# 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](#vertex-ai-alternatives-at-a-glance) - [Why alternatives to Vertex AI](#why-alternatives-to-vertex-ai) - [1. Lindy](#1-lindy-best-for-workflow-and-ai-agent-automation) - [2. AWS SageMaker](#2-aws-sagemaker-best-for-aws-native-ml-workloads) - [3. Azure Machine Learning](#3-azure-machine-learning-best-for-microsoft-ecosystem-users) - [4. Databricks](#4-databricks-best-for-data-driven-enterprises-with-lakehouses) - [5. Kubeflow](#5-kubeflow-best-for-engineered-control-and-hybrid-infrastructure) - [6. IBM watsonx.ai](#6-ibm-watsonxai-best-for-regulated-industries-and-hybrid-ai) - [How I tested these alternatives](#how-i-tested-these-alternatives) - [Which Vertex AI alternative should you choose?](#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: | Alternative | Best for | Starting price (billed monthly) | Key advantage vs Vertex AI | |-----------------------------------------|------------------------------------------------------------|-------------------------------------|-----------------------------------------------------------------------------------------------------------------------| | [Lindy](/content/site-root.html) | AI agents that automate business tasks | Starts at $49.99/month | No-code setup, pre-built templates for workflows, 4,000+ app integrations, and customizable AI agents | | [AWS SageMaker](https://aws.amazon.com/sagemaker/) | Full ML lifecycle on AWS | Pay-as-you-go | Integration with AWS ecosystem and the most mature MLOps toolset | | [Azure Machine Learning](https://azure.microsoft.com/en-us/products/machine-learning) | Microsoft-stack users needing governance | Pay-as-you-go | Integration with Microsoft 365, Power Platform, and Azure OpenAI models | | [Databricks](https://www.databricks.com/) | Lakehouse data + AI operations | Usage-based | Unified data and AI environment with real-time serving on Delta Lake | | [Kubeflow](https://www.kubeflow.org/) | Hybrid or on-prem control | Free, open-source | Full-stack ownership and cloud-agnostic flexibility for engineering teams | | [IBM watsonx.ai](https://www.ibm.com/products/watsonx-ai) | Regulated enterprises needing hybrid AI | From $1050/month | Advanced governance and model risk management for regulated industries | ## 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](/content/site-root.html) 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**](/content/integrations/index.html) 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**](/content/pricing/index.html). ### 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](https://aws.amazon.com/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](https://azure.microsoft.com/en-us/products/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**](https://azure.microsoft.com/en-us/pricing/details/machine-learning/) 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](https://www.databricks.com/) 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**](https://www.databricks.com/product/databricks-pricing) options. ### Bottom line Best for enterprises needing unified data and AI workflows. ## 5. Kubeflow: Best for engineered control and hybrid infrastructure [Kubeflow](https://www.kubeflow.org/) 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](https://www.ibm.com/products/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**](https://www.ibm.com/products/watsonx-ai/pricing). ### 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.