Amazon SageMaker Alternatives - Top 5 softwares at a Glance

Even though the Big 5 cloud providers, according to Statista the Cloud hosting market in 2023, are big names for building a robust DevOps Infrastructure for AI or Machine Learning, also MLOps called, not everything.

Especially when building services for your own AI models, other factors often play a role, and not just the pure company size of the suppliers. You may have already noticed this if you have been looking into Amazon SageMaker and possible alternatives.

What is Amazon SageMaker suitable for?

As a cloud-based machine learning platform, Amazon SageMaker allows developers and data scientists to create, train and implement AI models. The interface is designed to visualize and thus accelerate basic processes - from data preparation to the automated operation of created or prefabricated algorithms. The web service is fully embedded in the Amazon cosmos and therefore interacts preferentially with other AWS tools such as Amazon Kinesis and the in-house databases.

Cycle of Active Learning
Typical workflow for creating a model to use human feedback in training. You can find more information in the article Human-in-the-loop (HITL).

Users who wish to use SageMaker have specific requirements and expectations of the platform, especially when compared to alternative solutions such as building their own infrastructure. Based on the list provided, here are the requirements that such users might have:

  1. Auto Scaling: Users need automatic scaling to add instances according to the current load. They expect this to happen in an efficient and cost-effective manner, without the hassle and cost of building and maintaining such an infrastructure.
  2. Multi Model Server: There is a need to consolidate multiple endpoints to take full advantage of the existing infrastructure. This is not easy to implement on your own servers.
  3. Versioning and data management: Clear and efficient model versioning and management of the associated data source code are critical. On own servers this could be more complicated and less intuitive.
  4. Model Training Cycle: An automatic training cycle based on the data received is desirable. This is easier to implement on SageMaker than on own infrastructure.
  5. Incremental learning or transfer learning: Advanced ML techniques such as incremental learning or transfer learning require an efficient and cost-effective solution that may be more difficult to implement and maintain on in-house infrastructure.
  6. Elastic inference: Fast model performance especially for deep learning tasks while reducing latency is required. Building and maintaining own infrastructure could be more expensive in terms of development and operational costs.
  7. DevOps Integration: Simple and seamless integration into existing DevOps workflows is necessary. While SageMaker offers an integrated CLI functionality, this function would have to be developed independently for the own infrastructure.

Finally, users should consider the cost of SageMaker and comparable add-on services, which usually cost 20 % to 40 % more than a simple infrastructure with the same compute capacity, see Reddit or StackOverflow.

It becomes clear how Amazon relies on its own tools and functions in almost every single step of a machine learning project. The use of supplementary services is no exception in many cases. Last but not least, this results in some disadvantages for companies.

Amazon SageMaker is suitable for experienced analysts and developers who want to run large-scale AI projects almost exclusively in the AWS cosmos.

Amazon SageMaker Disadvantages

  • Complexity: The platform is aimed exclusively at professional developers and data scientists. The user interface is correspondingly confusing and requires the user to enter his own development code for many processes. Even ready-made machine learning models usually require medium to large amounts of data. Their preparation proves to be complicated, even with the integrated tools. SageMaker is therefore not suitable for entry and small processes.
SageMaker User Interface with Data
User interface of SageMaker. Source: Amazon Web Services
  •  On the other hand, the interface for developers can also be technically constricting if the requirements are highly individual. This applies, for example, to the integration of existing machine learning models or extensive data migration from legacy systems or third-party applications. On-premises operation via own servers is also not possible.
  • Instead, the user is strongly dependent on services within the Amazon Cloud - through which profit is ultimately generated. This dependency is already consolidated in a two-month free trial phase, so that even a subsequent decision against the software can become costly and time-consuming due to infrastructural adjustments.
  • Cost: Amazon advertises a usage-based pricing model that does not include any basic fees. The costs are based on the number of machine learning models, their use, the (working) memory used, the training duration, and the amount of data - in other words, virtually after every mouse click. Considering that the platform is only suitable for a large scope of use, high costs are inevitable. The computationally intensive GPU instances also contribute to this. The complexity of the pricing model can be seen here .

SageMaker and the future of automation

Most employees in companies are not developers. In fact, according to Bitkom, Germany still lacks 137.000 IT specialists. However, automation and gaining insights through machine learning have long since become important success factors. Language models such as ChatGPT have shown that use by non-professional users is also possible. Access to artificial intelligence is currently facing democratization, which will leave companies that do not participate at a disadvantage. It is therefore important to know the appropriate alternatives if the know-how or resources are lacking for solutions such as Amazon SageMaker.

Initially, companies benefit most from automating particularly frequent and small processes that tend to add up to large time-consuming tasks. This applies, for example, to the processing of e-mail attachments, invoices, delivery bills or payment notifications. Corresponding software based on machine learning must be uncomplicated in its integration and handling, yet flexible in its applicability. The desire for a different range of functions or on-premises use can also motivate the search for Amazon SageMaker alternatives.

Automation starts with small, repetitive standard processes. An important example that crops up in every company is document management.

Alternatives and additions from Amazon

To compensate for SageMaker's lack of competencies and ensure the widest possible use of the AWS Cloud, Amazon offers a myriad of other services. The following are particularly relevant:


The need for automated analysis of documents is not new territory for Amazon either. For this purpose, the OCR (optical character recognition) based software Textract offered. The tool focuses on the extraction of text and data and is therefore only suitable for document analysis. In addition, it offers only a small range of functions, which are largely limited to data extraction from various forms and a manual control workflow.

Textract thus represents a small addition to data extraction from documents in the AWS Cloud in relation to SageMaker. For users who only deal with OCR-based analysis of simple documents, the software is a more cost-effective alternative. A detailed analysis can be found here.

Amazon Forecast

Amazon Forecast is a fully managed forecasting service based on Machine Learning and offered by Amazon Web Services (AWS). This service enables users to make accurate forecasts over time series data without requiring ML expertise. It uses the same technologies that uses for its own forecasting needs. However, with Amazon Forecast, users can only upload time series data, evaluate the forecast quality of various algorithms, and use the best models to predict future values.

In connection with Amazon SageMaker Amazon Forecast can be considered as a complementary solution. While SageMaker provides a comprehensive platform for developing, training and deploying machine learning models, Forecast is specifically focused on forecasting applications and provides a simple workflow for such scenarios.

The two services complement each other well, especially when companies need both customized ML models and specialized predictions for time series data.

Replicate Amazon SageMaker for free

Open source tools can be used to implement a SageMaker-like environment on your own infrastructure. Kubernetes serves as the basis for container orchestration, while Kubeflow optimizes machine learning lifecycle management. JupyterHub enables the use of interactive notebooks and MinIO or Ceph can be used as scalable data storage solutions.

The flexibility and control that open source tools offer are their biggest advantages over integrated solutions like SageMaker. Despite the higher initial setup effort, tools like TensorFlow, PyTorch, and Scikit-Learn provide deep insights and customization capabilities for ML models. In addition, monitoring tools such as Prometheus and Grafana support system monitoring and provide transparency throughout the ML process. Altogether, these tools allow the creation of an individual and fully customized ML platform.

We would be happy to support you in setting up such an infrastructure. You can find further documentation from Berkeley or in the following technical Medium post.

-> Contact us now for a free initial consultation.

Commercial providers - Who makes the race of the top 5?

Amazon is by no means the only provider to help companies advance through the potential of machine learning. When making a selection, it is important to precisely match the requirements with the respective scope of functions.

For example, the following 5 softwares are suitable as SageMaker alternatives:

  1. Konfuzio

    AI-based all-in-one tool for automated document management. Ideal for first-time users.

  2. Binder

    Lean solution for hosting Jupyter interactive notebooks in the cloud.

  3. Dataiku

    Complete AI solution for large-scale analytics and data-driven insights. Open Source.

  4. IBM Watson Studio

    SageMaker-like cloud platform, but one that makes it easier for less experienced Data Scientists without programming skills to get started.

  5. Azure Machine Learning

    Microsoft's easier-to-use counterpart for machine learning in the Azure Cloud.

sagemaker alternative konfuzio


Konfuzio is a powerful AI platform. It gives access to various open source and close source models like OCR, Computer Vision and natural language processing (NLP). This enables the operation of large different AI models and interaction through uniform and well-documented technical interfaces. This makes Konfuzio a potent alternative to SageMaker in appropriate use cases, but it can also serve as a complement.

The following advantages result from the range of functions compared to SageMaker:

  • Use in the Cloud and on-premises possible
  • Suitable for any skill level: intuitive interface and Source code modules and API & SDK development
  • Extensive integration options: REST API, Google Docs, Microsoft Office, Airtable as well as various ERP, CRM or RPA systems
  • Auto Scaling: Konfzio enables automatic scaling to provide additional instances to users during increased load without the need and cost of infrastructure maintenance.
  • Multi Model Server: Konfuzio allows users to efficiently combine multiple endpoints to make the most of their infrastructure, which could be a challenge on their own servers.
  • Versioning and data management: Konfzio provides clear model versioning and efficient data management, allowing users to keep track of different models and their data sources.
  • Model Training Cycle: Konfzio facilitates the automatic training cycle based on the received data, simplifying the implementation.
  • Incremental learning or transfer learning: For advanced techniques, Konfuzio offers solutions that would otherwise be difficult to implement on your own infrastructure.
  • Elastic inference: Konfzio ensures that models work quickly, especially in Deep Learning tasks, and latency is minimized.
  • DevOps Integration: Finally, Konfuzio enables smooth integration with DevOps workflows or MLOps workflowsso users don't have to develop their own integration tools from scratch.

Konfuzio can be used to adjust, train and monitor AI models. In terms of its scope of application, Konfuzio outperforms corresponding individual modules from well-known software providers and even leaves Amazon Textract behind in terms of flexibility and performance. we reported.


...specializes in hosting Jupyter interactive notebooks in the cloud.

  • Features: Share Jupyter notebooks directly from GitHub repositories, no setup required, Docker support for environment replication, open source platform for interactive computing environments.
  • Pro: Easy to get started, ideal for Data Scientists and researchers, allows sharing and collaboration on notebooks without any installation, completely open source, offers flexibility through Docker support.
  • Contra: Not specifically focused on machine learning workflow optimization, may lag behind SageMaker in scalability and advanced ML deployment features.

Dataiku suitable for large-scale data analysis.

  • Functions: Visual workflow for creating data pipelines, connectors for common data sources, data transformers, visualization tools, AutoML.
  • Pros: Central platform for large amounts of data, many analysis and visualization options, open source.
  • Contra: Complex user interface, no optimal support.

IBM Watson Studio particularly similar to Amazon SageMaker in its range of services.

  • Functions: Proprietary Data AI platform, AutoAI, model drift, explainable AI, model risk management.
  • Pros: Various implementation options, cloud/on-premises hybrid possible, extensive collaboration options for teams.
  • Contra: Potentially high cost, requires a lot of technical expertise from Data Scientists.

Azure Machine Learning the GUI-based development environment from Microsoft.

  • Functions: Multiple integrated frameworks such as PyTorch or Tensorflow, drag-and-drop designer for data preparation and model training, AutoML, managed endpoints.
  • Pros: Wide range of integrated frameworks and services, clear interface, no code required.
  • Contra: Limited number of models per workflow, vendor lock-in, big data.


Due to high complexity, clunky integration and heavy dependency on Amazon, SageMaker is not suitable for many companies. Costs can also easily skyrocket in AI experiments without a direct return on investment.

Common alternatives from other providers often allow easier access to machine learning through a more intuitive design of the user interface. This means that some solutions can even be used by employees and data scientists with significantly less technical expertise. For almost every company, however, the flexible document AI Konfuzio is suitable, which enables users of any level of expertise to enter the world of machine learning.

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