Magic Quadrant for Data Science and Machine Learning Platforms

Data Science and Machine Learning Platforms Vendor Product Comparisons 2024

Key features and pros and cons of top Data Science and Machine Learning Platforms products:

Alibaba Cloud - Challenger

Pros:

Focus on data governance. DataWorks, Alibaba Cloud's platform as a service (PaaS), provides a leading platform for governance and managing data quality throughout the ML life cycle.
Cost-control capabilities. High-performance computing innovation and cost-management tools enable users to lower AI training costs by optimizing resource utilization.
Foundation model availability. Alibaba Cloud's own set of large language models (LLMs), community and open-source models from partners provide its clients access to various multimodal foundation models.

Cons:

Complexity. With Alibaba Cloud still evolving its PAI's low-code capabilities for model development, high technical competence is required to utilize all platform features.
Limited presence outside of China. Enterprises outside of China may encounter limited support, communities and partners to help with their implementation.
Corporate restructuring. Alibaba Group is undergoing organizational and management changes that raise uncertainty about the future direction of services on Alibaba Cloud.

Altair - Leader

Pros:

Market understanding. Altair's RapidMiner platform addresses enterprise pain points for enterprise GenAI and ML adoption, measuring success by completed project deployments rather than platform usage.
Industry focus. RapidMiner's 2022 acquisition by Altair has opened new opportunities, particularly in asset-centric industries and scientific applications.
Pricing. Altair's pricing structure is flexible and not based on named user licenses or consumption levels, but on units tied to usage of the Altair software portfolio.

Cons:

Market traction. Altair is placed in the lower quartile of Gartner's Customer Interest Indicator, indicating a lack of awareness of capabilities among end users.
Product coherence. Integration between rebranded and legacy products with overlapping capabilities in the Altair RapidMiner platform is continuing and will need to be completed over time.
Acquisition friction. The embedding of RapidMiner within the Altair ecosystem can increase end-user demand for support, which could potentially result in lower levels of customer interaction.

Alteryx - Niche Player

Pros:

Insights-driven vision. Alteryx has targeted its platform to all roles involved in data science, aiming to increase efficiency through augmentation.
User sentiment. Alteryx has a large, passionate user community and a long history of customer satisfaction.
Automation and augmentation. Alteryx simplifies complex analytics processes and data preparation through AI-generated augmentation of key tasks and automation of end-to-end processes.

Cons:

Recent acquisition. In March 2024, Alteryx was acquired by two private equity companies. The implications of this acquisition on the company's existing partnerships and future direction are still being determined.
Competitive differentiation. Alteryx struggles to stand out in a crowded market where much of the functionality that brought it initial success has been replicated or enhanced.
Expert data scientist focus. Alteryx is known for its low-code platform, to which it has recently added code-centric functionality. Data scientists will need to be convinced the platform can meet their needs.

Amazon Web Services - Leader

Pros:

Security compliance. AWS provides access to a variety of GenAI foundation model providers with enhanced privacy and security guarantees, including fine-tuning and retrieval-augmented generation (RAG) use cases.
Custom AI infrastructure. AWS has made significant investment in providing custom hardware and services, such as SageMaker HyperPod for training and serving large models at scale.
Training and community. AWS offers a number of in-person and virtual events targeted at different user groups, including re:Invent, Summits and Dev Days.

Cons:

Core data science. AWS has given more attention to ML engineering than decision augmentation-based data science, causing end users to explore additional services to provide core functionality.
Complexity. AWS' large portfolio for data science, ML and GenAI can cause difficulties in identifying the right product for a business problem.
GenAI market sentiment. AWS' own Titan foundation models have limited brand recognition, weakening its position as an innovative model provider.

Anaconda - Niche Player

Pros:

Large user community. The open-source Anaconda Python Distribution and conda package and environment management tool are well-known and used by thousands of data scientists.
Tooling. Anaconda offers open-source tools that expert data scientists need to provide core data science and machine learning engineering capabilities.
Low barrier to entry. Anaconda offers a free edition of its platform that is well-used, well-maintained and supported by many different groups, significantly lowering the barrier to entry for new practitioners.

Cons:

Scope of vision. Anaconda shows a lack of vision in providing out-of-the-box capabilities for data science tasks, in favor of relying on open-source components.
Target personas. Anaconda targets expert data scientists without addressing the needs of data engineers, MLOps engineers and team leads.
Differentiation. The commercial offering of the platform does not offer significant benefits for users, as package management and DSML libraries are available from other vendors in the market.

Cloudera - Visionary

Pros:

Hybrid and multicloud. Cloudera provides an integrated data and DSML platform built for hybrid cloud, multicloud and on-premises environments, and complex workloads.
Secure open source. Cloudera leverages popular open-source components that are familiar to data scientists, together with enterprise-level security that minimizes lock-in.
Use-case templates. Cloudera offers a wide selection of prebuilt GenAI and DSML use-case templates for a variety of foundation models and techniques.

Cons:

Complexity. Although low-code functionality, such as augmented assistance and visual workflows for business and domain-focused personas, is improving, it is limited compared with other vendors in this Magic Quadrant.
Partnerships. Compared with vendors in this Magic Quadrant, Cloudera has fewer stable partnerships with systems integrators that have dedicated resources to help with enterprise deployments.
Market position. Cloudera is still developing the vision for its platform to move the company from being known as a data vendor and toward being also known as an AI vendor.

Databricks - Leader

Pros:

Unified platform. Databricks' platform combines a lakehouse platform and centralized governance with DSML functionality to simplify data and ML engineering team processes and management.
Acquisitions. Recent acquisitions, including MosaicML, enable Databricks' users to utilize large language models quickly and at lower cost than competitors.
Future-facing strategy. Databricks has invested in its vision of the future of user demands by bringing GenAI and low-code capabilities to its platform.

Cons:

Performance. Gartner Peer Insights reviews show some users are unhappy with the performance tuning and cluster management required when developing and deploying DSML workloads.
Upgrade friction. Databricks' rapid product expansion leads to new product versions that can require customers to keep up with updates and ensure stability of existing workloads.
Solution alignment. Databricks' all-in-one solution and lakehouse architecture requires additional design considerations when used with composable, hybrid data architectures.

Dataiku - Leader

Pros:

Collaboration. Dataiku's platform is designed to enable diverse roles to work together to operationalize AI through bringing together code and low-code development.
GenAI vision. Dataiku, along with partners, founded the LLM Mesh initiative focused on providing enterprisewide usage of GenAI models to abstract away the complexity of managing cost and performance.
People focus. Dataiku's focus on the impact of AI on people and business through change management gives AI leaders confidence to utilize the technology enterprisewide.

Cons:

User community. Dataiku's user community is not as vast, accessible or at the same level of interaction as those of other vendors in this Magic Quadrant, limiting the number of support channels available to users.
Pricing. Dataiku has a complex pricing model based on platform fees and per-user licenses, formed around four types of personas.
Administration. Maintaining the on-premises offering of the platform can be resource-intensive, especially for upgrading and resource management.

DataRobot - Leader

Pros:

Ease of use. DataRobot's abstraction of GenAI and predictive model building simplifies processes for data scientists and business users.
Focus on value generation. DataRobot uses targeted customer-facing teams for customer engineering and applied AI to accelerate value delivery.
Market understanding. DataRobot's strategy focuses on collaboration between multiple participants in end-to-end DSML activities, enabling business-focused personas to ensure their needs are addressed.

Cons:

Operational practices. DataRobot has experienced changes in its leadership team and high staff turnover, which may hamper the company's efforts to maintain customer satisfaction.
Pricing. DataRobot's pricing model and license cost are among the highest in this report.
Product strategy. DataRobot promotes itself as an AI platform but its focus on data management, visualization, exploration, annotation and labeling are behind those of peers in the market.

Domino Data Lab - Visionary

Pros:

Security. Domino Data Lab differentiates itself in the market by focusing on verticals that require increased levels of security.
Governance. Domino Data Lab's governance of data science and machine learning assets enables the vendor's focus on regulated industry verticals.
Flexibility. Extensive compatibility with developer tools, languages and development environments makes Domino Data Lab's platform an option for enterprisewide DSML deployment.

Cons:

Market penetration. Two-thirds of Domino Data Lab's customers are concentrated in four industries, limiting its potential to appeal to a broad range of enterprises.
Complexity. Domino Data Lab is focused on serving large, expert data science teams and is the best option for enterprises with purely line-of-business (LOB) user needs.
Low code. Domino Data Lab offers low-code options, but it lags behind the offerings of other vendors in this Magic Quadrant.

Google - Leader

Pros:

Foundation models. Foundation models available from Google's in-house R&D and third-party providers, for both commercial and open use, ensure that end users benefit from cutting-edge innovation.
Balanced portfolio. Investment in GenAI has not derailed Google's attention from enhancing existing DSML methods and capabilities in its Vertex AI platform.
Delivery. Google rolled out more than 500 updates to Vertex AI in 2023, further enhancing its positioning as a platform centered on innovation and meeting end-user needs.

Cons:

Data and AI governance. Unification of data and AI governance capabilities lags behind competitors.
Platform ecosystem. Vertex AI makes sense as a DSML platform for enterprises that have invested or will invest in Google Cloud Platform as their data storage and processing platform of choice.
Core data science. Google's focus on core data science use cases is weak compared with its focus on machine learning.

H2O.ai - Visionary

Pros:

Customer collaboration. H2O.ai works closely with customers to identify, remediate and co-create product functionality.
Generative AI. H2O.ai has trained its own open-source foundation model and created a GenAI ecosystem for different foundation models, vector databases, hardware and hosting.
Open-source community. A large community of data scientists uses open-source H2O.ai, strengthening the vendor's understanding of data scientists' needs as well as key trends and pain points.

Cons:

Dependence on partners. H2O.ai leverages partnerships for some DSML features, such as data management, data preparation and governance.
Limited presence outside of key markets. H2O.ai has a presence in telecommunications and consumer packaged goods industries, but most customers are concentrated in banking, financial services and the public sector.
Product portfolio coherence. The division of functionality between products means the ability to collaborate between multiple personas and stakeholders is limited.

IBM - Challenger

Pros:

Investment in responsible AI. IBM has partnered with Meta to co-launch the AI Alliance to support open science and open innovation in AI.
Openness and flexibility. IBM's tight integration with Red Hat OpenShift, and partnership ecosystem, provides adaptability and flexibility for enterprise needs in multiple deployment scenarios.
Market understanding. IBM has targeted governance of AI as a market need, aligning with the increased demand from enterprises with security and compliance requirements.

Cons:

Market traction. The adoption of watsonx is typically seen among customers who already have a significant investment in other IBM products.
Product coherence. watsonx is the latest marketing name for the Watson brand. watsonx itself consists of three core components, which could cause confusion for buyers.
Pricing structure. Gartner clients report difficulty navigating IBM and watsonx's complex pricing.

KNIME - Niche Player

Pros:

Democratization. KNIME's desktop analytics platform is available as a free and unrestricted download, and it enables users that have a range of analytical skill sets, as well as those users getting started in the data science field.
Data science depth. KNIME's range of out-of-the-box nodes and components covers the entire spectrum of DSML techniques, from simulation to econometrics and deep learning.
Use cases. KNIME's community of more than 400,000 users has contributed a wide variety of industry and horizontal solutions, in addition to those from KNIME and its partners.

Cons:

Global reach. KNIME has direct presence in Europe and the U.S., and uses partners to serve clients in other regions.
Market traction. KNIME is placed in the lower quartile of Gartner's Customer Interest Indicator, indicating a lack of awareness of capabilities among end users.
Target personas. KNIME's strategy focuses on enabling all types of users through low-code development, but code-first development needs are underserved.

MathWorks - Niche Player

Pros:

Longevity. MathWorks is a longstanding vendor that provides a stable set of core DSML tools and continues to evolve with market trends.
Embedded AI. MathWorks allows engineers to embed AI into industrial devices, appliances and IoT assets without needing specialist deployment knowledge.
Safety-critical systems. MathWorks is ubiquitous in sectors where safety is paramount, such as the aerospace and automotive industries, providing a suite of tools for robust DSML development.

Cons:

Innovation. MathWorks does not operate at the cutting edge of GenAI, and many use cases outside of asset-centric industries may not be as well-served.
Complexity. Despite the presence of some low-code tooling, MathWorks is not a toolset for newcomers to DSML development.
Trajectory of vision. MathWorks continues to target technical LOB users, such as engineers, limiting its usage in other business domains.

Microsoft - Leader

Pros:

Enterprise GenAI. Microsoft has continued to innovate in its offering of foundation models through its Azure ML model catalog by providing a range of models, deployment options and pricing options.
Upskilling. Microsoft produces an extensive range of training resources aimed at beginners, experts and executives in the DSML space, including new Azure-based GenAI certification.
Research and development. Microsoft's own research group has contributed with its own smaller foundation models, prompt engineering frameworks and new architectures for retrieval-augmented generation (RAG) implementation.

Cons:

Separate product lines. Frequent rebranding of components and separate product lines within Azure ML create friction for data science teams looking for a unified platform that supports collaboration.
Data exploration and visualization. Exploratory data visualization capabilities in Azure ML are provided through Power BI. Power BI's offering through Microsoft Fabric will trigger reevaluation for many enterprises.
Copilot integration. GitHub Copilot requires additional assessment for full functionality across Azure ML compute instances.

Posit - Niche Player

Pros:

Corporate citizenship. Posit's corporate social responsibility commitments include community initiatives and programs for public good written into its charter, aligning with many corporate ESG objectives.
Partnerships. Expanding partnerships with major cloud and data platforms will make integration and usage of enterprise data with its platform easier.
Dual usage. Posit's products can function as a low-cost DSML, analytics and business intelligence (ABI) platform instead of separate solutions for data science teams and analysts.

Cons:

ML engineering. Posit's focus is on insight delivery and not on elements of ML engineering, such as deployment, monitoring and governance functionality.
Low code. With limited low-code functionality, Posit's platform is geared toward data scientists, with use for business analysts restricted to dashboard or application usage.
GenAI. Posit lacks a clear roadmap for GenAI in areas such as foundation model development, fine-tuning LLMs and agent development.

SAS - Leader

Pros:

Partner network. SAS's network of more than 1,700 partners across technology, resellers and services leads to greater adoption of its platform, with a global reach.
Strong industry focus. SAS's customized models, workflows and solutions reduce the time to value for a variety of customer departments and business units.
DecisionOps. SAS is one of the few providers to focus on operationalization of decisions as well as models, accelerating business value through process optimization.

Cons:

Cost. Discussions with users of Gartner's client inquiry service, together with evidence from Gartner Peer Insights, indicate the SAS platform is perceived as expensive compared with competitors' offerings. As the company works toward an initial public offering (IPO), further pricing amendments can be expected.
SAS language. SAS has added open-source support, but the decline in usage of SAS as a programming language reduces the attractiveness of the product compared with other platforms.
GenAI functionality. SAS has not been an early mover with GenAI, and many GenAI functions embedded in the platform are in preview form.

FAQs

Q: Which vendors are Leaders in 2024?

A: Altair, Amazon Web Services, Databricks, Dataiku, DataRobot, Google, Microsoft, SAS

Q: What does "Leader" mean in MQ terms?

A: Leaders in this market have a mature, refined, and targeted company and platform strategy that incorporates and leverages GenAI to drive their customers' business value. They see opportunities for leveraging GenAI that other providers may not see or have made significant investments above and beyond standard offerings. They have the capability to innovate at a speed that outperforms other vendors. In addition, they can clearly articulate how they provide value to the multiple types of personas involved in the process of building data science and machine learning models.

Q: How do I interpret a Gartner Magic Quadrant?

A: https://www.gartner.com/en/research/methodologies/magic-quadrants-research

Report:

Top Vendor Products for Data Science and Machine Learning Platforms in 2024

Data Science and Machine Learning Platforms Vendor Product Comparisons 2024

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FAQs

Q: Which vendors are Leaders in 2024?

Q: What does "Leader" mean in MQ terms?

Q: How do I interpret a Gartner Magic Quadrant?

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