Cloud services: Google Vertex AI vs. Salesforce Einstein 1 Platform
Quick Verdict
Both Google Vertex AI and Salesforce Einstein 1 Platform are robust AI platforms with different strengths, catering to different user needs and preferences.
- Google Vertex AI offers extensive customization options and a wide range of pre-trained models.
- Salesforce Einstein 1 Platform provides low-code tools and seamless integration with Salesforce products.
- Both platforms offer scalability, data security, and AutoML features.
Key features – Side-by-Side
| Attribute | Google Vertex AI | Salesforce Einstein 1 Platform |
|---|---|---|
| AI Model Customization Options | Offers Generative AI tuning, custom training, and Model Garden for customization of Vertex AI and open-source models. | Einstein 1 Studio provides low-code tools, Copilot Builder, Prompt Builder, Model Builder, and Skills Builder. |
| Pre-trained AI Models Availability | Provides access to a wide variety of pre-trained models through Model Garden, including first-party (Gemini, Imagen 3), third-party (Anthropic's Claude), and open models (Gemma, Llama 3). | Einstein 1 Studio allows users to choose from a range of pre-built Large Language Models (LLMs) provided by Salesforce partners. |
| Scalability for Large Datasets | Leverages Google Cloud's infrastructure to provide a scalable and efficient platform for ML model development and deployment. | Supports thousands of metadata-enabled objects per customer and can handle up to 20,000 events per second. Designed to handle massive data volumes from various sources. |
| Integration with Existing Systems | Seamlessly integrates with other Google Cloud services like BigQuery, Cloud Storage, and Dataflow for data preparation, analytics, and management. | Integrates with Salesforce products like Sales, Service, Marketing, and Commerce Clouds. Can also integrate with external systems and applications through APIs and connectors. MuleSoft facilitates interaction of flows with any enterprise system. |
| Data Security and Compliance | Implements Google Cloud security controls to help secure models and training data. | Einstein 1 Studio includes the Einstein Trust Layer, which ensures data privacy and security. Offers customer-configured data masking and stores audit trail and feedback data in Data Cloud. |
| Ease of Use and Deployment | Provides a unified interface for managing datasets, training models, and deploying them to production. AutoML function automatically builds and fine-tunes models. | Offers low-code/no-code development options, making AI accessible to users regardless of technical expertise. Einstein 1 Studio provides a user-friendly interface and guided workflows. |
| Cost Structure and Pricing | Custom model training costs range from $0.094/hour to $11+/hour. AutoML training starts at $3.465 per node hour, and deployment at $1.375 per node hour. Gemini 2.5 Pro is priced at $1.25 per million input tokens (up to 200K) and $10-$15 per million output tokens. | Einstein 1 Editions start at $500/user per month for Sales and Service. Einstein AI pricing starts at $75 per user per month. |
| Real-time Prediction Capabilities | Allows you to create custom ML models and deploy them to get real-time online predictions through an endpoint. | Integrates Data Cloud, a real-time hyperscale data engine processing trillions of transactions per month. Offers real-time predictions and insights. |
| Automated Machine Learning (AutoML) Features | AutoML function automatically builds and fine-tunes models for various tasks and data types. | Salesforce AutoML is integrated into Salesforce Einstein. It automates the process of training and deploying machine learning models. |
| Model Monitoring and Management | Provides end-to-end MLOps tools to automate and scale projects throughout the ML lifecycle. Vertex AI Pipelines enables you to track the lineage of a model's output data back to its training data. | Model Builder allows users to manage AI models from a unified control plane. |
| Customer Support and Documentation | Google Cloud provides extensive documentation and support resources for Vertex AI. | Salesforce provides communities for users to connect with peers, learn skills, and find experts. Trailhead offers resources for learning about Einstein 1 Studio. |
| Industry-Specific Solutions | Provides AI opportunities across various industries such as retail, healthcare, financial services, and more. | The manufacturing industry can leverage the Salesforce Einstein 1 Platform to improve production planning, predict equipment failures, and enhance quality control. It can be applied in various industries like retail, finance, healthcare, manufacturing, and technology. |
Overall Comparison
Customization: Vertex AI wins; Integration: Einstein 1 wins; Scalability & Security: Both strong
Pros and Cons
Google Vertex AI
Pros:
- Offers multiple options for customizing models, including Generative AI to tune Google's LLMs, custom training for complete control over the training process, and Model Garden for customization of Vertex AI and open-source models.
- Provides access to a wide variety of pre-trained models through Model Garden, including first-party (Gemini, Imagen 3), third-party (Anthropic's Claude), and open models (Gemma, Llama 3).
- Leverages Google Cloud's infrastructure to provide a scalable and efficient platform for ML model development and deployment.
- Seamlessly integrates with other Google Cloud services like BigQuery, Cloud Storage, and Dataflow for data preparation, analytics, and management.
- Implements Google Cloud security controls to help secure models and training data.
- Provides a unified interface for managing datasets, training models, and deploying them to production.
- Allows you to create custom ML models and deploy them to get real-time online predictions through an endpoint.
- AutoML function automatically builds and fine-tunes models for various tasks and data types.
- Provides end-to-end MLOps tools to automate and scale projects throughout the ML lifecycle.
- Google Cloud provides extensive documentation and support resources for Vertex AI.
- Provides AI opportunities across various industries such as retail, healthcare, financial services, and more.
Cons:
- No major disadvantages reported.
Salesforce Einstein 1 Platform
Pros:
- AI Model Customization Options
- Pre-trained AI Models Availability
- Scalability for Large Datasets
- Integration with Existing Systems
- Data Security and Compliance
- Ease of Use and Deployment
- Real-time Prediction Capabilities
- Automated Machine Learning (AutoML) Features
- Model Monitoring and Management
- Customer Support and Documentation
- Industry-Specific Solutions
Cons:
- No major disadvantages reported.
User Experiences and Feedback
Google Vertex AI
What Users Love
- Supports extensive customization through custom training, AutoML, and Model Garden, allowing businesses to tailor models to their specific needs.
- A wide range of pre-trained models are available, including Gemini, Imagen, and open-source models, suitable for various common use cases.
- The platform is designed to handle large and growing datasets efficiently, leveraging Google Cloud's infrastructure for scalable data processing and model training.
- Integrates seamlessly with other Google Cloud services like BigQuery, Dataflow, and Cloud Storage, as well as with external systems through AI Platform Extensions.
- Robust security measures are in place, including access controls, data encryption, and compliance certifications, with shared responsibility between Google and the customer.
- The platform offers AutoML for non-technical users and custom training for advanced users, with a unified interface and streamlined deployment process.
- Provides effective real-time prediction capabilities, with low-latency inferences and support for online prediction endpoints.
- AutoML automates the machine learning process, offering features like automated data preparation, model training, and hyperparameter tuning.
- Offers tools for monitoring model performance over time, including Vertex AI Model Monitoring, which tracks training-serving skew and inference drift.
Common Complaints
- No major complaints reported.
Value Perception
- No value feedback reported.
Salesforce Einstein 1 Platform
What Users Love
- No highlights reported.
Common Complaints
- No major complaints reported.
Value Perception
- No value feedback reported.