MLOps Blog

The Best MLflow Alternatives

10 min
7th December, 2023

Machine learning model development generates an array of experiments as ML engineers tweak architectures, parameters, and datasets. Manually tracking these iterations becomes intractable as projects increase in complexity.

MLflow is a popular open source solution to manage the machine learning lifecycle and facilitate reproducibility. It consists of three main components:

It also provides a way to package data science code into projects and to define MLOps workflows. Recently, the MLflow developers have added capabilities around Large Language Models (LLMs): a dedicated LLM experiment tracker, experimental support for prompt engineering, and an experimental interface for connecting to LLMs provided by third parties like OpenAI.

MLflow excels at streamlining machine learning lifecycle management and simplifying experiment tracking. However, it lacks many features that data science teams seek, such as dataset versioning or user access management. Further, you need to deploy MLflow on your own infrastructure and rely on its community for support.

That’s where alternatives come in. These are typically available as SaaS – meaning you don’t have to worry about hosting and updates – and come with security and compliance capabilities required by many organizations. Further, their UI is often more advanced and centered around teams and collaboration.

In this article, we will first examine MLflow’s limitations in depth. We’ll provide a comprehensive overview based on insights from extensive user interviews, first-hand experience in various projects across industries, and in-depth research during vendor selection processes.

In a hurry? We’ve summarized the key comparison criteria in a table at the end of this article

Main limitations of MLflow and reasons to explore alternatives

Every project and team has distinctive requirements for managing the machine learning lifecycle. While MLflow is a great tool and often the first option that comes to mind, it’s by no means the best tool for every scenario.

To break down why MLflow may not be a good fit for you, we’ll look at the most common challenges users encounter.

The concerns with MLflow often raised by users can be divided into the following categories:

  • 1 Security and compliance
  • 2 User, group, and access management
  • 3 Lack of collaborative features
  • 4 UI limitations
  • 5 Lack of code and dataset versioning
  • 6 Scalability and performance

Further, open source MLflow comes with all the drawbacks of a self-hosted tool not backed by a company:

  • 1 Configuration and maintenance overhead
  • 2 Integration and compatibility challenges
  • 3 Lack of dedicated support

In the following, we’ll look at each category in detail.

Security and compliance

Many organizations – and, in turn, their ML teams – have strict security and compliance requirements. While open source MLflow offers resource-level permissions management and password-based authentication, by design, it is up to the user to configure more advanced access controls and ensure compliance adherence.

If you choose MLflow, it’s your responsibility to:

  • Implement measures to ensure that specific resources, such as experiments and models, can only be accessed by authorized individuals.
  • Ensure that sensitive data remains encrypted, safeguarding it from potential threats.
  • Regularly conduct vulnerability assessments and mitigate potential risks.

Sure, this gives you the flexibility to design a security framework tailored to your specific requirements. However, this flexibility is a double-edged sword. It requires significant expertise, development effort, and vigilant oversight.

While some organizations might find the self-managed security approach challenging, others might appreciate its flexibility. When considering MLflow, it’s essential to weigh its adaptability against the comfort of the built-in, ready-to-use security features of other platforms.

Lack of user and group management

Hand in hand with the relative lack of security features comes a lack of user management. While it’s commonplace in many enterprise applications to be able to restrict access to files or information to a select group of users, open source MLflow does not support even coarse-grained permissions.

This lack is a serious limitation in the eyes of organizations that are used to systems like LDAP or the IAM capabilities of cloud platforms:

I would say that the main caveat we have with open-source MLflow is the lack of ubiquitous access control. Projects become accessible to all, forcing us to either write extensive infrastructure code or deploy separate MLflow instances for each team to ensure data isolation.

— Senior MLOps Engineer, Digital Identity Verification Platform, UK

Lack of collaborative features

The ease of collaboration – or lack thereof – can make or break a machine learning project. This becomes even more evident when working across diverse teams. MLflow, while celebrated for its wide range of MLOps capabilities and availability as a high-quality open source software, leaves much to be desired.

For instance, collaboration tools allowing team members to seamlessly review projects, share data, or create detailed reports are noticeably absent in MLflow. Instead, a more manual process is required. To share your projects with collaborators, you’d need to create URL aliases for each experiment.

Kha Nguyen, a Senior Data Scientist at a leading retail and hospitality analytics service company, recalls his experience with MLflow:

There’s also the issue of creating a URL alias for [MLflow experiments]. Why do I have to do all this manually?

— Kha Nguyen, Senior Data Scientist

This became a significant hurdle because Kha worked primarily as a solo data scientist, reporting to a non-technical manager who could not navigate MLflow himself.

User interface limitations

A poorly designed user interface can seriously hamper productivity and adoption, especially for less technical users or those new to a tool. Without a doubt, MLflow provides a clean and straightforward interface. But while MLflow’s UI is clean and functional, it is far less configurable or feature-rich than the UI some other platforms discussed in this article offer.

For some teams and use cases, the simplicity and restrictiveness of MLflow’s UI is a strength. If you only care about some standard metrics like accuracy or precision, the fairly basic plots in MLflow’s Tracking UI are more than sufficient.

Others might do most of their analysis outside of MLflow, retrieving the necessary data through MLflow’s API and importing it into other tools to create visualizations or dashboards.

Concerns about scalability and performance

For organizations looking to scale their ML projects and integrate machine learning models into their products, performance is paramount. When evaluating machine learning platforms, it is thus important to understand how they fare under increased loads.

When it comes to performance, the main areas of concern are model training, experiment tracking, and model serving.

MLflow, while renowned for its ease of use for individual users or smaller teams, reportedly faces challenges when tracking a large number of experiments or machine learning models. A common observation is that MLflow seems to not be as resource-efficient as some of its competitors.

Sometimes, MLflow is unreliable because I think it’s not optimized, as it consumes quite a lot of RAM and runs slow, too. The real challenge arose when we ran 100 experiments and 100 forecasts simultaneously, streaming data into MLflow. That’s when we experienced issues with MLflow’s responsiveness.

— MLOps Engineer at a large retailer

MLflow supports distributed computing platforms like Apache Spark and Databricks for model training and provides integrations with distributed storage systems such as AWS S3 and DBFS. However, it’s up to the users to configure, tune, and maintain these systems.

When it comes to model serving, open source MLflow offers plenty of options. Aside from its built-in model server, through Seldon’s MLServer, MLflow integrates with Seldon Core and KServe. Further, MLflow ships with integrations for third-party model serving solutions, namely Microsoft’s  AzureML, Amazon SageMaker, and Apache Spark. It also provides a Python interface for deploying to custom targets, enabling users to write their own deployment integration. This gives teams the flexibility to choose the optimal model serving solution for their use case, but it often comes with engineering and maintenance overhead or additional costs.

Configuration and maintenance overhead

As an open source tool, MLflow is free to download, and anyone can operate as many instances as they like without incurring license fees. There are also virtually no limits to its adaptability, allowing organizations to tailor the platform to their needs.

However, hosting an MLflow instance comes with costs for the infrastructure and maintenance. You need to configure and manage the servers and the underlying storage, watch out for and apply security patches, upgrade as new MLflow versions are released, and troubleshoot any issues.

The difficult part for us was making it work very quickly. We had to spend 50 engineering hours to set it up and make it work for us.

—  Principal ML Engineer, Software Development, USA

Setting up and deploying MLflow can be complex. It typically requires a virtual machine or Kubernetes cluster. You must also manage a backend store (like MySQL, SQLite, or PostgreSQL) and an artifact store (like S3, Azure Blob Storage, or GCS). Further, you’ll have to take care of backups.

What is quite important for us as a company is data security and knowing which data is saved on which servers. MLflow would, therefore, be perfect but require a lot of administration, which is why I would prefer a SaaS solution. It’s a hassle for me to experiment and maintain MLflow simultaneously.

— VP of Engineering at a large enterprise

Additionally, MLflow only provides password-based authentication by default. Integrating it with authentication protocols like OAuth or LDAP or setting up role-based access control (RBAC) will inevitably add complexity.

While there are challenges to consider, the open source nature of MLflow provides unparalleled options for customization and adaptability. Whether this flexibility is a drawback or an advantage depends on how much configuration and maintenance your team and organization can handle.

Integration and compatibility challenges

MLflow integrates with many machine learning frameworks, cloud platforms, and third-party tools. However, in practice, the extent of these integrations might not always meet every organization’s unique requirements – especially for those utilizing less conventional tools or proprietary frameworks.

We developed a proprietary tool for preprocessing our data, and integrating it with MLflow wasn’t straightforward. We had to invest additional engineering hours to make it work.

— Lead ML Engineer at a FinTech firm in the UK

Data storage integration might pose another set of challenges. While MLflow can work with several storage solutions, it does not support all of them.

Due to industry-specific compliance, we use a niche cloud storage solution. Sadly, MLflow didn’t offer an immediate integration for it.

— Data Scientist at a health technology firm

Overall, while MLflow is versatile, teams with unique workflows or tools should be prepared for some hands-on tweaking to achieve seamless integration.

Lack of dedicated support

Open source MLflow benefits from solid documentation and community support, which many users find sufficient. This support typically comes from forums and discussion groups, but it’s important to note that there’s no assurance of a prompt response.

Further, since all discussions take place in public and are archived, you cannot share sensitive information. Your organization’s policies might even prohibit you from sharing any details about your infrastructure or tech stack.

The lack of dedicated support when it comes to setting up, troubleshooting, or maintaining the platform is a pain point for many organizations, especially as they scale their machine-learning initiatives.

As an open source project, MLflow does not guarantee:

  • Timely response to questions
  • Access to expert guidance on complex topics that go beyond the documentation
  • Onboarding and continued training
  • Acting on feature requests
  • Support for custom integrations and extensions

MLflow has a vibrant community. Many of its users share their experiences through online discussions, talks, and blog posts. However, the lack of dedicated support might lead you to consider a managed platform backed by a company.

This begs the question: What other options exist if MLflow is not for your team?

Let’s explore some of the alternatives to MLflow available.

Alternatives to open source MLflow

There are many alternatives to open source MLflow available.

First of all, there is Managed MLflow by Databricks. It’s exactly what it sounds like: MLflow instances hosted and managed for you by Databricks, the original creators of MLflow.

Azure Machine Learning, the end-to-end ML solution on Microsoft’s Azure cloud platform, is unique among the alternatives to MLflow. While not based on MLflow, many of its components, such as the model registry or experiment tracker, are compatible with MLflow.

Then, there are managed ML products offered by dedicated companies., Weights & Biases, Comet ML, and Valohai all provide platforms with different feature sets worth considering.

Metaflow, an open source framework initially developed by Netflix, is focused on orchestrating data workflows and ML pipelines. While it lacks many features MLflow offers regarding experiment tracking and model management, it excels at managing large-scale deployments.

Finally, there are Amazon SageMaker and Google’s Vertex AI, the end-to-end MLOps solutions integrated into these tech giants’ cloud platforms.

With that overview in mind, let’s dive deep into each of these alternatives to MLflow.

Alternatives to open-source MLflow
Alternatives to open source MLflow

Managed MLflow (Databricks)

MLflow is available in two main flavors: open source MLflow and Managed MLflow, a service offered by Databricks, MLflow’s original creators. While both versions retain the core functionalities that MLflow is widely renowned for, they cater to different audiences and use cases.

One of the benefits of managed MLflow is the tight integration with other Databricks services, such as Databricks Notebooks, the Databricks Jobs Scheduler, and managed Spark clusters.

Managed MLflow Databricks, quickstart
Users of managed MLflow can use Databrick’s integrated notebook environments to interact with MLflow

Cases where Databrick’s Managed MLflow excels over open source MLflow

Managed MLflow alleviates many drawbacks that large organizations face with the open source variant. It’s a good choice for teams for which MLflow fits their machine learning workflow but for whom the lack of security and user management features is a dealbreaker.

Managed MLflow by Databricks

Setup and deployment

Seamless integration with the Databricks ecosystem, reducing the time and effort required for setup and deployment.

Requires manual setup, configuration, and management. Users need to allocate infrastructure and handle installation, scaling, updates, and backups.


As part of the Databricks ecosystem, it can handle large-scale machine learning workloads seamlessly.

Scaling to handle larger workloads might involve manual intervention and pose performance challenges.

Security and management

Offers advanced security features out-of-the-box, including role-based access control (RBAC), integration with enterprise identity providers, and data encryption.

Advanced security like RBAC requires developing custom implementations or integrating third-party tools.

Integration with Databricks ecosystem

Deeply integrated into the Databricks ecosystem, offering seamless interoperability with Databricks notebooks, workspace, ACL-based stage transition, and other features.

Open source MLflow integrates with Databricks through MLflow Projects and the experimental MLflow AI Gateway.

Data storage and backup

Integrated storage solutions with automated backup strategies ensure data safety and reliability.

Users need to manually set up storage and implement backup strategies.


As a managed service, users pay for access to the platform, storage, and compute resources.

While open source MLflow itself is free to use, organizations bear the costs of infrastructure, manual setup, scaling, and potential customizations.

Support and maintenance

Comes with dedicated support from Databricks. Databricks handles regular updates, patches, and maintenance.

Users have to rely on community support. Maintenance and updates are the responsibility of the user. is an experiment tracking platform and metadata store that offers model versioning, model registry, and real-time model performance monitoring. It’s focused on ML team collaboration, comes with fine-grained user management, and has a highly customizable user interface with many visualization features. It is available as a managed and self-hosted offering.

Thanks to its built-in MLflow integration, data scientists can use MLflow’s client for experiment tracking. Thus, Neptune is an interesting option if you’re migrating from an existing MLflow setup.

See in the app
The Neptune training overview dashboard contains plots of core metrics, a table with important preprocessing parameters, and sample predictions

Neptune’s features include:

  • Metadata management:  Users can log a diverse range of metadata, from metrics and hyperparameters to interactive visualizations, jupyter notebooks, source code, and data versions.

    Neptune can be the one source of truth no matter where your team runs the training (whether it’s in the cloud, locally, in notebooks, or somewhere else). For any model, you’ll know who created it and how, what data it was trained on, and how it compares to other model versions.
  • Intuitive user interface and custom dashboards: Neptune’s user interface facilitates the viewing, analyzing, and comparing different experiments. This is particularly useful when trying to discern patterns or identify the best-performing models among a batch of runs.

    Users can design and set up custom dashboards, aggregating pertinent metadata. This feature is tailored for collaborative scenarios where sharing insights with team members or stakeholders becomes essential.
  • Collaborative features available in the free tier: Even without paying for the service, up to five users can collaborate on a project. This makes Neptune an interesting option for researchers and students without a tool budget. It also allows teams to test whether Neptune fits their workflows and collaboration needs.

Neptune offers integrations with a wide range of ML frameworks and tools.

Cases where excels over MLflow

Neptune shines for teams looking for an easy-to-use platform with extensive experiment visualization and metadata tracking features. However, it might pose integration challenges for organizations heavily invested in custom or legacy infrastructure.


Scalable solution

Performs equally well whether you run 10 or 10,000 experiments. Both the database and dashboards scale with thousands of runs.

Scaling MLflow requires significant configuration, management of databases, and possibly sharding to handle larger data volumes or many concurrent users.

Real-time monitoring

Enables real-time tracking and visualization of metrics, hyperparameters, and outputs, allowing users to see how their experiments are progressing as they run.

Supports logging and tracking metrics during model training, but its default UI does not provide a dynamically updating, real-time streaming view of those metrics.

Team collaboration features

Strong focus on team collaboration with capabilities for sharing work, a central metadata store, user-specific views, and managing team permissions.

Designed mostly for single users with basic collaboration features through shared dashboards, but lacks more interactive or granular collaboration tools.


Comes with built-in security features, including role-based access control (RBAC), ensuring only authorized users can access certain experiments or data.

Out-of-the-box, MLflow doesn’t offer features like RBAC. Implementing advanced security requires custom solutions or additional tools.

Support and documentation

Dedicated support in all paid tiers and comprehensive documentation.

Provides community support through forums and discussion groups, but there’s no guaranteed response time or dedicated assistance.

User interface and custom dashboard

Neptune allows you to create custom dashboards, which can be useful for visualizing data in different ways and sharing dashboards.

MLflow has a functional UI for experiment tracking but offers limited customization options.

Resource monitoring

Supports resource metrics logging like CPU, GPU, or memory consumption for your experiments.

Doesn’t have built-in support for resource logging but allows for integrating a third-party tool.

Service account and API access

Provides service accounts with granular permissions for pipeline automation and report generation.

Supports REST API access for pipeline automation and report generation but does not differentiate between service and user accounts.

Azure Machine Learning

Azure Machine Learning is Microsoft’s cloud-based MLOps platform. It lets you manage and automate the whole ML lifecycle, including model management, deployment, and monitoring. It is tightly integrated with the Azure platform, offering seamless integration with other Azure services that many organizations already use.

While Azure Machine Learning is not based on MLflow internally, large parts of its API are compatible with MLflow. In fact, for experiment tracking, MLflow is the only supported client.

Features / Capabilities
Azure ML

Experiment tracking

Detailed experiment tracking with logging and visualization (uses the MLflow client for experiment tracking).

Detailed tracking with logging and visualization.

Model registry and audit trail

Includes automatic capture of lineage and governance data (supports MLflow’s model format).

Basic model registry with audit trail.

Reproducible ML pipelines

Supports creating reproducible ML pipelines.

Supports creating reproducible ML pipelines (known as MLFlow Projects).

Reusable software environments

Automation of end-to-end ML lifecycle

Supports MLOps-centric workflows.

Only partial support out-of-the-box but can be augmented with other tools to achieve a similar experience.

Monitoring ML applications

Yes, via integration with Prometheus and dashboards like Grafana.

Notifications and alerts

You can specify notifications and alerts that are triggered by events in the ML lifecycle.

No, but you can write plug-ins to, e.g., send email notifications when certain events occur or integrate MLflow with third-party tools.

Weights & Biases (WandB)

Weights & Biases (WandB) is a platform for experiment tracking, dataset versioning, and model management. WandB’s components, including the Model Registry and Artifacts, allow you to store and manage the model ML lifecycle, version datasets, and models. That helps with the lineage tracking of machine learning models and fosters their reproducibility.

UI of one of the MLflow alternatives: Weights & Biases
WandB dashboard that shows experiment, run, feature importance, model train/test accuracy, and model training logs

One of WandB’s standout features is its hyperparameter sweep capability. While you can undoubtedly set and adjust hyperparameters in any Python script, WandB automates and optimizes this process. By defining a hyperparameter search space, WandB can automatically train multiple models with different hyperparameter combinations to help you identify the best configuration without manual iteration. It visualizes the performance of those hyperparameter combinations in an intuitive dashboard, enabling data scientists to make informed decisions quickly.

Cases where WandB excels over MLflow

WandB excels in visualization, real-time monitoring, collaboration, and a user-friendly interface.

Weights & Biases

Deployment and infrastructure

Provides a managed cloud solution, eliminating the hassle of server setup, maintenance, and database configurations. WandB can be set up swiftly and offers on-premises options for those requiring it.

Users have to handle setup, maintenance, and scaling. While this grants flexibility, it necessitates more effort, especially for smaller teams lacking dedicated DevOps support.

Interactive visualizations and reports to compare model runs

Provides dynamic system metrics monitoring, which allows users to trace the potential impact of system behavior (like GPU utilization) on model training and interact with  rich media logging and hyperparameter distributions in a scatter plot format.

Gives a clear and concise view of experiment results. WandB might be preferred by those looking for deeper, more interactive insights into their model’s training dynamics.


Comes with built-in features to ensure secure experimentation, including access controls for shared projects.

Does not offer advanced security features out-of-the-box. For more robust security features, users might look into the managed version by Databricks or use custom solutions.

Real-time monitoring

Offers extensive real-time monitoring, logging, and alerting, making it easier to keep track of experiments as they run.

Provides real-time logging capabilities, but real-time visualization and monitoring might be more basic than WandB.

Collaboration and team features

Strong emphasis on team collaboration. Features include shared projects, real-time collaboration on dashboards, and commenting on runs or charts.

Offers basic collaborative features. Sharing results is primarily achieved through sharing the full dashboard URL.

User management

Provides an integrated user management system that streamlines the process of granting and restricting access to projects, experiments, and models.

Does not offer a built-in user management system and supports only basic authentication mechanisms.

Access control

Supports private projects restricted to specific team members or public projects for universal access.

Projects are public by default. To grant a team member access to view experiment results, you would share the complete link to the MLflow dashboard hosting those results.

Artifact management

Built-in artifact management system, allowing users to easily version, store, and track datasets, models, and other artifacts.

Offers artifact logging and tracking.

Comet ML

Comet ML offers a comprehensive platform for MLOps, providing cloud-based services that cover a broad spectrum of the MLOps lifecycle, from model training to deployment. Comet’s capabilities include ML experiment tracking, model management, collaboration tools, and proactive notifications.

UI of one of the MLflow alternatives: Comet
Comet dashboard that shows experiment metadata using different visualization widgets

Comet’s standout feature is its ability to automatically capture and categorize essential run metadata such as parameters, metrics, code versions, and outputs. The platform boasts a unique experiment comparison engine that greatly exceeds MLflow’s capabilities. Instead of merely displaying tables or lists of results, Comet’s engine visualizes the data in ways that facilitate quick comparisons and insights, allowing researchers and data scientists to discern differences between model variations efficiently.

Cases where Comet excels over MLflow

While both Comet and MLflow assist in tracking and managing ML experiments, their target audiences differ. Comet ML is more geared toward teams seeking an out-of-the-box, cloud-based solution with rich visualizations and collaboration features, while MLflow emphasizes customizability and integrations.


Comparing experiments

Provides an interactive dashboard that offers diverse plots and visualizations for metrics, hyperparameters, and other tracked information with side-by-side comparisons and innovative visualizations.

Provides a robust foundation for experiment comparison, but it’s important to note that other platforms might offer additional visualization or comparison tools.
Depending on the specific requirements of a project, users might integrate MLflow with other tools to get a more comprehensive experiment comparison setup.

User interface (UI) and visualization

Offers a visually rich web interface that presents detailed information on every experiment, including charts, graphs, and other visual aids that make comparing and analyzing runs easier.

Provides a more straightforward web UI primarily focused on tabular data for runs.

Automated hyperparameter tuning 

Offers a powerful and intuitive hyperparameter optimizer. The optimizer can dynamically find the best set of hyperparameter values to minimize or maximize a particular metric, either in serial, combined, or in parallel.

Integrates with hyperopt to run parameter sweeps but does not offer the same out-of-the-box optimization capabilities as Comet.


Users can comment on experiments, share projects, and collaborate on analyses without leaving the platform. Comet also ships with a Slack integration.

While MLflow supports sharing through its server functionality, it lacks built-in collaboration features like discussions, comments, or team workspaces.

Public sharing

Allows users to create public links to their experiments, enabling easy sharing with those outside  their team or organization.

Users can create links to projects, but this requires manual effort, and access control has to be managed outside of MLflow.

Integration & auto-logging

Comet can auto-log various experiment details.  This reduces the need for manual intervention and streamlines the ML experiment tracking process.

Cloud-based and managed solution

Handles backend infrastructure, storage, and scalability issues, freeing the user from many operational concerns.

Requires self-hosting and management, which comes with operational overhead for the user.


Valohai is an end-to-end MLOps platform for traditional machine learning and deep learning tasks. It supports hyperparameter tuning, feature engineering, and artifact tracking. Valohai manages the end-to-end orchestration, including scheduling, notifications, and handling failures.

However, Valohai might not be the best fit for more straightforward data analytics or statistical tasks that don’t require full-fledged machine-learning pipelines.

Additionally, integration requires extra effort for organizations that predominantly use platforms or ecosystems outside of what Valohai supports.

UI of one of the MLflow alternatives: Valohai
Vaholai dashboard that shows experiment log, duration, environment and status

Cases where Valohai excels over MLflow

Valohai excels over MLflow when it comes to workflow orchestration, user management, integration with third-party tools, and Kubernetes features.


Infrastructure and workflow orchestration

Provides seamless integration with various cloud providers and automatically handles infrastructure orchestration. This includes spinning up and down resources, managing distributed training, and more.

Doesn’t handle infrastructure orchestration. Users need to manage their own servers or cloud resources.

End-to-end deep learning lifecycle

Designed as an end-to-end MLOps platform, Valohai covers the entire ML  lifecycle. It offers tools for data preprocessing, training, deploying, and monitoring machine learning models.

MLflow is not an all-in-one solution for the entire deep learning workflow but focuses on experiment tracking and model management.

Pipeline versioning

Offers robust version control not only for models but also for data, hyperparameters, and even the training environment. This ensures full reproducibility.

While it provides model versioning and logging of parameters, MLflow doesn’t inherently offer the same depth of versioning control for the complete environment as Valohai.

Integration and extensibility with other tools

Provides API-driven functionality, ensuring that you can trigger every action available in the UI via API calls. This makes it highly extensible and integrable into existing workflows.

Cost management 

Offers tools for tracking and optimizing costs, ensuring resources are used efficiently.

MLflow does not provide built-in features for monitoring, managing, or optimizing costs.

Automatic parallel hyperparameter tuning

Supports automatic parallel hyperparameter tuning, letting users try multiple hyperparameter combinations simultaneously.

While MLflow can track different hyperparameters and their outcomes, automated parallel hyperparameter tuning is not a built-in feature.


Metaflow is an open source framework for developing, deploying, and operating machine-learning applications. In contrast to MLflow, its primary focus is helping data scientists and ML engineers manage the infrastructure that powers these applications.

While MLflow excels at experiment tracking, model versioning, and deployment, Metaflow addresses the often complex pipelines that feed into and arise from these models. Its emphasis on orchestrating scalable data applications provides a robust foundation, ensuring that the data feeding into models is consistent, reproducible, and scalable.

UI of one of the MLflow alternatives: Metaflow
Metaflow dashboard showing task ID, status, runtime, and model accuracy

Cases where Metaflow excels over MLflow

Originally created by Netflix, Metaflow excels over MLflow when it comes to scaling, pipeline orchestration, workflow design, and integration with third-party features.


Scaling on various platforms

Achieves data processing scalability through its integration with Spark. Scalable model deployment requires third-party tooling or the use of separate platforms.

Support for pipeline and workflow orchestration

Metaflow’s primary focus is the management of complex workflows. Its Python-centric design lets developers naturally express dependencies and orchestrate data-processing tasks.

MLflow’s dual-layered approach might require context-switching between YAML-based orchestration and Python-scripted logic.

It was created with the intention of making life easier for data scientists. It prioritizes intuitive design, allowing users to focus on the logic of their workflow rather than the underlying infrastructure.

While logging, experiment tracking, and deployment are user-friendly through client libraries and a CLI, infrastructure management is left entirely to the user.

Resource scalability

Has built-in integrations for cloud providers like AWS. It allows seamless scaling by letting workflows spill over from a laptop to cloud-based resources without any code changes.

While MLflow integrates well with many cloud platforms, seamless transition from local resources to the cloud isn’t its core strength.

Data management

Provides strong capabilities in data versioning and lineage, allowing data scientists to use versioned datasets and manage dependencies between data and code.

Primarily tracks parameters, metrics, and model artifacts. Versioning of datasets is not a core feature.

Integrated environment

Combines both the local development environment and production setups. This means data scientists can develop and test on their local machines and then move to a cloud-based system for production runs without altering the code.

While MLflow supports both local and cloud-based tracking, transitioning between environments is typically not fluid.


Has a built-in ability to checkpoint intermediate data in workflows, ensuring that if a long-running job fails, it can resume from the point of interruption.

Does not support intermediate checkpointing within workflows out-of-the-box.

Namespace isolation

Namespaces allow multiple users to collaborate without interfering with each other’s experiments and workflows.

Isolating workspaces requires the setup of separate instances.

Amazon SageMaker

Amazon SageMaker is a fully managed service on AWS that enables users to build, train, and deploy machine learning models in the cloud. Included in Amazon’s suite of cloud services, SageMaker offers capabilities such as logging machine learning experiments, tracking model performance, and storing relevant metadata and artifacts.

Amazon SageMaker is one of the oldest offerings on the market and is particularly interesting for those already invested in the AWS ecosystem.

UI of one of the MLflow alternatives: Amazon Sagemaker
Amazon SageMaker’s experiment analysis capabilities include visualizations like loss curves and confusion matrices

Cases where Amazon SageMaker excels over MLflow

Amazon SageMaker shines for teams looking to manage the entire machine learning lifecycle on one platform and native integration with the AWS ecosystem.

Amazon SageMaker

Managing the end-to-end ML lifecycle from notebooks to production

Provides a comprehensive suite of tools that cover everything from data labeling to model training, hyperparameter tuning, model serving, and monitoring machine learning models in production.

Primarily focuses on experiment tracking, model versioning, and deployment.

Infrastructure management

Automatically manages infrastructure and scaling for training and deployment. Users don’t need to provision or manage servers. SageMaker handles everything from spinning up GPU clusters to tearing them down post-training.

Users must set up and manage their resources, whether cloud-based or on-premises.

User management and access control

Fine-grained access control through AWS’s Identity and Access Management capabilities.

Limited built-in functionality. Users need to integrate identity management and authentication themselves.

User management and access control

Fine-grained access control through AWS’s Identity and Access Management capabilities.

Limited built-in functionality. Users need to integrate identity management and authentication themselves.

Pre-built containers for popular algorithms and frameworks

SageMaker comes with optimized algorithms and pre-built containers for many popular deep learning frameworks you can use for training without further setup.

MLflow does not provide any built-in algorithms. It tracks and logs parameters and metrics but relies on external libraries or user-provided algorithms for actual modeling.

Integration with the AWS ecosystem

Seamlessly integrates with other AWS services, enabling, e.g., easy data ingestion from sources like S3 or Redshift.

As a platform-agnostic tool, MLflow provides integrations with many third-party solutions and enables users to develop their own. MLflow has built-in integration with Amazon SageMaker’s model serving component.

Model deployment and hosting

Offers a managed environment for deploying machine learning models as RESTful APIs with auto-scaling, A/B testing, and integration with other AWS services.

Requires manual configurations to achieve similar security standards and does not come with compliance certifications.

Security and compliance

Inherits AWS’s robust security features, including encryption, VPC support, IAM roles, and compliance certifications.

Requires manual configurations to achieve similar security standards and does not come with compliance certifications.

Integrated Development Environment (IDE)

Offers SageMaker Studio, an integrated development environment (IDE) for building, training, and deploying machine learning models. This includes data wrangling tools, Jupyter notebooks, and debugging tools.

No integrated development environment.

Vertex AI

Vertex AI is the fully managed machine learning solution of Google’s Cloud Platform. Vertex helps you build, train, deploy, and monitor machine learning models at scale. It provides a unified platform for the entire ML lifecycle, from data preparation and model training to model deployment, model experiment tracking, and monitoring.

Google has firmly established itself as a leading machine learning and AI player, with many advances coming from the tech giant’s research branch and infrastructure teams. Much of this experience has found its way into Vertex. The platform is especially interesting for teams working on Google Cloud or looking to leverage leading data management solutions like BigQuery.

UI of one of the MLflow alternatives: Vertex AI
Vertex AI provides a notebook environment with pre-configured Google Cloud Platform services integrations

Cases where Vertex AI excels over MLflow

Vertex AI shines when it comes to managing scalable training and deployment infrastructure and seamless integration with the Google Cloud platform.

Vertex AI

Training infrastructure

Provides managed infrastructure for training machine learning models and auto-scaling resources as needed. It also offers hyperparameter tuning.

While MLflow tracks and logs training runs and hyperparameters, it doesn’t provide its own training infrastructure.

User management and access control

Limited built-in functionality. Users need to integrate identity management and authentication themselves.

AutoML integration

Comes with AutoML, which automatically searches for the best model architectures and hyperparameters for a given dataset.

Primarily a tool for managing and tracking custom model development. Doesn’t have built-in AutoML capabilities.

Deployment and serving

Has built-in capabilities for deploying models as scalable endpoints, supporting both online and batch predictions. Integrates seamlessly with other Google Cloud Platform services.

Provides a model registry and basic model deployment capabilities, but setting up scalable online serving typically requires additional tooling or the use of a separate platform.

Explainability and fairness tools

Provides tools to interpret model predictions and evaluate models for fairness concerns.

Focuses on model tracking and management without built-in features for model explainability or fairness.

Integration with other tools and platforms

Seamless integration with other Google Cloud Platform services like BigQuery, Dataflow, and Pub/Sub.

Flexible and platform-agnostic, with integrations for many third-party platforms. Users can develop custom integrations.

Model monitoring

Provides capabilities for continuous model monitoring and alerting users to any anomalies in model predictions.

Offers logging and tracking of model metrics but doesn’t have out-of-the-box continuous monitoring features.

Final thoughts

MLflow has become a cornerstone of many machine learning platforms due to its flexibility and availability as an open source tool. However, as teams scale up, limitations around collaboration, deployment, and advanced functionality like user management, fine-grained access control, and collaboration often emerge.

Whether MLflow is the right choice depends heavily on your team’s needs and existing MLOps stack. But for many, the advanced visualization, hosting, permissions, and ease of use of alternative platforms like provide compelling reasons to move away from MLflow.

Editor’s note

Do you feel like experimenting with

In this article, you learned the key considerations when evaluating alternatives to MLflow, including deployment requirements, functionality needs, and ease of transition. You and your team should also weigh the benefits of community-driven development versus commercial solutions. There is no universal best choice – the optimal machine learning platform depends on the team’s and organization’s requirements.

Table comparison of alternatives to MLflow based on core experiment tracking features

In the table below, I’ve summarized the key capabilities of alternatives to MLflow discussed in this article.

Managed MLflow
Azure ML
Weights & Biases
AWS SageMaker
Vertex AI
Managed MLflow
Azure ML
Weights & Biases
AWS SageMaker
Vertex AI
Open source
Managed hosting
Metrics tracking
Parameters logging
Artifact storage
Environment tracking
Run comparisons
Collaboration & sharing
Code versioning
Integration with source control
Model registry

Alternatives to MLflow FAQ

  • Some leading commercial options like MLflow are, Comet ML, Weights & Biases, Valohai, and Databricks ML. Each platform offers added capabilities around user management, collaboration, experiment tracking, model monitoring, and ease of deployment.

  • The difficulty depends on how heavily customized your MLflow implementation is. If you’ve kept your MLflow deployment close to its original setup, many tools on the market allow for an almost seamless transition.

    However, with increased customization comes the need for a more strategic and planned migration approach. Before making a move, assessing the compatibility of potential alternatives is beneficial, especially regarding data ingestion and compatibility of the model serving infrastructure.

  • While on the surface, many alternatives to MLflow might seem to offer comparable tracking capabilities, the depth, flexibility, and collaborative features can vary widely. When evaluating an alternative, it’s essential to consider not just the presence of features but thoroughly evaluate the extensibility and user experience.

    You should also consider the pricing and contract options offered. Platforms that cater to large enterprise customers might not be a good fit for small teams, who generally benefit from a pay-as-you-go pricing model.

  • Extending MLflow is viable if its core capabilities align with your requirements and you’re looking to add specific functionalities. However, many teams find that MLflow already does not support their core machine learning workflows. If you would need to invest significant development effort just to cover the basics, it’s usually better to look for a different solution.

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