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Comparing 10+ LLMOps Tools: A Comprehensive Vendor Benchmark

Comparing 10+ LLMOps Tools: A Comprehensive Vendor BenchmarkComparing 10+ LLMOps Tools: A Comprehensive Vendor Benchmark

The number of large language models (LLMs) has been increasing since 2019 due to the models’ extensive application areas and capabilities (See Figure 1).

Yet, the estimates show that designing a new foundation model can cost up to $90 million while fine-tuning or enhancing existing large language models can cost $1 million to $100 thousand.1 These costs result from:

  • Computational costs like hardware usage for training runs
  • Data gathering and labelling costs
  • Engineering and R&D costs
The graph shows the names of the LLMs since 2019.
Figure 1: The increasing number of LLMs since 20192

LLMOps tools can reduce these costs by facilitating LLM management. However, LLMOps is a relatively recent solution and most business leaders are not aware of the leading players in this market. This article explains the LLMOps market and compares available tools.

LLMOps Landscape

There are 20+ 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.

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 deep lake 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.

Compare the top LLMOps platforms in 2023

In this section, we focus on LLMOps platforms and excluded integration frameworks and other tools. LLMOps platforms can be examined in these categories:

1. MLOps platforms

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. LLM platforms

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

3. 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

4. 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:

LLMOps ToolsGithub StarsNumber of B2B Reviews*Average Review Score**
Nemo by Nvidia7,900NANA
Deep Lake6,600NANA
Fine-Tuner AI6,000NANA
Snorkel AI5,500NANA
Zen ML3,000NANA
Lamini AI2100NANA
Comet54NANA
Titan ML47NANA
Deepset AI6NANA
ValohaiNot open source204.9

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

  1. 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.
Here is Comet platform, as an example of an AIOps tool.
Figure 2: Comet LLMops platform 4
  1. Deep Lake: 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.
  2. 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.
  3. 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.
  4. 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.
  5. 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.
Snorkel AI is an LLMOps tool that offers to fine-tune model, prompt builder and zero and few shot learnings to train LLMs.
Figure 3: Snorkel AI LLMOps platform 5

6. 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.

7. 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. 

8. 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.

Disclaimer about current categorization approach

We are aware that there are different approaches to categorize these tools. For instance, some vendors include other technologies that can help large language model development in this landscape, such as containerization or edge computing. However, such technologies are not built for designing or monitoring models, even though they can be paired with LLMOps tools to improve model performance. Therefore, we excluded these tools.

A more classical approach categorizes tools based on licence type (e.g. open source or not) or whether the tool provides pre-trained models or not. While these are relevant categorizations, we think they are less critical than other functionality provided by the tool. For example, it is quite important whether an LLM is open source or not since it impacts how the end user can finetune the model. However, an LLMOps platform, like most other software, will be used by most end users without modifications to the software code and therefore it is less impactful for an LLMOps tool to be open source.

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?

Large Language Models (LLMs) are advanced machine learning models designed to understand and generate human-like text based on the patterns and information they’ve learned from training data. These models are built using deep learning models to capture intricate linguistic nuances and context.

LLMOps refer to techniques and tools used for the operational model management of LLMs in production environments.

Key components of LLMOps tools

Large Language Model Operations (LLMOps) tools encompass crucial components for efficient management and deployment of large language models (LLMs). These tools typically include features such as:
Prompt Engineering: Creating effective prompt templates for improved model performance.
Data Management: Handling vast datasets, ensuring proper data versioning, and facilitating exploratory data analysis.
Model Fine Tuning: Fine-tuning LLMs to specific tasks and refining models for optimal performance.
Model Monitoring: Continuous tracking of model outcomes, detection of accuracy degradation, and addressing model drift.

What are LLMOps benefits?

LLMOps delivers significant advantages to machine learning projects leveraging large language models:
1.) Increased Accuracy: Ensuring high-quality data for training and reliable deployment enhances model accuracy.
2.) Reduced Latency: Efficient deployment strategies lead to reduced latency in LLMs, enabling faster data retrieval.
3.) Fairness Promotion: Striving to eliminate bias ensures more impartial outputs, preventing discrimination.

LLMOps challenges & solutions

Challenges in large language model operations require robust solutions to maintain optimal performance:
1.) Data Management Challenges: Handling vast datasets and sensitive data necessitates efficient data collection and versioning.
2.) Model Monitoring Solutions: Implementing model monitoring tools to track model outcomes, detect accuracy degradation, and address model drift.
3.) Scalable Deployment: Deploying scalable infrastructure and utilizing cloud-native technologies to meet computational power requirements.
4.) Optimizing Models: Employing model compression techniques and refining models to enhance overall efficiency.
LLMOps tools are pivotal in overcoming challenges and delivering higher quality models in the dynamic landscape of large language models.

Further reading

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

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

Find the Right Vendors

External sources

Access Cem's 2 decades of B2B tech experience as a tech consultant, enterprise leader, startup entrepreneur & industry analyst. Leverage insights informing top Fortune 500 every month.
Cem Dilmegani
Principal Analyst
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Cem Dilmegani
Principal Analyst

Cem has been the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per similarWeb) including 60% of Fortune 500 every month.

Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE, NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and media that referenced AIMultiple.

Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised businesses on their enterprise software, automation, cloud, AI / ML and other technology related decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.

He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.

Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.

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