Top 6 Vertex AI Alternatives in 2026: Tested & Reviewed

Written by
Marvin Aziz
Growth Engineer

Marvin tested six Vertex AI alternatives, including AWS SageMaker, Azure Machine Learning, and Databricks, weighing pricing clarity, cross-cloud flexibility, and setup speed for teams.

Last Updated: November 11, 2025

Table of contents

• Vertex AI alternatives at a glance
• Why alternatives to Vertex AI
• 1. Lindy
• 2. AWS SageMaker
• 3. Azure Machine Learning
• 4. Databricks
• 5. Kubeflow
• 6. IBM watsonx.ai
• How I tested these alternatives
• Which Vertex AI alternative should you choose?

After comparing 10+ AI and MLOps platforms, I gathered these 6 Vertex AI alternatives that offer clearer pricing, faster deployment, and better workflow flexibility for teams in 2026.

Top 6 Vertex AI alternatives: At a glance

Here’s how the top 6 alternatives compare:

Why I looked for Vertex AI alternatives

Vertex AI excels but doesn’t fit every team’s needs. Most users start comparing alternatives when facing practical challenges. Common complaints include:

Complex pricing structure

Vertex AI’s pricing model makes it hard to predict total monthly costs due to variables like training hours, token usage, and storage.

Limited cross-cloud flexibility

Vertex AI integrates with Google Cloud, causing friction for teams working across AWS or Azure.

Slower setup for smaller teams

Setup often requires dedicated data engineers, delaying results and increasing dependence on technical staff.

Narrow focus on model operations

Vertex AI offers limited support for workflow automation. Tasks often need separate tools or custom code, especially for teams focusing on conversational AI.

1. Lindy: Best for workflow and AI agent automation

Lindy allows users to build and deploy AI agents for business operations without coding.

Why it beats Vertex AI

• No-code builder: Create custom agents using natural language instructions.
• Multi-channel support: Agents connect with tools like Slack, HubSpot, and Google Workspace.
• Model flexibility: Choose from various language models based on cost or accuracy.

Pros

• Quick setup without engineer dependency.
• Ready-to-use templates for common tasks.
• Over 4,000 integrations with popular tools.

Cons

• Not designed for large-scale training or fine-tuning.
• Complex workflows may require setup help.

Pricing

• Free plan: Up to 40 monthly tasks.
• Paid plans starting at $49.99/month.

Bottom line

Lindy suits teams wanting to automate recurring tasks without machine learning infrastructure.

2. AWS SageMaker: Best for AWS-native ML workloads

AWS SageMaker supports the complete lifecycle of model building, training, and deployment within AWS.

Why it beats Vertex AI

• End-to-end MLOps support with data labeling and model registry.
• Flexible infrastructure: Choose from a range of instance types.

Pricing

• Free tier: 4,000 API requests.
• Pay-as-you-go pricing.

Bottom line

Ideal for teams operating within AWS that seek control and scalability.

3. Azure Machine Learning: Best for Microsoft ecosystem users

Azure Machine Learning is designed for teams using Microsoft products.

Why it beats Vertex AI

• Works seamlessly with Microsoft infrastructure.
• Offers strict governance and security tools.

Pricing

• Pay-as-you-go structure for use.

Bottom line

Choose Azure if your organization primarily operates on Microsoft products.

4. Databricks: Best for data-driven enterprises with lakehouses

Databricks integrates data engineering, analytics, and machine learning.

Why it beats Vertex AI

• Offers a unified data and AI environment.
• Cross-cloud availability for flexibility.

Pricing

• Pay-as-you-go options.

Bottom line

Best for enterprises needing unified data and AI workflows.

5. Kubeflow: Best for engineered control and hybrid infrastructure

Kubeflow is an open-source ML toolkit for Kubernetes.

Why it beats Vertex AI

• Provides complete control over the ML stack.
• Cloud-agnostic functionality.

Pricing

• Free to use; operational costs depend on infrastructure.

Bottom line

Best for those needing full ownership and control over systems.

6. IBM watsonx.ai: Best for regulated industries and hybrid AI

IBM watsonx.ai focuses on governance for regulated industries.

Why it beats Vertex AI

• Strong governance and compliance features.
• Supports hybrid deployment setups.

Pricing

• Plans start from $1050/month.

Bottom line

Best for large enterprises prioritizing compliance and auditability.

How I tested these alternatives

I evaluated each platform using similar workflows, noting setup efforts, integration ease, and pricing transparency.

My testing process

I used trial accounts to run small-scale tasks like model deployment.

Which Vertex AI alternative should you choose?

Choose Lindy if you:

• Want to automate workflows like email handling and meeting scheduling.
• Prefer a no-code builder.

Choose alternatives:

• SageMaker for AWS users wanting advanced model training.
• Azure Machine Learning for Microsoft ecosystem users.
• Databricks for analytics-heavy environments.
• Kubeflow for open-source control needs.
• IBM watsonx.ai for governance-focused enterprises.

Stick with Vertex AI if you:

• Heavily use Google Cloud.

My verdict

Vertex AI is suitable for teams within Google Cloud, but projects can be limiting without sufficient flexibility. Choose Lindy for streamlined automation without requiring technical needs.