If your team is working with Large Language Models (LLMs), chances are you’ve already run into a familiar question: how do we know which model works best for our use case?
You might be testing different APIs, adjusting prompts, or running a few in parallel. But without a consistent way to track what’s working – and why – it gets messy fast. Models change, costs vary, outputs drift. And when visual input comes into play, things get even more complex.
LLMOps is about building structure around that process. Not in theory, but in practice – so teams can benchmark models, compare outputs, track prompts, and understand where performance is coming from.
This article looks at how LLMOps differs from traditional MLOps, why it matters when working with text and vision models, and what a real workflow can look like when you’re building or evaluating LLM-powered solutions.
What’s different from MLOps?
At a high level, MLOps focuses on training models with mainly structured data, meaning that you train the model based on training dataset including ground truth data. That’s not how most LLM-based systems work.
With LLMs, you often start with a pre-trained model and adapt it using prompts, a bit of context, maybe some retrieval or fine-tuning. For the majority of the companies you don’t need to build the model – you need to figure out how to get useful results from it.
That shifts the focus from training pipelines to interaction. And that brings up new questions:
- Which prompt version gives more reliable results?
- How much does output quality change with slight tweaks?
- How do we evaluate the answers in the first place?
In most MLOps workflows, these questions don’t really come up.
If you want to learn more about MLOps and how it supports the delivery of reliable, scalable AI solutions, take a look at our other article: Read more
Prompting, evaluating, and the limits of accuracy
Prompt engineering isn’t something you do once and move on – it requires ongoing adjustment and testing. Small wording changes can produce very different outputs. What worked last week might not hold up if the model updates or if the input slightly changes.
And then there’s evaluation. You’re not looking for a “correct” label. You’re looking for outputs that are relevant, coherent, on-topic, and safe. That’s harder to measure and often requires human input or tools that can score outputs more intelligently.
Without a process to version prompts and compare outputs side by side, it’s hard to tell whether you’re improving or just trying things at random.
Data looks different, too
In traditional machine learning, datasets are typically made up of clear input – output pairs, each with one correct answer. With LLMs, the structure is often more flexible – and more complex. There may be multiple valid responses. Some prompts work best with a few example completions for context. In other cases, the model needs to follow a full dialogue history to respond appropriately.
And in most cases, you’re not preparing data for training – you’re using it to evaluate how the model performs, or to support retrieval-based techniques like RAG.
As a result, data preparation isn’t about labeling in the traditional sense. It’s about creating realistic examples that reflect how the model will be used and tracking how well it handles those situations.
What LLMOps tools focus on
If you’ve worked with MLOps platforms before, you’re likely familiar with tools for experiment tracking, model registries, and feature stores.
LLMOps shifts the focus. Instead of managing training workflows, these tools help you understand and control how models behave during inference – where real usage happens.
Key areas include:
- Prompt versioning: Managing and comparing different prompts over time
- Monitoring: Tracking token usage, latency, cost, and failure rates
- Tracing: Visualizing complex interactions like multi-step flows or tool integrations
- Evaluation: Scoring outputs using test sets, LLM-as-a-judge methods, or human review
These tools aren’t limited to developers. They also support product teams, quality assurance (QA), and anyone responsible for maintaining quality and consistency across LLM-based systems.
Read more about how to observe and monitor LLMs in production in our article on Observability and Monitoring of LLMs: Read More
Why it matters for teams using LLMs
If you’re building with LLMs, you already know how fast things move. New models are released every few weeks. Performance changes. Pricing shifts. APIs evolve.
LLMOps helps teams keep up by:
- Making model comparisons easier
- Giving visibility into what’s working (and why)
- Supporting repeatable testing over time
- Creating clear criteria for when something is ready for production
You’re no longer guessing which prompt works best – you’re tracking it.
What about vision tasks?
Large Language Models (LLMs) are no longer limited to text. Newer models like GPT-4V, GPT-4o, Gemini, and others can process visual input and generate meaningful results based on it.That unlocks a wide range of practical business use cases, such as:
- Identifying defects in product images
- Estimating quantities or costs based on photos
- Verifying whether steps in a process were completed correctly
- Parsing documents through OCR
- Understanding room layouts or spaces for navigation tasks
These vision-based capabilities are expanding quickly, but most LLMOps tooling is still designed for text. If you’re working with Large Language Models (LLMs) in visual workflows, you’ll likely need a mixed approach – combining general-purpose MLOps tools with custom components tailored to vision-specific evaluation and monitoring.
A Practical workflow for benchmarking vision LLMs
Here’s what a typical workflow might look like when evaluating vision-capable models:
1. Store and version your data:
Use platforms like S3 or DVC to manage your image datasets. Keep track of file paths, tags, and version history to ensure consistency across experiments.
2. Choose your model backend:
You can self serve models (using tools like vLLM or Triton), deploy them in the cloud, or rely on commercial APIs. Each option has trade-offs in latency, cost, and control.
3. Trigger experiments consistently:
Start with notebooks for early testing, then move to CI/CD pipelines that automatically run benchmarks when models or datasets are updated.
4. Monitor what matters:
Track key metrics like token usage, response time, and error rates. These help you detect performance issues early and manage operating costs.
5. Use tracing for more complex applications:
If your system involves multiple steps or tools (such as RAG or agents), tracing helps you understand how outputs are generated and where things might be breaking.
6. Evaluate outputs using your own criteria:
Define clear evaluation methods for your use case – whether that means scoring rules, side-by-side comparisons, or LLM-as-a-judge approaches.
7. Generate reports to support decisions:
Use tools like Langfuse and Phoenix to compare results, track prompt changes, and document what’s improving over time.
Exemplary tools
Langfuse helps teams track experiments, version prompts, and generate summaries that highlight key performance metrics.
Phoenix is designed for managing dataset versions, visualizing runs, and setting up structured evaluations – even for complex, multimodal use cases.
Both tools can be extended with custom logic to support image-based workflows. Some configuration is required, but they provide a strong foundation for reliable, repeatable evaluations.
Final thoughts
If you’re building anything serious with Large Language Models (LLMs) – whether it’s document processing, question answering, or vision-based tasks – you need more than just a working model. You need a way to understand how it behaves, how it changes over time, and how it performs compared to other options. That’s exactly what LLMOps is designed to support.
About theBlue.ai
At theBlue.ai, we help companies bring AI into real use – built on stable workflows, measurable outcomes, and long-term value.
Our expertise goes beyond Large Language Models (LLMs). We support a wide range of AI projects, from traditional machine learning and computer vision to advanced use cases involving LLMs and multimodal models.
We offer end-to-end AI solutions tailored to your needs – whether that means building and deploying custom systems, supporting your internal teams with consulting, or running hands-on workshops to accelerate progress.
If you’re exploring how to apply AI in your business – or want to get more value out of what you’ve already started – we’re happy to help. Contact us right away. We are here to help you.
You can reach us at theblue.ai/contact.
