Top 40+ LLMOps Tools & Compare them to MLOPs in 2025

The number of large language models (LLMs) has grown significantly since 2019 due to their diverse applications (Figure 1). Yet, creating a new foundation model can cost up to $90 million, while fine-tuning existing models ranges from $100,000 to $1 million.¹ These expenses stem from computational demands, data processing, and R&D efforts.

LLMOps tools help reduce these costs by streamlining LLM management. Discover LLMOps tools and compare the top players:

Note that list only includes some top examples with 500+ GitHub stars for each category, since there are various open source libraries, tools and frameworks.

LLMOps Landscape

There are 40+ tools that claim to be LLMOps solutions, which can be evaluated under 6 main categories:

1. LLMOps Platforms

These are either designed specifically for LLMOps or are MLOps platforms that started offering LLMOps capabilities. They include features that allow carrying out these operations on LLMs:

• Finetuning
• Versioning
• Deploying

These LLM platforms can offer different levels of flexibility and ease of use:

• No-code LLM platforms: Some of these platforms are no-code and low-code, which facilitate LLM adoption. However, these tools typically have limited flexibility.
• Code-first platforms: These platforms target machine learning engineers and data scientists. They tend to offer a higher level of flexibility.

LLMOps platforms can be examined under these categories:

1. MLOps tools & frameworks

Some MLOps platforms offer LLMOps toolkits. Machine Learning Operations (MLOps) manages and optimizes the end-to-end machine learning lifecycle. Since LLMs are also machine learning models, MLOps vendors are naturally expanding into this domain.

2. Data and cloud platforms

Data or cloud platforms are starting to offer LLMOps capabilities that allow their users to leverage their own data to build and finetune LLMs. For example, Databricks acquired MosaicML for $1.3 billion.³

Cloud platforms

Cloud leaders Amazon, Azure and Google have all launched their LLMOps offering which allows users to deploy models from different providers with ease

3. LLMOPs frameworks

This category includes tools that exclusively focus on optimizing and managing LLM operations. The table below shows the Github stars, B2B reviews and average B2B score from B2B review pages (Trustradius, Gartner & G2) for some of these LLMOps tools:

Here is a brief explanation for each tool in alphabetical order:

Comet

Comet streamlines the ML lifecycle, tracking experiments and production models. Suited for large enterprise teams, it offers various deployment strategies. It supports private cloud, hybrid, and on-premise setups.

DeepLake

Deep Lake combines the capabilities of Data Lakes and Vector Databases to create, refine, and implement high-quality LLMs and MLOps solutions for businesses. Deep Lake allows users to visualize and manipulate datasets in their browser or Jupyter notebook, swiftly accessing different versions and generating new ones through queries, all compatible with PyTorch and TensorFlow.

Deepset AI

Deepset AI is a comprehensive platform that allows users to integrate their data with LLMs to build and deploy customized LLM features in their applications. Deepset supports Retrieval-augmented generation (RAG) and Enterprise knowledge search, as well.

Lamini AI

Lamini AI provides an easy method for training LLMs through both prompt-tuning and base model training. Lamini AI users can write custom code, integrate their own data, and host the resulting LLM on their infrastructure.

Nemo by Nvidia

Nvidia offers an end-to-end, cloud-native enterprise framework to develop, customize, and employ generative AI models and LLM applications. The framework can execute various tasks required to train LLMs, such as token classification, prompt learning and question answering.

Snorkel AI

Snorkel AI empowers enterprises to construct or customize foundation models (FMs) and large language models (LLMs) to achieve remarkable precision on domain-specific datasets and use cases. Snorkel AI introduces programmatic labelling, enabling data-centric AI development with automated processes.

Titan ML

TitanML is an NLP development platform that aims to allow businesses to swiftly build and implement smaller, more economical deployments of large language models. It offers proprietary, automated, efficient fine-tuning and inference optimization techniques. This way, it allows businesses to create and roll out large language models in-house.

Valohai

Valohai streamlines MLOps and LLMs, automating data extraction to model deployment. It can store models, experiments, and artefacts, making monitoring and deployment easier. Valohai creates an efficient workflow from code to deployment, supporting notebooks, scripts, and Git projects. 

Zen ML

ZenML primarily focuses on machine learning operations (MLOps) and the management of the machine learning workflow, including data preparation, experimentation and model deployment.

4. LLMs

Some LLM providers, especially OpenAI, are also providing LLMOps capabilities to fine-tune, integrate and deploy their models.

2. Integration frameworks

These tools are built to facilitate developing LLM applications such as document analyzers, code analyzers, chatbots etc.

3.) Vector databases (VD)

VDs store high-dimensional data vectors, such as patient data covering symptoms, blood test results, behaviors, and general health. Some VD software like DeepLake can facilitate LLM operations.

4.) Fine-tuning tools

Fine-tuning tools are frameworks, or platforms for fine-tuning pre-trained models. These tools provide a streamlined workflow to modify, retrain, and optimize pre-trained models for natural language processing, computer vision, and more tasks.

Some libraries are also designed for fine-tuning, such as Hugging Face Transformers, PyTorch, and TensorFlow.

5.) RLHF tools

Reinforcement learning from human feedback, or RLHF, is a way for AI to learn the best actions by listening to human input. Typically, Reinforcement learning includes an RL algorithm to learn by interacting with the environment and receiving rewards or penalties based on its actions.

In contrast, RLHF tools (e.g. Clickworker or Appen) include human feedback in the learning loop. RLHF can be useful to: 

• Enhance LLM fine-tuning by large data labeling
• Implement AI governance by reducing biases in LLM responses and moderating content
• Customize model
• Improve contextual understanding.

6.) LLM testing tools

LLM testing tools evaluate and assess LLMs by testing model performance, capabilities, and potential biases in various language-related tasks and applications, such as natural language understanding and generation. Testing tools may include: 

• Testing frameworks
• Benchmark datasets
• Evaluation metrics.

7.) LLM monitoring and observability

LLM monitoring and observability tools ensure their proper functioning, user safety, and brand protection. LLM monitoring includes activities like:

1. Functional monitoring: Keeping track of factors like response time, token usage, number of requests, costs and error rates.
2. Prompt monitoring: Checking user inputs and prompts to evaluate toxic content in responses, measure embedding distances, and identify malicious prompt injections.
3. Response monitoring: Analyzing to discover hallucinatory behavior, topic divergence, tone and sentiment in the responses.

Tools for secure and complaint LLMs

Some LLMOps integrate with AI governance and LLM security technologies to ensure safe, unbiased, and ethical LLM deployment and operation. Check out more on these:

• Compare Top 25 AI Governance Tools: A Vendor Benchmark
• Compare 20 LLM Security Tools & Open-Source Frameworks

Disclaimer about current categorization approach

We recognize different ways to categorize these tools. Some include technologies like containerization or edge computing, which, while useful for LLM performance, are not essential for model design or monitoring, so we exclude them.

Traditional categorizations focus on license type or pre-trained models. While relevant, we believe functionality is more critical. For instance, whether an LLM is open source affects fine-tuning, but since most LLMOps users don’t modify the code, open source status is less important for these tools.

Which LLMOps tool is the best choice for your business?

We now provide relatively generic recommendations on choosing these tools. We will make these more specific as we explore LLMOps platforms in more detail and as the market matures.

Here are a few steps you must complete in your selection process:

1. Define goals: Clearly outline your business goals to establish a solid foundation for your LLMOps tool selection process. For example, if your goal requires training a model from scratch vs fine-tuning an existing model, this will have important implications to your LLMOps stack.
2. Define requirements: Based on your goal, certain requirements will become more important. For example, if you aim to enable business users to use LLMs, you may want to include no code in your list of requirements.
3. Prepare a shortlist: Consider user reviews and feedback to gain insights into real-world experiences with different LLMOps tools. Rely on this market data to prepare a shortlist.
4. Compare functionality: Utilize free trials and demos provided by various LLMOps tools to compare their features and functionalities firsthand.

What is LLMOps?

LLMOPS stands for Large Language Model Operations, denoting a strategy or system to automate and refine the AI development pipeline through the utilization of expansive language models. LLMOPs tools facilitate the continuous integration of these substantial language models as the underlying backend or driving force for AI applications. 

Key components of LLMOps:

1. Selection of a foundation model: A starting point dictates subsequent refinements and fine-tuning to make foundation models cater to specific application domains.
2. Data management: Managing extensive volumes of data becomes pivotal for accurate language model operation.
3. Deployment and monitoring model: Ensuring the efficient deployment of language models and their continuous monitoring ensures consistent performance.
   • Prompt engineering: Creating effective prompt templates for improved model performance.
   • Model monitoring: Continuous tracking of model outcomes, detection of accuracy degradation, and addressing model drift.
4. Evaluation and benchmarking: Rigorous evaluation of refined models against standardized benchmarks helps gauge the effectiveness of language models.
   • Model fine-tuning: Fine-tuning LLMs to specific tasks and refining models for optimal performance.

How Is LLMOps Different Than MLOps?

LLMOps is specialized and centred around utilising large language models. At the same time, MLOps has a broader scope encompassing various machine learning models and techniques. In this sense, LLMOps are known as MLOps for LLMs. Therefore, these two diverge in their specific focus on foundational models and methodologies: 

Computational resources

Training and deploying large language models require extensive computations on large datasets, often using specialized hardware like GPUs for faster processing. Access to such resources is crucial for effective model training and deployment. Additionally, managing inference costs highlights the importance of model compression and distillation techniques to reduce resource usage while maintaining performance.

Transfer learning

Unlike conventional ML models built from the ground up, LLMs frequently commence with a base model, fine-tuned with fresh data to optimize performance for specific domains. This fine-tuning facilitates state-of-the-art outcomes for particular applications while utilizing less data and computational resources.

Human feedback 

Advancements in training large language models are attributed to reinforcement learning from human feedback (RLHF). Given the open-ended nature of LLM tasks, human input from end users holds considerable value for evaluating model performance. Integrating this feedback loop within LLMOps pipelines simplifies assessment and gathers data for future model refinement.

Hyperparameter tuning

While conventional ML involves hyperparameter tuning primarily to enhance accuracy, LLMs introduce an added dimension of reducing training and inference costs. Adjusting parameters like batch sizes and learning rates can substantially influence training speed and cost. Consequently, meticulous tuning process tracking and optimisation remain pertinent for both classical ML models and LLMs, albeit with varying focuses.

Performance metrics

Traditional ML models rely on well-defined metrics such as accuracy, AUC, and F1 score, which are relatively straightforward to compute. In contrast, evaluating LLMs entails an array of distinct standard metrics and scoring systems—like bilingual evaluation understudy (BLEU) and Recall-Oriented Understudy for Gisting Evaluation (ROUGE)—that necessitate specialized attention during implementation.

Prompt engineering

Models that follow instructions can handle intricate prompts or instruction sets. Crafting these prompt templates is critical for securing accurate and dependable responses from LLMs. Effective, prompt engineering mitigates the risks of model hallucination, prompt manipulation, data leakage, and security vulnerabilities.

Constructing LLM pipelines

LLM pipelines string together multiple LLM invocations and may interface with external systems such as vector databases or web searches. These pipelines empower LLMs to tackle intricate tasks like knowledge base Q&A or responding to user queries based on a document set. In LLM application development, the emphasis often shifts towards constructing and optimizing these pipelines instead of creating novel LLMs. 

Additionally, large multimodal models extend these capabilities by incorporating diverse data types, such as images and text, enhancing the flexibility and utility of LLM pipelines 

LLMOPS vs MLOPS: Pros and Cons

While deciding which one is the best practice for your business, it is important to consider benefits and drawbacks of each technology. Let’s dive deeper into the pros and cons of both LLMOps and MLOps to compare them better:

LLMOPS Pros

1. Simple development: LLMOPS simplifies AI development significantly compared to MLOPS. Tedious tasks like data collection, preprocessing, and labeling become obsolete, streamlining the process.
2. Easy to model and deploy: The complexities of model construction, testing, and fine-tuning are circumvented in LLMOPS, enabling quicker development cycles. Also, deploying, monitoring, and enhancing models are made hassle-free. You can leverage expansive language models directly as the engine for your AI applications.
3. Flexible and creative: LLMOPS offers greater creative latitude due to the diverse applications of large language models. These models excel in text generation, summarization, translation, sentiment analysis, question answering, and beyond.
4. Advanced language models: By utilizing advanced models like GPT-3, Turing-NLG, and BERT, LLMOPS enables you to harness the power of billions or trillions of parameters, delivering natural and coherent text generation across various language tasks.

LLMOPS Cons

1. Limitations and quotas: LLMOPS comes with constraints such as token limits, request quotas, response times, and output length, affecting its operational scope.
2. Risky and complex integration: As LLMOPS relies on models in beta stages, potential bugs and errors could surface, introducing an element of risk and unpredictability. Also, Integrating large language models as APIs requires technical skills and understanding. Scripting and tool utilization become integral components, adding to the complexity.

MLOPS Pros

1. Simple development process: MLOPS streamlines the entire AI development process, from data collection and preprocessing to deployment and monitoring.
2. Accurate and reliable: MLOPS ensures the accuracy and reliability of AI applications through standardized data validation, security measures, and governance practices.
3. Scalable and robust: MLOPS empowers AI applications to handle large, complex data sets and models seamlessly, scaling according to traffic and load demands.
4. Access to diverse tools: MLOPS provides access to many tools and platforms like cloud computing, distributed computing, and edge computing, enhancing development capabilities.

MLOPS Cons

1. Complex to deploy: MLOPS introduces complexity, demanding time and effort across various tasks like data collection, preprocessing, deployment, and monitoring.
2. Less flexible and creative: While versatile, MLOPS confines the application of machine learning to specific purposes, often employing less sophisticated models than expansive language models.

Which one to choose?

Choosing between MLOps and LLMOps depends on your specific goals, background, and the nature of the projects you’re working on. Here are some instructions to help you make an informed decision:

1. Understand your goals: Define your primary objectives by asking whether you focus on deploying machine learning models efficiently (MLOps) or working with large language models like GPT-3 (LLMOps). 

2. Project requirements: Consider the nature of your projects by checking if you primarily deal with text and language-related tasks or with a wider range of machine learning models. If your project heavily relies on natural language processing and understanding, LLMOps is more relevant.

3. Resources and infrastructure: Think about the resources and infrastructure you have access to. MLOps may involve setting up infrastructure for model deployment and monitoring. LLMOps may require significant computing resources due to the computational demands of large language models.

4. Evaluate expertise and team composition by determining if your expertise lies in machine learning, software development, or both. Do you have specialists in machine learning, DevOps, or both? MLOps requires collaboration between data scientists, software engineers, and DevOps professionals for deploying and managing machine learning models. LLMOps focuses on working with large language models and integrating them into applications.

5. Industry and use cases: Explore the industry you’re in and the specific use cases you’re addressing. Some industries may heavily favour one approach over the other. LLMOps might be more relevant in industries like content generation, chatbots, and virtual assistants.

6. Hybrid approach: Remember that there’s no strict division between MLOps and LLMOps. Some projects may require a combination of both systems.

Further reading

Explore more on LLMs, MLOps and AIOps by checking out our articles:

• MLOps Tools & Platforms Landscape: In-Depth Guide
• 15 Best AiOps Platforms: Streamline IT Ops with AI
• ChatGPT AIOps in IT Automation: 8 Powerful Examples

If you still have questions about LLMOps tools and landscape, we would like to help:

Find the Right Vendors

External sources

• 1. “The CEO’s Roadmap on Generative AI” BCG. March 2023. Revisited August 11, 2023.
• 2. “A Survey of Large Language Models.” Github. March 2023. Revisited August 11, 2023.
• 3. Databricks Signs Definitive Agreement to Acquire MosaicML, a Leading Generative AI Platform - Databricks.
• 4. Open-Source LLM Evaluation Platform | Opik by Comet.
• 5. Introducing Deep Lake, the Data Lake for Deep Learning.
• 6. NVIDIA NeMo Framework - NVIDIA Docs. NVIDIA Docs
• 7. The Snorkel AI Blog | Home. Snorkel AI
• 8. Valohai | The Scalable MLOps Platform.