Magic Quadrant for Cloud AI Developer Services

How does Gartner define the Cloud AI Developer Services market in 2024?

Gartner defines cloud AI developer services (CAIDS) as cloud-hosted or containerized services and products that enable software developers who are not data science experts to use artificial intelligence (AI) models via APIs, software development kits (SDKs) or applications. Core capabilities include automated machine learning (autoML) including automated data preparation, automated feature engineering and automated model building, and model management and operationalization for language, vision and tabular use cases. Optional and important complementary capabilities include AI code models and assistants. Cloud AI developer services help organizations embed intelligence, such as AI and ML insights, into their applications. These services democratize and increase the availability of AI and ML to software engineers through the automation and features offered.

Key Facts for Magic Quadrant for Cloud AI Developer Services in 2024

Publication Date: 29-Apr-2024

Document ID: G00796065

Coverage: Global

Authors: Jim Scheibmeir, Arun Batchu

Core Purpose: Cloud AI developer services help deliver AI benefits by offering an end-to-end platform to design, develop, deploy and monitor language, vision and tabular AI models for software developers who are not data science experts.

How was the Cloud AI Developer Services market evolved in 2024?

By 2027, spending on AI software is projected to grow to $297.9 billion, with a CAGR of 19.1%

55% of software engineering leaders identified AI/ML engineer as the most in-demand role in their organization

65% of software engineering leaders said applying AI/ML to applications was a high- or medium-level pain point

Few software engineers have the skills required to embed AI/ML capabilities into applications

The global addressable market for AI code assistants stands at $10.08 billion ARR, with only 14% adoption among 28 million professional developers

Vendors are expanding industry-specific ML models across education, life sciences, professional services, transportation, and banking

CAIDS vendors are improving capabilities across language, vision, tabular data, and AI code assistants

Gartner plans to retire the CAIDS market and introduce multiple new Magic Quadrants, including one for AI Code Assistants in 2024

What product features are required to be included in this year's evaluation?

Tabular services: Using autoML services for tabular use cases, developers can create custom models. These services allow developers without significant ML or data science skills to customize the vendor-provided ML services or build purpose-specific ML using enterprise structured data.

Language services: These services offer developers the ability to cluster documents by topic, analyze sentiment, and summarize and generate text. They can include natural language processing/understanding, speech to text, natural language generation using foundation models, text to speech, translation, sentiment analysis and text analytics.

Vision services: These services utilize image and video technology as both input and output. Image labeling, segmenting and boundary definition are common capabilities. Other vision services can include image generation using foundation models, video AI and ML-enabled optical character recognition (OCR).

What are the common features of top products in the Cloud AI Developer Services space?

Automated data preparation: These services can prepare datasets, such as text, tabular or image data, to be used for training the models. These services can cleanse and augment datasets from raw data provided by the organization and can include data visualization, text embedding and vector database integration.

Automated feature engineering and model building: These models require developers to provide datasets that can be used to train the models, as well as metadata tags that have the attributes they want the models to be trained to identify and predict. Alternatively, feature engineering can automate data evaluation and metadata tagging. These services can recommend or select the potentially best algorithms that can be used to build and optimize the model.

Model management/operationalization: These services may offer model factory functionality to automate the building of ML pipelines, including model training, deployment, and monitoring and management of models in production. This service helps to orchestrate across various models, APIs and databases such as retrieval augmented generation (RAG) and agent. The service may also provide for packaging and provisioning of infrastructure for the models created, such as Kubernetes containers. The services should also automate creation of the APIs needed to access the models. This capability allows developers to monitor models in test and production environments to assess model performance and drift. These services may provide automated retraining of the model and provision for replacing an outdated model with a better one. Additional features may include business key performance indicators (KPIs) for model value and the ability to assess the quality of data impacting the model performance. These capabilities may integrate with and enable broader MLOps platforms.

Responsible AI: These services analyze datasets for potential bias, explainability, interpretability, validity and reliability. They can be used to filter both large language model (LLM) input and output for toxic or violent material. Responsible AI services and capabilities involve creating AI systems that are transparent, explainable, fair and unbiased. Responsible AI also aims to minimize harmful impacts and ensure privacy and security.

Natural language understanding: A subset of natural language processing, natural language understanding deals with machine comprehension. It takes textual input and extracts metadata from the text.

Speech to text: This is a subset of computational linguistics that converts analog input to text output. The text output can be the final product, or it can be entered into a natural language understanding model. Many computing devices, such as personal computers and smartphones, have a built-in automated speech recognition (ASR) capability.

Text to speech: This service converts textual input into analog output/speech. It may offer customizing of the speech with unique vocabulary or applying different voicings such as accents.

Natural language generation: This service creates humanlike text using LLMs trained on extensive sets of data. Text generation can be used in a wide variety of applications such as drafting emails, writing code, creating written content, translating languages and answering questions.

Translation: This service takes text input from the source language and converts it to a target language. This is a challenging task because different languages have distinct structures. It is not as simple as translating a word from one language to the corresponding word in another language. Recent advancements in translation services utilize LLMs.

Image recognition: This service normally identifies what objects or people are contained in an image. Some implementations can also enable multimodal-chat-based Q&A, identify other attributes in the image such as colors or patterns, or offer customizations for object identification.

Video AI: This service normally combines image recognition and ASR to identify people and other objects in a video and to create a transcript for the audio. Some services also track the movement of people across multiple frames of the video.

ML-enabled OCR: This service converts electronic images of typed, handwritten or printed text, or text in images or video, into machine-encoded text and adds metadata to the content. The service also uses ML to classify the information in a given field based on its content.

Image/video generation: This service involves the creation of new, unique images and videos through algorithms and foundation models. This can be done either from inputs such as a series of images, text prompts or other forms of data. The generated images and video, which can be highly realistic or abstract in nature, are often used in a range of fields, including computer vision research, gaming, media and digital art.

AI code assistance: This service helps developers in software development activities across the life cycle of an application. Developers prompt the assistants with natural language, code snippets and triggers to generate code blocks. The assistants also analyze, explain and refactor code; generate documentation; help with testing activities; and translate between programming languages.

Scope Exclusions

Vendors that offer CAIDS only as part of a professional services contract where services are used exclusively by the vendor's consultants

Vendors that market and message exclusively to target users who are not professional software developers

Vendors with insufficient geographic reach (less than three regions with paying customers)

Vendors generating less than $15 million in CAIDS revenue in 2023

Vendors with fewer than 15 paying customers as of December 1, 2023

Inclusion Criteria

Vendors must, among other requirements:

Go-to-market strategy focused on professional software developers

Include all must-have capabilities of a CAIDS platform per Market Definition

Generated at least $15 million in revenue from CAIDS offerings (excluding professional services) in 2023

At least 15 paying customers as of December 1, 2023 in at least three of the following regions: North America, South America, EMEA, China, Central Asia, Asia/Pacific

Ability to Execute — Relative Weighting

Product or Service - High

Overall Viability - Medium

Sales Execution/Pricing - Medium

Market Responsiveness/Record - Medium

Marketing Execution - Medium

Customer Experience - Medium

Operations - Medium

Completeness of Vision — Relative Weighting

Market Understanding - High

Marketing Strategy - Medium

Sales Strategy - Medium

Offering (Product) Strategy - High

Business Model - Medium

Vertical/Industry Strategy - High

Innovation - High

Geographic Strategy - Medium

FAQs

Q: What does this research cover?

A: This research evaluates cloud AI developer services (CAIDS) vendors that provide cloud-hosted or containerized services enabling software developers without data science expertise to use AI models via APIs, SDKs or applications. It covers automated machine learning (autoML), model management and operationalization for language, vision and tabular use cases, plus AI code models and assistants. The report assesses vendors across multiple dimensions including product capabilities, market understanding, innovation, vertical strategies, geographic reach, and customer experience.

Q: Who should use this research?

A: Software engineering leaders should use this research to evaluate and select CAIDS vendors based on their specific needs. The research helps organizations: 1) Understand vendor capabilities across tabular, language, vision services and AI code assistance; 2) Assess vendors on responsible AI, generative AI maturity, geographic support, and deployment flexibility; 3) Identify key differentiators between vendors; 4) Make informed decisions about which vendors can help bridge the AI/ML skills gap in their development teams; 5) Understand market trends and future directions in cloud AI developer services. Organizations should prioritize vendors with strong responsible AI features, explainable models, and built-in bias detection.

Q: What are the mandatory features of vendors included in this market?

A: To be included in this Magic Quadrant, vendors must provide all three must-have service areas: (1) Tabular services using autoML that allow developers without significant ML/data science skills to create custom models from enterprise structured data, (2) Language services offering capabilities like document clustering, sentiment analysis, text summarization and generation, natural language processing, speech-to-text, text-to-speech, and translation, and (3) Vision services utilizing image and video technology for capabilities like image labeling, segmentation, boundary definition, image generation, video AI, and ML-enabled optical character recognition (OCR). These services must be accessible via APIs, SDKs, or applications and designed for software developers who are not data science experts.

Q: What are some reasons for not being included in this report?

Offering CAIDS only through professional services contracts where services are used exclusively by vendor consultants rather than being available to customer developers
Marketing and messaging exclusively to non-developer audiences rather than professional software developers
Insufficient revenue - generating less than $15 million from CAIDS offerings (excluding professional services) in 2023
Limited customer base - having fewer than 15 paying customers as of December 1, 2023
Inadequate geographic presence - operating in fewer than three of the specified regions (North America, South America, EMEA, China, Central Asia, Asia/Pacific)
Not including all must-have capabilities: tabular services, language services, and vision services as defined in the market definition

Q: What differentiates Ability to Execute vs. Completeness of Vision?

A: Ability to Execute focuses on the vendor's current capabilities and performance in delivering CAIDS solutions. It emphasizes the product/service quality (weighted High), operational excellence, sales effectiveness, market responsiveness, and customer experience. This dimension assesses how well vendors are executing today. Completeness of Vision evaluates the vendor's future-focused strategy and innovation capabilities. It places high weight on Market Understanding, Offering Strategy, Vertical/Industry Strategy, and Innovation - assessing how well vendors understand emerging trends, can articulate product roadmaps, serve specific industries, and invest in next-generation capabilities like generative AI. Vision criteria assess strategic direction rather than current execution.

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