26 MLOps Tools for 2026: Key Features & Benefits

MLOps is a method for managing machine learning projects at scale. It improves collaboration across development, operations, and data science teams to accelerate model deployment, increase team productivity, and reduce risk and costs.

This article dives into the top MLOps tools for model creation, deployment, and monitoring that help teams standardize, simplify, and streamline their ML ecosystems. 

What are MLOps Tools?

MLOps tools are software programs that help data scientists, machine learning engineers, and IT operations teams integrate, streamline workflows and machine learning components, and collaborate more effectively. Ultimately, they support the central goal of MLOps: automating the process of generating, deploying, and monitoring models by merging machine learning, DevOps, and data engineering. 

MLOps tools are critical for maintaining and improving AI infrastructure, allowing teams to develop more efficient models.

Top 26 MLOps Tools and Platforms

Data and Pipeline Versioning

1. lakeFS Data Versioning System

MLOps is all about managing models. When managing the model, you consider data quality, model performance, and the data path that leads to the model. However, this method does not secure data. You get a logging system that does not support branch methods, commit, or distinguish between data versions.

This is where lakeFS comes in. 

lakeFS is the control plane for AI-ready data, built on a highly scalable data version control architecture. It provides a Git-like version control interface for managing data at petabyte scale, bridging the critical infrastructure gap that slows down AI initiatives. 

The platform manages data lakes the same way developers manage code: with branches, commits, merges, and rollbacks. This unified approach works across all data types: structured tables, unstructured files, images, videos, and model artifacts, making it especially valuable for modern multimodal AI applications that combine different data formats. This helps accelerate AI delivery, ensure data quality and  reproducibility across experiments, as well as reduce data friction between teams and support compliance requirements across data silos.

One of its most important features is environmental isolation. Using lakeFS, many data practitioners can work on the same data, creating a different branch for each experiment. Data can be labeled to indicate specific studies, allowing them to be reproduced with the same tag.

When the update works for you, you can push or merge it back into the main branch, making it available to users. Alternatively, as with Git, you can revert changes immediately without going through each file individually. You can reverse the modification and return to the previous good status. lakeFS ensures that your data is always reproducible and production-ready.

lakeFS is available free of charge as an open-source solution, but larger teams may benefit from the lakeFS Enterprise offering, which comes with other benefits and SLAs.

Key features:

• Zero-copy data versioning: create isolated branches for experimentation without duplicating data, scaling petabytes with instant branch creation
• Write-Audit-Publish workflows (CI/CD): enforce data quality gates with pre-commit and pre-merge hooks that validate data before production
• Data lineage and reproducibility: automatically track the complete history of every data transformation for full reproducibility of ML experiments
• Unified data access: provide consistent, governed access to versioned data across teams and tools while maintaining security and compliance requirements

2. DVC

Update (November 2025): DVC was acquired by lakeFS. DVC continues as a 100% open-source tool under the same license, focused on data versioning for data scientists working with smaller datasets. It seamlessly integrates with Git to enable code, data, model, metadata, and pipeline versioning.

DVC can be used for:

• Experiment tracking (model metrics, parameters, and versioning)
• Building, visualizing, and running machine learning pipelines
• Achieving reproducibility
• Workflow for deployment and cooperation
• Data and model registration
• Continuous integration and deployment of machine learning using CML

Experiment Tracking and Model Metadata Management Tools

3. MLflow

MLflow is an open-source tool for managing key components of the machine learning lifecycle. It’s mostly used for experiment tracking but can also be used for repeatability, deployment, and model registry. Machine learning experiments and model information may be managed via CLI, Python, R, Java, and the REST API.

MLflow provides four main functions:

• MLflow Tracking involves storing and accessing code, data, configuration, and outcomes.
• MLflow Projects allows compiling data science sources for repeatability.
• MLflow Models is all about deploying and maintaining machine learning models across multiple serving environments.
• The MLflow Model Registry is a centralized model repository that supports versioning, stage transitions, annotations, and machine learning model management. 

4. Comet ML

Comet ML is a platform for monitoring, comparing, explaining, and optimizing machine learning models and experiments. You can use it with any machine learning library, including Scikit-learn, Pytorch, TensorFlow, and Hugging Face.

Comet ML allows anyone to readily view and compare experiments, as well as visualize samples of photos, music, text, and table data.

5. Weights & Biases

Source: Weights & Biases

Weights & Biases (acquired by CoreWeave) is a machine learning platform that lets you log experiments, version data and models, optimize hyperparameters, and manage models. You can also track artifacts (datasets, models, dependencies, pipelines, and outcomes) and view datasets (audio, visual, textual, and tabular).

Weights & Biases provides a user-friendly single dashboard for machine learning. Like Comet ML, you can use it in conjunction with other machine learning libraries such as Keras, PyTorch, Hugging Face, Yolov5, Spacy, and others.

Key Features:

• Panels – visuals that allow you to study your recorded data, the correlations between hyperparameters and output metrics, and dataset examples.
• Custom Charts – You can use queries to create custom visualizations and panels.
• Runs table – Using the sidebar and table on the project page.
• Tags – You can label runs with certain attributes that may not be clear from the reported stats or Artifact data.
• Notes – Make notes on your runs and projects, and use them to discuss your results in reports.
• System Metrics – Automatically logged by Wandb.
• Anonymous Mode – Log and view data without a W&B account.

Orchestration and Workflow Pipelines MLOps Tools

6. Prefect

Prefect is an open-source tool for monitoring, coordinating, and orchestrating operations across applications. It’s lightweight and designed for end-to-end machine learning pipelines.

Prefect comes in two variants:

• Perfect Orion UI is an open-source, locally hosted orchestration engine and API server that offers insights into the local Prefect Orion instance and workflows. 
• Prefect Cloud is a hosted solution that allows you to see flows, executions, and deployments. You can also manage accounts, workspaces, and team collaboration.

7. Metaflow

Metaflow is a sophisticated and battle-tested workflow management solution for data science and machine learning projects. It was designed to allow data scientists to focus on model development rather than MLOps engineering.

Metaflow allows you to create workflows, execute them at scale, and deploy the models into production. It automatically records and updates machine learning experiments and data. 

Metaflow is compatible with many cloud service providers (including AWS, GCP, and Azure) and machine-learning Python packages (such as Scikit-learn and Tensorflow), and the API is also accessible for the R language. 

8. Dagster

Dagster provides an orchestration platform that helps manage data pipelines efficiently, using an innovative and cloud-native approach for data teams. Dagster allows for the definition, execution and observation of complex data workflows. 

Key features include task-based workflows, declarative programming models and integrations with popular tools, enhancing both observability and testability. 

9. Kedro

Kedro is a Python-based workflow orchestration tool that allows you to create reproducible, manageable, and modular data science projects. It incorporates principles from software engineering into machine learning, such as modularity, separation of responsibilities, and versioning.

Kedro lets teams do the following:

• Set up dependencies and settings
• Create, visualize, and run pipelines
• Log and track experiments
• Deploy on a single or several machines
• Make sure your data science code is maintainable
• Develop modular, reusable code
• Collaborate with teammates on projects

Feature Stores

10. Feast

Feast is an open-source feature store that lets machine learning teams produce real-time models and create a feature platform that encourages cooperation between machine learning engineers and data scientists.

Key features:

• Manage an offline shop, a low-latency online store, and a feature server to guarantee that features are consistently available for model training, deployment and serving.
• Avoid data leaks by building precise point-in-time feature sets, which relieves data scientists of the burden of error-prone dataset merging.
• You can decouple machine learning from data infrastructure by implementing a single access layer.

11. Featureform

Featureform is a virtual feature repository that allows data scientists to design, maintain, and serve features from their machine learning models. It helps data practitioners improve communication, organize experiments, simplify deployment, boost dependability, and maintain compliance.

Key features:

• Improve teamwork by sharing, reusing, and understanding features across the team.
• When your feature is ready to be deployed, Featureform will coordinate your data infrastructure to prepare it for production.
• The system guarantees that no features, labels, or training sets may be changed to improve reliability.
• Featureform’s built-in role-based access control, audit logs, and dynamic serving rules allow you to implement your compliance logic directly.

Model Testing Tools

12. Deepchecks ML Models Testing

Deepchecks is an open-source solution that meets all of your ML validation requirements, guaranteeing that your data and models are rigorously validated from research to production. It provides a comprehensive way to validate your data and models via its numerous components.

Deepchecks consists of three components:

• Deepchecks Testing enables you to create custom checks and suites for tabular, natural language processing, and computer vision validation.
• CI & Testing Management offers CI & Testing Management to help you collaborate with your team and efficiently manage test findings.
• Deepchecks Monitoring tracks and validates models in production.

13. TruEra

TruEra (acquired by Snowflake) is an observability platform that optimizes model quality and performance through automated testing, explainability, and root cause analysis. It provides a variety of features to assist with model optimization and debugging, achieving best-in-class explainability, and integrating seamlessly into your ML tech stack.

Key features:

• The model testing and debugging function helps to enhance model quality during development and production
• It can run automatic and systematic tests to verify performance, stability, and fairness
• It knows the progression of model versions, which helps to gain insights that will guide quicker and more successful model development
• Identify and isolate the exact factors that contribute to model bias
• Integrates seamlessly with your existing infrastructure and processes

Model Deployment and Serving Tools

14. Kubeflow

Kubeflow facilitates the deployment of machine learning models on Kubernetes by making them portable and scalable. You can use it to prepare data, train models, optimize models, serve predictions, and improve model performance in production. You may install machine learning workflows locally, on-premises, or in the cloud.

Key features:

• Centralized dashboard with an interactive user interface
• Machine learning pipelines for repeatability and efficiency
• Native support for JupyterLab, RStudio, and Visual Studio Code
• Hyperparameter optimization and neural architecture search
• Job postings for Tensorflow, Pytorch, PaddlePaddle, MXNet, and XGboost
• Job scheduling
• Multi-user isolation

15. BentoML

BentoML is a Python-based utility for deploying and managing APIs in production. It simplifies and speeds up the deployment of machine learning applications. The tool also includes hardware acceleration and scales with sophisticated optimizations, such as parallel inference and adaptive batching.

BentoML’s interactive centralized dashboard makes it simple to plan and monitor machine learning model deployments. The best feature is that it works with a wide range of machine learning frameworks and tools, including Keras, ONNX, LightGBM, Pytorch, and Scikit-Learn. BentoML offers a comprehensive solution for model deployment, serving, and monitoring.

16. Hugging Face Inference Endpoints

Hugging Face offers Hugging Face Inference Endpoints, a cloud-based service that enables users to train, store, and share models, datasets, and demos on a comprehensive ML platform. These endpoints are intended to allow users to deploy their trained machine learning models for inference without having to set up and manage the necessary infrastructure.

Key features:

• Depending on your requirements, you may keep costs as low as $0.06 per CPU core/hour and $0.6 per GPU/hour
• Easy to deploy in seconds
• Fully controlled and autoscaled
• Part of the Hugging Face ecosystem
• Enterprise-grade security

Model Monitoring in Production ML Ops Tools

17. Evidently AI

Evidently AI is an open-source Python library for monitoring machine learning models throughout development, validation, and production. It evaluates data and model quality, drift, target drift, regression, and classification performance.

Evidently AI contains three major components:

• Tests (batch model checks) are used to ensure the quality of structured data and models.
• Reports (interactive dashboards) include interactive data drift, model performance, and target virtualization.
• Monitors (real-time monitoring) track data and model metrics from the installed ML service.

18. Fiddler AI

Fiddler AI is an ML model monitoring tool with an easy-to-use, straightforward interface. It lets you explain and debug predictions, evaluate model behavior over a whole dataset, deploy machine learning models at scale, and track model performance.

Key features:

• Performance monitoring – Detailed display of data drift, including when and how it occurs
• Data integrity – Prevents using inaccurate data for model training
• Tracking outliers – Displays univariate and multivariate outliers
• Service metrics – Provides fundamental insights into ML service functioning
• Alerts – Set up alerts for a model or collection of models to notify you of any concerns in production

Runtime Engines

19. Ray

Ray is a flexible framework for scaling AI and Python applications, allowing developers to manage and optimize machine learning projects. The platform is made up of two primary components: a core distributed runtime and a set of AI modules designed to facilitate ML computation.

Key features:

• Tasks – functions that have no state and run within the cluster.
• Actors – worker processes that are stateful and originate within the cluster.
• Objects – immutable values that any component in the cluster can access.

Ray also offers AI libraries for scalable datasets in machine learning, distributed training, hyperparameter tweaking, reinforcement learning, and scalable and programmable serving.

20. Nuclio

Nuclio is a strong framework designed for data, I/O, and compute-intensive tasks. It’s meant to be serverless, so you don’t have to bother about managing servers. Nuclio seamlessly integrates with popular data science tools like Jupyter and Kubeflow, supporting a wide range of data and streaming sources, and can run on both CPUs and GPUs.

Key features:

• Requires minimal CPU/GPU and I/O resources are required to execute real-time processing with maximum parallelism, 
• Integrates with a diverse set of data sources and ML frameworks
• Provides stateful functions with data path acceleration
• Portability to various types of devices and cloud platforms, particularly low-power ones

End-to-End MLOps Platforms

21. AWS SageMaker

Amazon Web Services SageMaker is a comprehensive solution for MLOps. You can train and speed model development, track and version experiments, catalog ML artifacts, integrate Write-Audit-Publish ML workflows, and deploy, serve, and monitor models in production with ease.

Key features:

• A collaboration platform for data science teams
• Automation of the ML training processes
• Deploying and managing models in production
• Tracking and managing model versions
• Write-Audit-Publish automates integration and deployment
• Models are continuously monitored and retained to ensure quality
• Opportunities for optimizing cost and performance

22. DagsHub

DagsHub is a platform that allows the machine learning community to track and version data, models, experiments, ML pipelines, and code. It enables your team to create, review, and share machine learning projects. It’s like a machine learning version of GitHub, with a variety of tools for optimizing the entire process.

Key features:

• Git and DVC repositories for your machine learning projects
• DagsHub logger and MLflow instance for experiment monitoring
• Dataset annotation with the label studio instance
• Comparing the Jupyter notebooks, code, datasets, and photos
• The ability to leave comments on the file, code line, or dataset
• Create a project report using the same format as the GitHub wiki
• ML pipeline visualization
• Reproducible findings
• Running Write-Audit-Publish for model training and deployment
• Integrations for GitHub, Google Colab, DVC, Jenkins, external storage, webhooks, and New Relic. 

23. Iguazio MLOps Platform

Iguazio MLOps Platform is a comprehensive MLOps platform that allows enterprises to automate the machine learning process from data collection and preparation to training, deployment, and production monitoring. It offers an open (MLRun) and managed platform.

The flexibility of deployment choices is a fundamental difference for the Iguazio MLOps Platform; it supports cloud, hybrid, and on-premises settings.

​​Key features:

• The platform enables users to import data from any source and create reusable online and offline features via the integrated feature store
• It enables continuous model training and evaluation at scale by leveraging scalable serverless technology, including automatic tracking, data versioning, and continuous integration and deployment
• Models may be deployed to production with a few clicks, and model performance is continually monitored to avoid drift in your machine learning workflow
• The platform includes a simple dashboard for model management, governance, and monitoring, as well as real-time production

24. TrueFoundry

TrueFoundry is a cloud-native ML training and deployment PaaS on top of Kubernetes that makes it really easy to build, track, and deploy models without having a detailed understanding of Kubernetes. It enables ML teams to train and deploy models at the speed of Big Tech.

Key features:

• Jupyter Notebooks: Start Notebooks or VSCode Server on the cloud with auto shutdown
• Deploy a training batch or inference job: Write your Python script, log metrics, models, and artifacts, and trigger jobs either manually or on a schedule
• Deploy models as APIs: Deploy the model artifact directly to get APIs or wrap it in FastAPI, Flask, or another framework to host the APIs. The deployments support autoscaling and canary deployments out of the box
• Easy debugging: View logs, metrics and cost optimization insights for all services
• Model registry: Track all the models and their versions in your organization along with their current deployment status and metadata
• Deploy and fine-tune LLMs: Deploy open source LLMs in one click and fine-tune them on your own data
• Deploy common ML software: Deploy the most commonly used ML software like LabelStudio, Helm Charts, etc.
• Manage multiple environments and promotion: Manage multiple Kubernetes clusters from different environments and move workloads across them in a single click

Large Language Models (LLMs) Framework

25. Qdrant

Qdrant is an open-source vector similarity search engine and database that offers a production-ready service with a simple API for storing, searching, and managing vector embeddings.

Key features:

• It has an easy-to-use Python API and allows developers to create client libraries in a variety of computer languages
• It uses a unique proprietary adaptation of the HNSW algorithm for Approximate Nearest Neighbor Search, resulting in cutting-edge search speeds without sacrificing accuracy
• Rich Data Types: Qdrant supports a broad range of data types and query criteria, including string matching, integer ranges, geolocations, and others
• It’s cloud-native and can grow horizontally, letting developers employ just the necessary computing resources to serve any quantity of data
• Qdrant is written entirely in Rust, a programming language noted for its speed and resource efficiency

26. LangChain

LangChain is a versatile and powerful framework for constructing language-driven applications. It includes numerous components that let developers create, deploy, and monitor context-aware and reasoning-based systems.

The framework consists of four major components:

• LangChain Libraries – Python and JavaScript libraries provide interfaces and integrations for developing context-aware reasoning applications.
• LangChain Templates – This collection of readily deployable reference architectures addresses a wide range of jobs and offers developers pre-built solutions.
• LangServe – This library allows developers to distribute LangChain chains over REST API.
• LangSmith – is a platform that allows you to debug, test, assess, and monitor chains created using any LLM framework.

Key Features of MLOps Tools

End-to-End Workflow Management

A complete MLOps platform should include an end-to-end workflow management system that streamlines the complicated procedures around developing, training, and deploying ML models. This system should contain features like data preparation, feature engineering, hyperparameter tweaking, model assessment, and more.

Model Versioning and Experiment Tracking

Platforms should have capabilities that allow you to build and conduct experiments, investigate various methods and architectures, and improve model performance. This contains tools for hyperparameter tweaking, automatic model selection, and metric display.

MLOps tools should also be able to efficiently monitor experiments and handle multiple versions of trained models. With good version control in place, teams can simply compare different iterations of a model and revert to prior versions as needed.

Scalable Infrastructure Management

Maintaining a scalable infrastructure is critical when working on large-scale ML projects as it allows effective resource use throughout both training and inference. Most MLOps products integrate well with major cloud machine learning platforms or on-premises settings running container orchestration systems like Kubernetes.

As datasets and models expand in size, distributed training becomes increasingly important for reducing model training time. MLOps systems should support parallelization approaches such as data parallelism or model parallelism in order to make optimal use of numerous GPUs or computing nodes.

A successful MLOps platform must provide automated resource allocation and scheduling features that assist optimizing infrastructure consumption by dynamically modifying resources in response to workload needs. This maximizes the use of existing resources while lowering the expenses associated with idle hardware.

Model Monitoring and Continuous Improvement

Platforms should have the ability to monitor and measure the performance of deployed ML models in real time. This includes capabilities for logging, monitoring model metrics, identifying abnormalities, and alerting, which help you to assure your models’ dependability, stability, and optimal performance.

Keeping high-quality ML models involves ongoing monitoring and development throughout their lifespan. A strong MLOps system should include features like performance metric tracking, drift detection, and anomaly alerts to guarantee that deployed models retain the appropriate accuracy levels over time.

Integration with Existing Tools & Frameworks

A good ML platform should provide you with flexibility and extensibility. This opens the door to using your chosen ML tools and gaining access to a variety of resources, increasing productivity and enabling the application of cutting-edge methodologies.

Data Tracking, History Tracking and Version Control

Version control enables data and ML teams to work on ML code, models, and experiments at the same time in isolation, ensuring that changes made to one area don’t affect the work of other team members. An ML platform should include version control tooling to manage changes and modifications to ML objects, assuring repeatability and promoting effective collaboration.

Benefits of MLOps Tools

1. Accelerate Model Development

MLOps solutions speed up model creation by simplifying workflows and decreasing the human work necessary to train, test, and deploy models. For example, Amazon SageMaker offers an integrated environment in which developers can simply write custom algorithms or utilize pre-built ones to swiftly generate ML models.

2. Enhance Team Collaboration

Tools such as MLflow provide seamless collaboration by tracking experiment progress through various phases of the pipeline while preserving version control over codebase modifications.

3. Improve Model Performance and Quality

Maintaining high-quality performance is crucial when deploying ML models into production environments. Otherwise, they may fail to produce accurate predictions or achieve service level agreements (SLAs).

4. Enhanced Version Control and Reproducibility

Reproducibility is essential for ML because it allows the same findings to be duplicated across diverse circumstances. MLOps technologies aid with version control for both code and data, making it easy to trace changes and replicate trials as required. 

For instance, Kubeflow offers a framework for packaging your ML processes as portable containers that can operate on any Kubernetes cluster.

Read this step by step guide to achieving reproducibility in your ML pipeline: How To Improve ML Pipeline Development With Reproducibility

5. Streamlined Model Deployment and Scaling

MLOps technologies make it easier to put models into production by automating operations like containerization, load balancing, and demand-driven resource scaling. This ensures that your models are always accessible and operating properly, even during peak usage times, without needing human intervention from IT operations personnel.

6. Improved Security and Compliance

Data privacy requirements such as GDPR require enterprises to maintain stringent controls over how personal information is processed and maintained inside their systems, including machine learning programs that may use sensitive data for training purposes. 

Using MLOps technologies with built-in security capabilities allows you to better secure your organization’s important data assets while guaranteeing compliance with regulatory standards.

How to Choose the Right MLOps Tool

Cloud and Technology Strategy

Select an MLOps solution that is compatible with your cloud provider or technology stack and supports the frameworks and languages you use for ML development, for example, for data preprocessing in machine learning specifically. For instance, if you use AWS, you could pick Amazon SageMaker as an MLOps platform that works with other AWS services.

Alignment With Other Tools In Your Tech Stack

Consider how effectively the MLOps solution works with your current tools and processes, including data sources, data engineering platforms, code repositories, Write-Audit-Publish pipelines, monitoring systems, machine learning architecture, and so on. 

Commercial Considerations

When assessing MLOps tools and platforms, keep commercial considerations in mind:

• Examine the price models, including any hidden charges, to verify they meet your budget and growth needs. 
• Review vendor support and maintenance terms (SLAs and SLOs), contractual agreements, and negotiating flexibility to ensure they meet your organization’s needs. 
• Free trials or proof of concepts (PoCs) can help you determine the tool’s usefulness before entering into a commercial deal.

Knowledge And Skills Inside The Organization

Evaluate your ML team’s degree of knowledge and experience before selecting a tool that suits their skillset and learning curve. For example, if your team is familiar with Python and R, you might prefer an MLOps solution that supports open data formats such as Parquet, JSON, CSV, and Pandas or Apache Spark DataFrames

User Support Arrangements

Consider the supplier or vendor’s availability and quality of assistance, such as documentation, tutorials, forums, and customer care. Check the frequency and stability of the tool’s updates and enhancements.

Active User Community And Future Roadmap

Consider a product with a lively community of users and developers who can share comments, ideas, and best practices. In addition to examining the vendor’s reputation, make sure you can obtain updates, review the tool’s plan, and evaluate how it aligns with your goals.

Conclusion

Every week, new advancements, businesses, and techniques emerge in MLOps to address the fundamental challenge of transforming notebooks into production-ready apps. Even legacy tools are broadening their scope and incorporating new capabilities to become MLOps solutions.

We hope the list of MLOps tools for each stage of the MLOps process – from experimentation, development, deployment, and monitoring – helps you build a solid MLOps practice.