Top 40+ LLMOps Tools & Compare them to MLOPs

The rapid adoption of large language models has outpaced the operational frameworks needed to manage them efficiently. Enterprises increasingly struggle with high development costs, complex pipelines, and limited visibility into model performance.

LLMOps tools aim to address these challenges by providing structured processes for fine-tuning, deployment, monitoring, and governance.

Examine the current LLMOps ecosystem, compare major platforms, and learn how these solutions differ from and complement established MLOps practices.

LLMOps tools comparison

Sorted by GitHub stars for LLMOps tools. See the extended LLMops and MLOps tools comparison table below for detailed star counts.

A breakdown of each metric is provided below:

• Evaluation: Some LLMOps tools include built-in capabilities to assess model outputs against task-specific criteria, while others rely on external frameworks for more customized or in-depth analysis.
• Cost tracking: Detailed cost analysis and monitoring of resources used during training and inference are either directly supported by tools or achieved through integrations.
• Fine-tuning: Some LLMOps tools perform fine-tuning of large language models themselves, whereas others focus on managing or orchestrating the fine-tuning process.
• Prompt engineering: Designing and optimizing prompts is directly handled by some tools, but most provide infrastructure to support this rather than performing it themselves.
• Pipeline Construction: Certain tools automate end-to-end LLM workflows, including data preparation, training, and evaluation. Meanwhile, others enable pipeline building through integrations.
• BLEU / ROUGE: BLEU and ROUGE are common language evaluation metrics used to assess text quality; some tools support them natively, while others rely on external libraries.
• Data storage & versioning: Secure storage and version tracking of training data are handled directly by some tools, while others integrate with third-party storage/versioning solutions.

What are LLMOps platforms?

LLMOps platforms support the lifecycle of LLMs by enabling:

• Fine-tuning
• Versioning
• Deployment
• Monitoring
• Prompt and experiment management

LLMOps platforms vary in approach:

• No-code/Low-code platforms: easy to use but less flexible.
• Code-first/Engineering-oriented platforms: require technical skills but offer greater customization.

LLMOps tools can be grouped into three main categories:

1. MLOps platforms extending into LLMOps

Certain Machine Learning Operations (MLOps) platforms include specialized toolkits tailored for large language model operations (LLMOps).

MLOps is the discipline focused on orchestrating the full lifecycle of machine learning, from development through to deployment and maintenance. Since LLMs are also machine learning models, MLOps vendors are naturally expanding into this domain.

Comet

Comet is an experiment-tracking and model-observability platform. It also supports LLM experiment tracking, prompt versioning, and LLM evaluation, making it suitable for teams building and optimizing LLM applications.

Valohai

Valohai is an MLOps platform that supports reproducible pipelines for data processing, training, and deployment. It recently added LLMOps-friendly capabilities such as metadata tracking, artifact versioning, and large-scale training orchestration.

Figure 1: Valohai knowledge repository.¹

TrueFoundry

TrueFoundry is an end-to-end ML/LLM platform that simplifies model deployment, finetuning, and monitoring. It offers GPU-optimized infra, model registry, prompt management, and enterprise-grade governance.

Zen ML

ZenML provides a production-ready pipeline framework for MLOps and LLMOps. It allows users to build reproducible pipelines, connect orchestrators (Airflow, Kubeflow), and integrate LLM workflows such as RAG, finetuning, and evaluation.

2. Data, cloud & infrastructure platforms offering LLMOps

Data, cloud, and infrastructure platforms are increasingly offering LLMOps capabilities that enable users to leverage their own data to build and fine-tune LLMs.

For example, Databricks provides LLM training, fine-tuning, and model hosting (expanded following the MosaicML acquisition).

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

3. LLM-Focused frameworks & platforms

This category includes tools that exclusively focus on optimizing and managing LLM operations. Here’s a breakdown of the tools and their core LLMOps functions:

DeepLake

Deep Lake provides a data lake designed for AI, offering storage, versioning, and a vector database. It supports workflows for LLM dataset creation, inspection, and retrieval, working seamlessly with PyTorch and TensorFlow.

Figure 2: The image shows the role of Deep Lake in an MLOps architecture²

Deepset AI

Deepset’s Haystack is a RAG and search framework that enables enterprises to build LLM-powered applications by combining document stores, retrievers, and large language models. It supports multi-modal RAG pipelines, model evaluation, and production deployment.

Lamini AI

Lamini offers a platform for building custom LLMs, supporting both full finetuning and lightweight tuning. It is built for enterprises needing domain-specific LLMs and provides APIs and SDKs for integrating organizational data.

Nemo by NVIDIA

NeMo is a framework for building, training, and customizing foundation models, including LLMs. It provides components for supervised finetuning, instruction tuning, RAG, model evaluation, and deployment on NVIDIA GPUs.

Figure 3: NeMo framework architecture.³

Snorkel AI

Snorkel AI provides a data-centric development platform for programmatically labeling and curating training data. It now extends into foundation model customization, enabling organizations to adapt LLMs with high-quality, automatically labeled datasets.

Titan ML

TitanML focuses on efficient LLM inference. Its Titan Takeoff Server helps teams run LLMs on-premise with optimized performance, reduced GPU requirements, and improved latency. It also provides quantization and compression features.

LLMOps supporting Technologies

LLMs

Some LLM providers, such as OpenAI, Anthropic, and Google, offer partial LLM lifecycle features (e.g., fine-tuning on select models, monitoring dashboards, and evaluation tooling).

Note: LLM providers offer tools for fine-tuning and integration, but they are not full LLMOps platforms. LLMOps typically requires additional components such as monitoring, governance, lineage, evaluation systems, and pipeline management.

Integration frameworks

These tools are built to facilitate the development of LLM applications, such as document and code analyzers, chatbots, etc.

Vector databases (VD)

VDs store high-dimensional vector embeddings generated from text, images, or other data. They do not store raw, sensitive records such as medical test results; instead, they index embeddings to enable semantic search and retrieval.

Fine-tuning tools

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

Libraries used for fine-tuning include Hugging Face Transformers, PEFT/LoRA-based frameworks, and training engines such as DeepSpeed or Megatron-LM. PyTorch and TensorFlow are general-purpose deep learning frameworks rather than fine-tuning tools.

RLHF tools

RLHF, short for reinforcement learning from human feedback, enables AI systems to refine their decisions by incorporating human guidance.

In reinforcement learning, an agent improves its behavior through trial and error, guided by feedback from the environment in the form of rewards or punishments.

In contrast, RLHF helps improve model behavior by integrating human preference data into the training loop. It does not replace large-scale labeling but relies on human-generated comparison data. RLHF supports alignment, safety, quality improvement, and better adherence to user intent.

LLM testing tools

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

• Testing frameworks
• Benchmark datasets
• Evaluation metrics.

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.

Benchmark: TrueFoundry vs Amazon SageMaker vs Manual (no LLMOps tools)

We benchmarked TrueFoundry, Amazon SageMaker, and a manual setup to evaluate the real-world benefits of LLMOps tools. Using the same model, dataset, and hardware, we measured training and evaluation times.

Both platforms reduced training from 2,572 seconds to under 570, and evaluation from 174 seconds to around 40. While SageMaker was slightly faster during training and TrueFoundry was slightly faster during evaluation, the overall difference was negligible; both delivered major improvements over manual setup.

See our methodology.

Choosing the proper infrastructure for LLMOps depends not only on speed but also on cost, automation, and integration quality. SageMaker offers deep AWS integration, TrueFoundry provides fast deployment with high cost efficiency, while manual setups are flexible but usually slower.

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 is to train a model from scratch rather than fine-tune an existing one, this will have significant implications for your LLMOps stack.
2. Define requirements: Based on your goal, specific requirements will become more critical. 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: Use free trials and demos from various LLMOps tools to evaluate their features firsthand.

What is LLMOps?

LLMOps stands for Large Language Model Operations. It refers to the practices, tools, and infrastructure used to manage the lifecycle of LLMs, such as fine-tuning, deployment, monitoring, evaluation, governance, and ongoing model improvement.

LLMOps does not automate the entire AI pipeline but focuses specifically on operationalizing LLM-based systems.

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: NVIDIA L40 vs L40S

Training and deploying large language models require significant computational power, often relying on specialized hardware such as GPUs to handle large datasets efficiently. Access to these resources is essential for effective model training and inference. Additionally, managing inference costs through techniques like model compression and distillation helps reduce resource consumption without sacrificing performance.

For example, the NVIDIA L40 and L40S share the same architecture, but the L40S enables more active SMs and delivers higher throughput, especially for AI and LLM workloads. Both GPUs are suitable for deep learning; the L40S provides a performance-optimized configuration for training and inference.

Transfer learning

Unlike conventional ML models built from the ground up, LLMs often start with a base model, which is 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 primarily focuses on hyperparameter tuning to enhance accuracy, LLMs introduce an additional dimension by 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.

Here is a categorized overview of key tools across the LLMOps and MLOps landscape:

LLMOPS vs MLOPS: Pros and Cons

While deciding which one is the best practice for your business, it is important to consider the 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. Development: LLMOps can simplify development by using pretrained models, reducing the need to build models from scratch. However, data preparation, evaluation, and prompt testing still play significant roles.
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 integrity 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 a range of tools and platforms, including cloud, distributed, and edge computing, enhancing development capabilities.

MLOPS Cons

1. Complex to deploy: MLOPS introduces complexity, requiring time and effort across tasks such as data collection, preprocessing, deployment, and monitoring.
2. Less flexible and creative: MLOps is not inherently less flexible, but its scope is broader and supports a wider range of ML models, including LLMs.

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 broader 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 among data scientists, software engineers, and DevOps professionals to deploy and manage machine learning models. LLMOps deals with deploying, fine-tuning, and maintaining large language models as part of real-world software systems.

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.

Benchmark methodology

We benchmarked the training and evaluation times of a DistilBERT-based sentiment classification model across three environments: a manual setup (CPU-only), TrueFoundry, and Amazon SageMaker. To ensure consistency, we used the same codebase, pretrained model (distilbert-base-uncased), and the first 5,000 samples from the Amazon Reviews dataset across all runs.

The dataset was filtered to include ratings from 1 to 5, relabeled into five classes (0–4), and split into stratified 80/20 training and validation sets. Tokenization was performed with a fixed maximum sequence length of 128.

The model was trained for one epoch using identical batch sizes (16 for training, 32 for evaluation). Both TrueFoundry and SageMaker used the same GPU instance type, while the manual setup was intentionally run on CPU to reflect a typical local or non-specialized environment.

This setup highlights not only the platform-level optimizations offered by modern LLMOps tools but also the substantial performance gains from seamless GPU access. The benchmark illustrates how using managed platforms like TrueFoundry and SageMaker can reduce training and evaluation time compared to running the same code manually on a CPU, especially in real-world, resource-limited scenarios.

FAQ

Reference Links

1.

Valohai | The Scalable MLOps Platform

2.

Introducing Deep Lake, the Data Lake for Deep Learning

3.

NVIDIA NeMo Framework - NVIDIA Docs

NVIDIA Docs

Principal Analyst

Cem Dilmegani

Principal Analyst

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Cem has been the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per similarWeb) including 55% 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 and NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and resources that referenced AIMultiple.

Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised enterprises on their technology 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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