The 7 best Amazon SageMaker alternatives for 2026

SageMaker has grown into a broader environment for analytics and AI, with Unified Studio pulling together notebooks, SQL analytics, data processing, model development, and governance, alongside Amazon Q Developer and newer serverless notebooks with a built-in AI agent.

That matters because people looking for an alternative are usually comparing different parts of the product. Sometimes the pain is the day-to-day authoring experience. Sometimes it is AWS lock-in. Sometimes it is the weight of the whole platform. And sometimes the real question is whether you want a more governed or more code-first way to build AI.

At a glance

Deepnote

Most teams looking at SageMaker alternatives aren't trying to replace their AWS infrastructure: they're trying to escape the friction that builds up around day-to-day notebook and data work: hard-to-review changes, analysis that never makes it beyond the session, workflows that can't run without someone sitting at a keyboard. That's the gap Deepnote fills. Notebooks in Deepnote are executable artifacts: schedulable, API-triggered, parameterized, and composable into larger workflows, while still sitting on top of whatever data infrastructure a team already uses. The .deepnote format produces Git-friendly diffs and clean code review; converts from .ipynb without format / infrastructure lock-in: works across AWS, GCP, Snowflake, BigQuery, and other systems, so teams can move data and workloads without being tied to a single cloud or platform For teams not ready to leave AWS entirely, Deepnote supports deploying models to SageMaker and includes template paths for creating SageMaker training jobs directly from Deepnote.

Deepnote isn't a substitute for teams whose work centers on SageMaker's training pipelines, model registry, MLOps tooling, or AWS-native orchestration. It's the right fit when the thing that's actually broken is the collaboration and execution experience wrapped around the work itself.

Pros:

• Notebooks function as composable artifacts: API-triggered, schedulable, parameterized, and embeddable into internal data platforms
• Agent operates across full project context, not individual cells; supports autonomous multi-step workflows
• Path from notebook to data apps, dashboards, and recurring jobs is built in

Cons:

• Not a replacement for SageMaker’s MLOps, model registry, training pipeline, or AWS-native governance tooling
• Teams doing primarily large-scale Spark or training infrastructure work won’t find a direct substitute here
• GPU and higher-spec machines require a paid tier

Databricks

Databricks makes sense when the alternative you want is not “lighter than SageMaker,” but “a different all-in-one platform.” Its notebooks now come with real-time coauthoring, automatic versioning, built-in visualizations, and access controls, while Genie Code has expanded into a context-aware assistant across notebooks, the SQL editor, jobs, dashboards, and more, including an agent mode for multi-step work.

Pros:

• Notebook collaboration and version history are more mature than older Databricks comparisons tend to reflect
• Genie Code operates as a full autonomous agent across the workspace, not just a coding assistant
• Photon-powered compute and Delta Lake give Databricks a meaningful edge for heavy Spark and data engineering workloads

Cons:

• Cost, cluster management complexity, and steep learning curve are consistent friction points; costs can be hard to forecast without careful cluster governance
• More platform than teams need if the primary goal is a better collaborative notebook experience
• Usage-based pricing across compute types makes budgeting non-trivial for teams new to the platform

This is the right alternative when SageMaker feels too AWS-shaped and you want your data engineering, analytics, and ML work to sit inside the same broader platform. It is less helpful if the main thing you want is simply a friendlier notebook experience.

Vertex AI

Vertex AI is the closest cloud-to-cloud comparison when you like the idea of a managed ML platform but do not want it tied to AWS. Google’s notebook story now spans both Colab Enterprise and Vertex AI Workbench, and the broader platform has moved further into agents through Vertex AI Agent Builder and Agent Engine. On top of that, Google’s Data Science Agent in Colab Enterprise now helps users perform data science tasks directly in notebooks, and Vertex AI Workbench show the Gemini CLI in preview inside Workbench instances.

Pros:

• Agent tooling through Agent Builder and Agent Engine is more explicit and production-facing
• Strong fit for teams already running data and ML workloads on Google Cloud
• Data Science Agent gives the notebook experience a more capable AI layer than older Vertex comparisons reflect

Cons:

• Colab Enterprise inherits Colab’s individual-first runtime model; multiplayer notebook editing and persistent collaboration are weaker than purpose-built team tools
• Usage-based pricing across notebook runtimes, compute, and storage can be difficult to model upfront
• Setup and permissions can get heavy quickly once notebooks need access to production data, service accounts, or multiple Google Cloud services.

Vertex AI is the strongest SageMaker alternative when the attraction is managed ML and notebooks, but the preferred cloud is Google’s.

Azure Machine Learning

Azure Machine Learning is the natural comparison point for teams already standardized on Microsoft. Teams can run notebooks in Azure Machine Learning studio, in Jupyter or JupyterLab, or from a remote VS Code connection to a compute instance. At the same time, Azure continues to position the product as an end-to-end machine learning lifecycle service rather than only a notebook product.

Pros:

• Natural fit inside a Microsoft-native stack of identity, governance, and infrastructure
• Flexible authoring options across studio notebooks, JupyterLab, and remote VS Code
• Managed compute keeps setup lighter than a fully self-managed stack

Cons:

• Compute instances have a single owner; file sharing around a compute instance is a different model from multiplayer notebook editing, and the distinction creates real collaboration friction
• Users consistently flag a disorganized interface and too many clicks to find common options
• Cost sensitivity and learning curve are recurring complaints for teams new to the platform

Azure Machine Learning is a good fit when the bigger architecture decision is already made. If your organization is Azure-heavy, it is often the most natural SageMaker alternative. If you are comparing purely on day-to-day authoring experience, other tools on this list feel more purpose-built.

Dataiku

Dataiku has been in this market since 2013. Its visual Flow-based interface and drag-and-drop ML recipes are both its signature and, for some teams, its defining limitation. It’s a platform built before the current generation of AI-native tooling.

Dataiku’s platform for AI Success framing addresses that: code notebooks for exploratory work sit alongside a structured Flow model, web-based IDEs (VS Code, JupyterLab, Streamlit) are available through Code Studios, and agent building, deployment, monitoring, and evaluation are all documented as first-class features. For large enterprises in financial services, healthcare, and manufacturing where process compliance and auditability drive decisions, Dataiku still earns its place.

Pros:

• Covers the full data lifecycle from ingestion and preparation through model deployment and monitoring
• Serves mixed teams of technical and non-technical users on a single governed platform
• Strong in regulated industries where audit trails, role-based access, and governed workflows are non-negotiable

Cons:

• Interface complexity and UI lag on large projects are recurring issues; the learning curve is steeper than the visual tooling implies
• Newer AI capabilities feel grafted onto an older architectural foundation rather than native to it
• Not the right answer for teams whose primary need is a collaborative, composable notebook runtime or Spark-scale compute

Domino Data Lab

Domino is the strongest fit for code-first teams that care most about reproducibility, traceability, and control. Its AI Workbench is built around self-serve compute, local IDE integration over SSH, experiment tracking, and a central system of record for enterprise AI operations.

Domino’s agentic AI tooling has also expanded: secure model hosting, deep observability, structured evaluations, monitoring, trace-level debugging, and reproducible delivery for agent workflows are all documented as part of the platform.

Pros:

• Reproducibility is automatic: code, data, and environments are recorded by default
• Local IDE workflows over SSH rather than forcing every team into a browser-native interface
• Strong fit for multi-cloud, regulated, or research-heavy organizations
• Agentic AI layer now covers evaluation, deployment monitoring, and trace-level debugging

Cons:

• Not a lightweight or fast-onboarding product; setup investment is real
• Expensive relative to teams that need only a fraction of its governance stack
• Smaller community and ecosystem than cloud-platform alternatives
• Less relevant for teams primarily doing SQL-heavy analytics or general collaborative notebook work

FAQs

What is the Amazon SageMaker alternative for collaborative notebook workflows?

Deepnote is the better fit when notebook work needs to become collaborative, reviewable, and repeatable without keeping the whole workflow inside AWS. It supports real-time collaboration, project-aware AI, Git-friendly notebook files, scheduling, API execution, data apps, and integrations with external systems. Teams can still deploy models or launch SageMaker training jobs from Deepnote when AWS remains part of the stack.

What is the right SageMaker alternative for avoiding platform lock-in?

Deepnote is the most direct alternative when you want to stay cloud-agnostic. It sits on top of your existing infrastructure: AWS, GCP, Snowflake, BigQuery, or others, instead of forcing everything into a single platform. That separation of concerns means data stays where it lives while notebooks become the shared runtime for collaboration, workflows, agent-driven execution, and reusable artifacts. You keep your infrastructure choices open instead of being locked into one cloud's tooling.

What is the SageMaker alternative for agent-driven workflows?

Deepnote is designed for notebooks that need to do more than hold code and outputs. Deepnote Agent works across project context, while notebooks can be scheduled, triggered through APIs, connected to Slack, turned into data apps, or composed into larger workflows. That makes it useful when agents need to help run, inspect, and iterate on work inside the same environment as the team.