The rise of large language models (LLMs) has revolutionized natural language processing, opening the door to powerful applications across industries—from conversational agents and code generation to enterprise search and document summarization. But building, deploying, and maintaining LLM-powered systems at scale isn’t straightforward. That’s where LLMOps comes in.
LLMOps—short for large language model operations—encompasses the practices, tools, and workflows required to manage the lifecycle of LLMs in production. This includes everything from dataset curation, model fine-tuning, prompt engineering, and evaluation to deployment, monitoring, and iterative improvement. It’s a high-stakes, high-complexity process that can easily become chaotic without the right infrastructure.
This is where Weights & Biases comes in. From managing large-scale model training to prompt versioning, evaluation frameworks, and real-time model monitoring, W&B offers the essential building blocks for modern LLMOps. We build tools for everything from training your own LLMs and fine-tuning pre-trained models to RAG pipelines and building AI applications on top of foundation models.
In this article, we’ll explore how Weights & Biases empowers teams to confidently take LLMs from prototype to production—and keep them there.
What is LLMOps?
At its core, LLMOps brings structure and repeatability to three critical phases of the LLM lifecycle:
Model building and fine-tuning
While many teams use pretrained models via APIs, others need to fine-tune or train custom LLMs for better performance on domain-specific data, improved accuracy, or cost efficiency. LLMOps helps orchestrate this process by enabling:
- Dataset versioning and management to track changes in training data over time
- Experiment tracking to compare model runs, hyperparameters, and outcomes
- Scalable training infrastructure to handle the compute demands of large models
- Reproducibility and collaboration so that teams can work together seamlessly across iterations
W&B Models, used by nearly every major foundation model builder, included Meta, OpenAI, and more, was purpose-built for this very use case.
Prompt engineering and evaluation
For teams building on closed-source LLMs or deploying open models via inference, prompt design becomes a core part of the development cycle. LLMOps here ensures:
- Prompt version control, so you can track and iterate on generations systematically
- Automated evaluation workflows using metrics, human feedback, or model-based scoring
- Comparative analysis to test variations across different prompts, models, or datasets
Application development and deployment
LLMOps doesn’t stop at the model—it extends to the AI-powered applications that end users interact with. This includes:
- Integrating LLMs into production-grade systems, such as search, chat, copilots, and data agents
- Monitoring model behavior in real-world usage to detect drift, hallucinations, or latency issues
- Feedback loops and retraining pipelines to continuously improve performance based on real data
We built W&B Weave to help with the development of AI applications built with foundation models. Weave lets you track prompts, evaluate different LLM outputs, trace calls from agents, and more.
How Is LLMOps Different Than MLOps?
The differences between MLOps and LLMOps are caused by the differences in how we build AI products with classical ML models versus LLMs. The differences mainly affect data management, experimentation, evaluation, cost, and latency.
Data Management
In classical MLOps, we are used to data-hungry ML models. Training a neural network from scratch requires a lot of labeled data, and even fine-tuning a pre-trained model requires at least a few hundred samples. Although data cleaning is integral to the ML development process, we know and accept that large datasets have imperfections.
In LLMOps, fine-tuning is similar to MLOps. But prompt engineering is a zero-shot or few-shot learning setting. That means we have few but hand-picked samples.
Experimentation
In MLOps, experimentation looks similar whether you train a model from scratch or fine-tune a pre-trained one. In both cases, you will track inputs, such as model architecture, hyperparameters, and data augmentations, and outputs, such as metrics.
But in LLMOps, the question is whether to prompt engineer or to fine-tune.Although fine-tuning will look similar in LLMOps to MLOps, prompt engineering requires a different experimentation setup including management of prompts.
Evaluation
In classical MLOps, a model’s performance is evaluated on a hold-out validation set with an evaluation metric. Because the performance of LLMs is more difficult to evaluate, currently organizations seem to be using A/B testing.
Cost
While the cost of traditional MLOps usually lies in data collection and model training, the cost of LLMOps lies in inference. Although we can expect some costs from using expensive APIs during experimentation, Chip Huyen shows that the cost of long prompts is in inference.
Latency
Another concern respondents mentioned in the LLM in production survey was latency. The completion length of an LLM significantly affects latency. Although latency concerns have to be considered in MLOps as well, they are much more prominent in LLMOps because this is a big issue for the experimentation velocity during development and the user experience in production.
LLMOps for fine-tuning
Fine-tuning large language models (LLMs) allows organizations to tailor general-purpose models to specific domains, improving performance on targeted tasks and aligning outputs with brand voice, regulatory constraints, or proprietary knowledge. However, managing fine-tuning in production requires more than just training a model—it demands robust operational practices. That’s where LLMOps comes in.
Key aspects of LLMOps for fine-tuning include:
Data Management
Fine-tuning is only as good as the data it’s based on. LLMOps practices ensure that training datasets are versioned, labeled, and curated with clear lineage, making it easier to trace model behavior back to specific data sources.
Experiment tracking
Each fine-tuning run should be tracked, including hyperparameters, datasets, model checkpoints, and evaluation results. Tools like Weights & Biases or MLflow enable repeatability and help teams collaborate effectively.
Evaluation pipelines
LLMs are notoriously hard to evaluate. LLMOps introduces automated evaluation pipelines using both quantitative metrics (e.g., perplexity, BLEU) and qualitative ones (e.g., human feedback, hallucination rates) to ensure model quality improves with each iteration.
Model registry and deployment
After training, the model needs to be stored, versioned, and served reliably. LLMOps frameworks support smooth handoffs between experimentation and deployment, integrating with CI/CD pipelines, A/B testing setups, and rollback mechanisms.
Governance and compliance
With growing regulatory scrutiny, tracking how and why a model was fine-tuned is critical. LLMOps provides the audit trails and governance mechanisms needed for compliance in industries like finance, healthcare, and legal.
LLMOps in retrieval-augmented generation (RAG) pipelines
While fine-tuning helps embed task-specific behavior into an LLM, it isn’t always the most efficient or scalable approach—especially when dealing with frequently changing data. That’s where Retrieval-Augmented Generation (RAG) comes in.
RAG augments an LLM with access to external knowledge sources (e.g., databases, vector stores, documents). Instead of retraining the model every time your knowledge base changes, RAG systems retrieve relevant information at runtime and feed it into the LLM’s context window. This enables up-to-date, accurate, and contextually rich responses without modifying the model weights.
LLMOps for RAG focuses on:
Prompt engineering and templates
In RAG, the prompt is the glue between retrieved data and the LLM. LLMOps tools like W&B Weave manage and test prompt templates, versioning them alongside model and data changes.
Observability and evaluation
It’s not just about the model; RAG pipelines need to be observable end-to-end. Metrics like retrieval accuracy, latency, and hallucination rates must be tracked, along with user feedback.
In many cases, fine-tuning and RAG are complementary: fine-tuning for style, tone, or reasoning patterns; RAG for real-time, factual grounding. Together, they form the operational backbone of modern LLM deployments—and LLMOps makes that backbone stable, scalable, and secure.
Weights & Biases has a full suite of tools for both RAG pipelines and fine-tuning. Our Models product shines for both, allowing you to track every experiment, how they affect accuracy metrics, and makes debugging far easier.
LLMOps for building AI agent applications
AI agents represent the next evolution in how LLMs are used—moving from passive completion engines to autonomous or semi-autonomous systems capable of reasoning, planning, and taking actions. But with this increased sophistication comes a new layer of complexity in development and operations. LLMOps provides the scaffolding to manage that complexity and bring agentic systems safely and reliably into production.
Some considerations for LLMOps in AI agent applications:
Agent architecture management
AI agents typically rely on modular frameworks—think tools, memory, environments, and planners. LLMOps practices ensure each component is: versioned and composable, so developers can iterate and swap parts easily; observable and debuggable, allowing you to inspect the decision tree or reasoning chain when things go wrong; and integrated with CI/CD to validate that changes in one module don’t introduce regressions in another.
Prompt and tool orchestration and monitoring
Agents don’t just use one prompt—they dynamically build and adapt prompts based on their goals. You’ll want to keep track of prompt versioning and testing pipelines that track changes and outcomes over time; tooling registries to manage external APIs, internal databases, calculators, or function-calling plugins the agent can invoke; and safety filters and constraints, ensuring agents don’t act outside their bounds (e.g., making unauthorized transactions or writing to sensitive systems).
Weave is a vital tool to track all of this. Our Trace feature lets you understand which tools your agent application is calling (and in which order) so you can better debug performance and fix your prompts. Our Guardrails feature lets you monitor safety, bias, and a whole host of other valuable metrics as you build, ensuring your AI applications behave the way you expect.
Evaluation and monitoring
Agent behavior is dynamic and non-deterministic. Evaluation is no longer about single prompt completions but about multi-step workflows. LLMOps pipelines test agent scenarios across decision trees so logging tools like Weave that capture traces, action logs, and outcomes for reproducibility are necessary to build the best applications. Weave also lets you gather user feedback from end-users or experts and loop those judgments back in for improvement.
On the evaluations side, you want to not only evaluate your applications performance but also how well different models perform at either different steps or across your whole pipeline. Weave Evaluations unlocks these insights, allowing you a holistic view of model performance (latency, token use, accuracy, etc.) and lets you drill into specific examples to uncover novel behavior.
Get started
Regardless of what you’re building, Weights & Biases has tools for improving your LLMOps practices. It’s free to sign up and get started. You can also feel free to reach out and we’d love to learn how we can work with you.