything, starting from the hardware arrangement, the underlayer, and the data pipelines organization, LLMOps is an error-cleansing activity and development and deployment “bushido” for a whole AI organization.
The organizational model for a team working over LLM (Source)
The goal of implementing LLMOps is not to hire one LLMOps rock star and wait for the magic to begin. It’s about building an even distribution of responsibilities between your dev and business teams to create a new culture shift effect that will make all deployment strategies and applications of LLMOps tools efficient. Here’s the table with an approximate allocation of responsibilities between roles in your organization:
Role
Key focus areas
Responsibilities
ML engineers
Ensuring models are performant, scalable, and aligned with use cases.
Design, train, and fine-tune LLMs for specific use cases.
Optimize model architecture and implement efficient training techniques.
Collaborate with data scientists to improve accuracy and performance.
Data engineers
Providing high-quality, well-structured data for model training and updates.
Manage data ingestion, storage, and preprocessing pipelines.
Develop data versioning and governance frameworks.
MLOps & LLMOps engineers
Ensuring LLMs run efficiently in production with continuous monitoring and updates.
Build and maintain automated pipelines for training, deployment, and monitoring.
Optimize inference speed, cost efficiency, and scalability.
Implement drift detection, bias mitigation, and performance monitoring systems.
AI/ML researchers
Exploring new techniques to improve model efficiency, adaptability, and ethical integrity.
Experiment with novel model architectures and techniques (e.g., retrieval-augmented generation).
Improve prompt engineering strategies for better LLM responses.
Investigate and mitigate biases, hallucinations, and ethical concerns.
DevOps & Cloud engineers
Making sure LLMs scale efficiently and run smoothly in production.
Manage cloud infrastructure, GPUs, TPUs, and Kubernetes clusters.
Implement CI/CD pipelines for seamless model updates and rollbacks.
Ensure system reliability, security, and compliance.
Security & Compliance teams
Preventing unauthorized access, ensuring compliance, and mitigating AI-related risks.
Enforce AI governance policies and regulatory adherence (GDPR, HIPAA, SOC 2).
Implement encryption, access controls, and threat detection mechanisms.
Conduct audits to ensure ethical AI practices and data privacy compliance.
Product managers & AI strategists
Ensuring LLM development aligns with company goals and user needs.
Define success metrics and oversee the continuous improvement cycle.
Manage stakeholder expectations and coordinate cross-functional teams.
Responsible AI & Ethics committees
Ensuring AI operates fairly, transparently, and without harmful bias.
Develop policies to prevent misuse and mitigate ethical risks.
Collaborate with regulators and industry groups on AI safety standards.
TL;DR for the table
Building the AI → ML engineers and researchers, responsible for model training, fine-tuning.
Automating & Deploying → LLMOps and DevOps engineers care about pipelines, deployment, monitoring, and automation.
Feeding the AI → Data engineers responsible for global data management and governance.
Securing & Governing → Security teams & Ethics committees care about regulation, privacy, and bias mitigation.
Ensuring business fit → Product managers & AI strategists, building the strategy, adoption, and improving user experience.
Key components of LLMOps
So, how do all these people and their actions participate in the LLM pipeline? Here’s the primitive schema of a typical one:
LLMOps is valid everywhere, from preparing data for the model to monitoring its incorporation into the AI tool. DevOps's leading powers, such as resource management, scaling, cost optimization, and recovery features, make any AI system reliable.
What is LLMOps? For us, it’s the only proper set of activities to build an efficient and capable LLM that can handle market competition.
Both practices share foundational principles, but LLMOps is much more “loaded,” if we may say so. Due to the scale, adaptability requirements, and unpredictability of LLMs, the orchestrating solutions applied to the infrastructure, data, and code levels are way more complex and saturated with responsibility.
Key distinctions of specialized LLM MLOps include:
Model size and computational demands
LLMs require High-Performance Computing (HPC) systems equipped with GPUs or TPUs to handle efficient parallel processing, while traditional ML models can operate efficiently on CPUs. Additionally, the training process of LLMs involves managing long input sequences, which further increases memory usage and computational load. The transformer architecture, commonly used in LLMs, scales with the length of the input text and requires more memory and processing power for longer sequences.
This feature makes LLM deployment more expensive and demands specialized infrastructure, including distributed computing and caching strategies to optimize inference costs.
Continuous adaptation instead of static training
MLOps typically involve training models with structured data, deploying them, and monitoring them for drift. LLMOps, on the other hand, involves continuous fine-tuning, reinforcement learning from human feedback (RLHF), and real-time adaptation to keep responses relevant. Thus, ongoing governance and oversight are critical.
Unpredictability and a must-have risk management
Unlike conventional ML models, which provide deterministic outputs within a defined scope, LLMs generate open-ended responses that can be biased, misleading, or harmful. LLMOps must integrate rigorous guardrails, such as prompt engineering, content moderation, and real-time risk assessment, to prevent reputational and compliance risks.
While MLOps optimizes feature engineering and model hyperparameters, LLMOps shifts focus to optimizing prompts, system instructions, and retrieval-augmented generation (RAG). This introduces a new layer of operational complexity, requiring domain expertise to shape model behavior dynamically without retraining.
Data privacy and regulatory twist
Traditional ML models often work with structured, proprietary data, where privacy risks are manageable through access controls. LLMs, however, may process user-generated inputs dynamically, creating potential legal and ethical risks. This detail requires robust anonymization, access control, and compliance with legal frameworks like GDPR or HIPAA.
Suppose a company plans to deploy a customer support chatbot powered by GPT-4. They need LLMOps for:
Managing version updates;
Lowering latency under high traffic;
Fine-tuning responses based on user feedback.
This plan involves a couple of repetitive LLMOps routines. The roadmap for the implementation of such an idea into life may look as follows:
Phase 1: Make a plan for LLM implementation
We’re going to start with the definition of objectives, use cases, and resource allocation.
Identify business goals: Determine how the LLM will improve customer support (e.g., reduce response time and enhance personalization).
Choose the right model and hosting approach:
API-based model (e.g., OpenAI) → Optimize API calls for cost-efficiency.
Self-hosted model → Deploy using Kubernetes, Docker, or cloud-based services (e.g., AWS SageMaker, Vertex AI).
Define success metrics and how to track them. Set KPIs such as accuracy, response latency, and customer satisfaction scores.
Phase 2: Get your data ready
Collect, clean, and prepare training data for the model.
Gather customer support data: Extract FAQs, chat logs, and past customer interactions.
Anonymize and label data: Remove PII (Personally Identifiable Information) and classify data for supervised fine-tuning.
Augment data for robustness: Introduce diverse scenarios (e.g., handling slang, multilingual queries).
Develop a data versioning strategy. Track dataset changes using DVC (Data Version Control) or LakeFS.
Phase 3: Prepare the model
Let’s train or customize the model for better domain relevance.
Fine-tune the model (if necessary): Use techniques like LoRA (Low-Rank Adaptation) or full fine-tuning on domain-specific data.
Implement prompt engineering: Optimize system prompts for better responses without retraining.
Test across scenarios: Evaluate the model’s performance in various support situations (e.g., handling complaints vs. FAQs).
Phase 4: Deploy
Now, we’ll set up scalable, reliable, and cost-efficient deployment.
Follow the selected deployment method (from the Phase 1).
Set up an inference pipeline: Use optimizations like model quantization, caching responses, and GPU acceleration.
Ensure scalability: Implement auto-scaling based on traffic spikes.
Phase 5: Monitor everything
It’s a necessity to monitor and improve model performance continuously.
Implement logging & analytics: Track response latency, API usage, and customer interactions.
Detect model drift: Regularly analyze changes in customer queries and retrain as needed.
Enable real-time feedback loops: Collect user ratings and fine-tune responses accordingly.
Apply monitoring tools: Tools like Prometheus, Grafana, and LangSmith help track operational performance.
Phase 6: Make AI adhere to security and compliance requirements
It’s time to ensure responsible AI use and regulatory compliance.
Enforce data privacy rules: Align with GDPR, CCPA, and other regulatory requirements.
Prevent bias and hallucinations: Use adversarial testing and regular audits.
Implement RBAC: Restrict access to model endpoints and logs.
Phase 7: Optimize iteratively
Last but not least, we’ll work on efficiency, reducing costs, and preparing for emerging needs.
Automate model retraining pipelines: Set up CI/CD for fine-tuning based on new data.
We'll optimize costs using model compression, distillation, or hybrid models (e.g., switching between smaller and larger models based on query complexity).
Enhance response personalization: Integrate RAG to provide more context-aware answers.
Share with us your LLM-powered idea and see us implementing it
It optimizes inference costs through caching, model quantization, and dynamic scaling, preventing unnecessary GPU overuse. Automated monitoring and drift detection reduce silent model degradation, ensuring responses remain relevant without manual oversight.
Data versioning and lineage tracking allow controlled improvements while maintaining compliance, which is essential for regulated industries. Prompt engineering and fine-tuning automation enable rapid adaptation to market or business changes without retraining from scratch.
Moreover, governance controls mitigate risks like AI hallucinations, security breaches, and compliance violations, securing enterprise-grade AI deployment.
LLMOps transforms raw model capabilities into a scalable, cost-effective, and continuously improving AI system, directly impacting ROI and business agility.
Best practices for LLMOps
We will summarize some of the best practices mentioned in this article for you to navigate.
Model versioning: Use version control for models and data (e.g., DVC) to track changes.
Continuous retraining: Automate retraining pipelines with CI/CD for up-to-date models.
Latency optimization: Leverage model quantization, caching, and GPU acceleration for faster inference.
Cost management: Implement cost-aware APIs, batch processing, and resource scaling.
Prompt engineering: Continuously fine-tune prompts to improve output without full retraining.
Scalable infrastructure: Deploy models on Kubernetes or cloud services for auto-scaling and high availability.
PredictKube Case Study
Originally developed for PancakeSwap to manage 158 billion monthly requests,
PredictKube optimized traffic prediction and resource scaling. The AI-driven
solution proved so effective that it later evolved into an independent
product.
Before
Overprovisioned infrastructure leading to excessive cloud costs
Frequent latency spikes during traffic surges
Inefficient manual scaling, unable to predict load
Challenges in handling unpredictable traffic growth
After
30% reduction in cloud costs through proactive, AI-based autoscaling
Reduced peak response time by 62.5x
Fully automated scaling with up to 6-hour traffic forecasts
Scalable infrastructure that adapts to traffic growth and ensures stability
The shorter introduction would be useless, so we’re glad you’ve read this far. Here’s the list of sources we recommend for your self-education in this domain:
If you’re comfortable with the theory, browse community forums for specific knowledge. For example, r/MachineLearning discusses strategies for efficient resource allocation, including cloud-based solutions and hardware accelerators. The LLMOps.space community provides resources on developing customized evaluation frameworks tailored to the specific tasks and objectives of LLMs.
In a recent MLOps.community podcast, experts discussed the significance of adaptive learning rates and the integration of transfer learning to enhance model performance.
