For many companies, machine learning still sounds complex, expensive and reserved for teams of rare specialists. If you plan to build the next Netflix recommendation engine, that’s true. However, machine learning as a service (MLaaS) has changed the picture. Today, you can start an ML project without large investments. For a company that is new to data science, this is often the right way to begin.
One of the most inspiring ML stories is about a Japanese engineer who wanted to help his parents sort cucumbers on their farm. He had no ML experience and no big budget. Still, he learned TensorFlow and trained a model that sorted cucumbers by quality.
With cloud ML services, even a small team can build working models and get valuable predictions. In this article, we compare the leading machine learning as a service platforms and explain what to consider when you choose one.
What Is Machine Learning as a Service?
Machine learning as a service is an umbrella term for cloud platforms that take care of most ML infrastructure. They help you prepare data, train and evaluate models, deploy them and serve predictions. Your applications then get those predictions through a REST API.
Four vendors lead the market: Amazon Web Services, Microsoft Azure, Google Cloud and IBM. Each of them offers three layers of services:
- ML platforms for data scientists who build and train their own models;
- ready-made AI APIs for speech, text, images and video, which require no ML expertise;
- generative AI services that give access to large language models (LLMs) and other foundation models.
This review won’t replace the documentation. Instead, it shows what each platform offers and what to look for when you compare them. Keep in mind that the market changes fast. Several services from earlier comparisons, such as Amazon Machine Learning, Azure Machine Learning Studio (classic) and IBM Visual Recognition, have already been retired.
Amazon SageMaker AI and Amazon Bedrock
AWS offers the broadest set of ML services, and many companies already run their infrastructure on it.
Amazon SageMaker AI
Amazon SageMaker AI is the main AWS platform for building, training and deploying models. It replaced the older, fully automated Amazon Machine Learning service, which AWS no longer develops.
SageMaker covers the whole ML lifecycle:
- managed Jupyter notebooks and SageMaker Studio, a web IDE for ML;
- SageMaker Autopilot (AutoML) for automatic model training on tabular data;
- built-in algorithms such as XGBoost, linear learner, k-means and DeepAR forecasting;
- support for PyTorch, TensorFlow, scikit-learn and Hugging Face models;
- managed endpoints for real-time and batch predictions;
- MLOps tools: pipelines, a model registry and model monitoring.
In short, SageMaker gives experienced data scientists full freedom. At the same time, its automated features help teams that don’t want to tune every step manually.
Amazon Bedrock
Amazon Bedrock provides access to foundation models from Anthropic, Meta, Mistral, Amazon and other providers through one API. You can customize models with your data, build retrieval-augmented generation (RAG) and create AI agents. As a result, Bedrock is often the fastest way to add generative AI to an AWS-based product.
AWS AI Services
For common tasks, AWS offers ready-made APIs:
- Amazon Rekognition analyzes images and video: objects, faces, text and unsafe content;
- Amazon Transcribe converts speech to text, including call center audio;
- Amazon Polly turns text into natural speech;
- Amazon Comprehend extracts entities, key phrases and sentiment from text;
- Amazon Translate provides neural machine translation;
- Amazon Textract extracts text and tables from scanned documents;
- Amazon Lex helps you build chatbots and voice assistants;
- Amazon Personalize creates real-time recommendations.
Microsoft Azure Machine Learning and Microsoft Foundry
Microsoft offers a strong combination of an ML platform, generative AI tools and APIs. It is a natural choice for companies that already use Microsoft 365, .NET or Azure.
Azure Machine Learning
Azure Machine Learning is the main Azure platform for training and deploying models. It works for both beginners and experts:
- the designer offers a drag-and-drop interface for building ML pipelines;
- automated ML trains classification, regression and forecasting models with little or no code;
- notebooks and the Python SDK give data scientists full control;
- it supports PyTorch, TensorFlow, scikit-learn and ONNX Runtime;
- built-in MLOps tools handle pipelines, model registry, deployment and monitoring;
- data labeling projects help teams prepare training data.
The older Azure Machine Learning Studio (classic) was retired in 2024, so new projects should use the current Azure Machine Learning workspace.
Microsoft Foundry and Azure AI Services
Microsoft Foundry (formerly Azure AI Foundry) is the Azure hub for generative AI. It provides access to OpenAI models and many other models, along with tools for RAG, evaluation and AI agents.
In addition, Azure AI services offer ready-made APIs:
- Speech for speech-to-text, text-to-speech, translation and speaker recognition;
- Language for sentiment analysis, entity recognition, summarization and conversational language understanding;
- Translator for text and document translation;
- Vision for image analysis, OCR and face detection;
- Document Intelligence for extracting data from forms and invoices;
- Content Safety for detecting harmful content.
Note that several older Azure APIs, such as LUIS, QnA Maker and the Bing Search APIs, have been retired. Their functions moved to the Language service and to generative AI tools.
Google Cloud Vertex AI
Google Cloud brought its ML tools together in Vertex AI, which replaced the older AI Platform.
Vertex AI Platform
Vertex AI combines no-code and full-code options in one platform:
- AutoML trains models for tabular data, images, text and video without code;
- custom training supports TensorFlow, PyTorch, JAX and scikit-learn, with GPUs and TPUs;
- Vertex AI Workbench provides managed Jupyter notebooks;
- Model Garden offers Google’s Gemini models together with open and third-party models;
- Vertex AI Pipelines, the model registry and model monitoring cover MLOps;
- tools for building AI agents and search over your own data.
Google also created TensorFlow, one of the most popular open-source ML libraries. Unlike Vertex AI, TensorFlow is a library, not a service. Nevertheless, both work well together.
Google Cloud AI APIs
Google’s ready-made APIs benefit from the huge amount of data Google works with:
- Speech-to-Text and Text-to-Speech support a very wide range of languages;
- Translation API brings Google Translate to your products;
- Natural Language API analyzes entities, sentiment, syntax and content categories;
- Vision API labels images, detects objects and faces and reads text;
- Video Intelligence API detects objects, scenes and explicit content in video;
- Document AI extracts structured data from documents;
- Dialogflow helps you build conversational agents.
IBM watsonx
IBM has rebuilt its AI portfolio around watsonx. The platform focuses on enterprise needs, especially governance and hybrid cloud.
watsonx.ai
watsonx.ai is IBM’s studio for both classic machine learning and generative AI. It includes:
- AutoAI, which automatically prepares data, selects algorithms and builds models for classification, regression and time series;
- notebooks for custom models with popular frameworks;
- IBM Granite and third-party foundation models;
- deployment on IBM Cloud or on premises through Cloud Pak for Data.
IBM also offers watsonx.governance, which tracks how models are built and used. For regulated industries, this is a strong advantage.
IBM AI APIs
IBM still provides Watson Speech to Text, Text to Speech and Natural Language Understanding. However, its API catalog has become smaller. Personality Insights, Tone Analyzer and Visual Recognition were discontinued. Therefore, for image and video analysis, the other three vendors offer more options.
Machine Learning as a Service Platforms Compared
The table below summarizes the main services of each vendor.
| AWS | Microsoft Azure | Google Cloud | IBM | |
|---|---|---|---|---|
| ML platform | SageMaker AI | Azure Machine Learning | Vertex AI | watsonx.ai |
| AutoML | SageMaker Autopilot | Automated ML | Vertex AI AutoML | AutoAI |
| Generative AI | Amazon Bedrock | Microsoft Foundry | Vertex AI, Gemini | watsonx.ai, Granite |
| Speech | Transcribe, Polly | Azure AI Speech | Speech-to-Text, Text-to-Speech | Watson Speech |
| Text analysis | Comprehend | Azure AI Language | Natural Language API | Watson NLU |
| Vision and video | Rekognition | Azure AI Vision | Vision API, Video Intelligence | Limited |
| Documents | Textract | Document Intelligence | Document AI | Watson Discovery |
How to Choose a Machine Learning as a Service Platform
It is easy to get lost in the variety of services. They differ in algorithms, required skills and pricing. Moreover, vendors release new features almost every month. Therefore, start with the business problem, not the platform.
Here is the approach we use with our clients:
- Define one clear goal. Reduce the problem to a single attribute you want to predict, such as a price, a category or a customer segment.
- Try ready-made APIs first. If an API already solves your task, you don’t need to train a model at all.
- Stay close to your data. In most cases, it makes sense to use the cloud where your data and applications already live.
- Plan for MLOps from day one. Models need monitoring and retraining, so check the pipeline and monitoring tools early.
- Estimate costs realistically. Training, GPUs and API calls can grow quickly as usage increases.
Is MLaaS a Viable Business Model?
Back in 2017, DCVC co-founder Bradford Cross argued that ML-as-a-service was not a viable business model. In his view, data scientists would choose open-source tools, while managers would buy higher-level solutions.
Since then, the market has evolved. Cloud vendors combined open-source frameworks with managed infrastructure, and generative AI made ML services accessible to almost every developer. Today, machine learning as a service lets companies get started without hiring a large data science team, while experts still keep full control when they need it.
Need Help with Machine Learning?
At Yotec, we help companies choose the right ML platform, prepare data and bring models to production. Learn more about our AI/ML and LLM solutions or contact us to discuss your project.
Read also: MLOps guide: how to deploy and monitor machine learning models and our AI & Machine Learning Development Guide.
