Cloud services: Google Vertex AI vs. Salesforce Einstein AI
Quick Verdict
Both platforms provide robust AI capabilities, with Google Vertex AI focusing on speed and flexibility, and Salesforce Einstein AI emphasizing integration with its CRM ecosystem.
- Google Vertex AI offers faster model training with distributed computing and supports a wider range of programming languages.
- Salesforce Einstein AI seamlessly integrates with Salesforce CRM and provides automated machine learning capabilities.
- Both platforms offer pre-trained models, custom model deployment options, and model explainability tools.
Key features – Side-by-Side
| Attribute | Google Vertex AI | Salesforce Einstein AI |
|---|---|---|
| Model Training Speed | Accelerates model training using distributed computing with GPUs and TPUs, optimizing bandwidth and latency during multi-node distributed training. TiDE model offers faster training times for forecasting. | Automated with Salesforce AutoML. |
| Pre-trained Models Availability | Model Garden provides pre-built models; Generative AI models like PaLM 2 are available. | Offers features like Einstein Lead Scoring and Einstein Opportunity Insights; Predictive lead scoring capabilities. |
| Custom Model Deployment Options | Deploy custom models using pre-built or custom containers; Supports frameworks such as TensorFlow, PyTorch, XGBoost, and scikit-learn. | Einstein Studio allows businesses to use their data to build and deploy AI models; Supports a "bring your own model" (BYOM) approach. |
| Integration with Existing Cloud Infrastructure | Integrates with Google Cloud services like BigQuery, Dataflow, and Cloud Storage. | Seamlessly integrates with Salesforce CRM; Integrates with third-party applications via APIs and connectors. |
| Scalability for Large Datasets | Integrates with BigQuery and Dataflow; Supports distributed training and hyperparameter tuning; Vertex AI vector search can scale to billions of embeddings. | Scales according to business size and data volume; Einstein 1 Platform manages massive data and AI workloads. |
| Support for Multiple Programming Languages | Supports languages like Python, C++, C#, Go, Java, JavaScript, Ruby, Scala, Swift, and TypeScript. | Information not available. |
| Data Security and Compliance Features | Implements Google Cloud security controls, including data encryption in transit; Meets regulatory requirements like HIPAA, PCI-DSS, and SOC 2. | Einstein Trust Layer offers features such as data masking, zero data retention, and toxicity detection; Built to meet regulatory requirements like GDPR and HIPAA. |
| Cost-Effectiveness and Pricing Models | Pricing is based on resource consumption; Committed use discounts and sustained use discounts can help reduce costs. | Information not available. |
| Real-time Prediction Capabilities | Allows creation of custom ML models and deployment for real-time online predictions; Streaming Ingestion for Feature Store enables low-latency retrieval of feature values. | Offers real-time prediction capabilities, providing actionable insights within Salesforce dashboards; Einstein Forecasting predicts more accurate sales opportunities. |
| Model Explainability Tools | Explainable AI offers feature-based and example-based explanations; Provides feature attributions and integrates with Vertex AI Vizier for hyperparameter tuning. | Explainable AI (XAI) provides insights into how the model makes predictions; Einstein Discovery uncovers hidden patterns and provides visual explanations of trends. |
| Automated Machine Learning (AutoML) Features | Automates aspects of the ML workflow, including data preparation, feature engineering, model training, and hyperparameter tuning; Supports various data types. | Salesforce AutoML, integrated into Einstein Discovery, automates the process of training and deploying machine learning models. |
| Customer Support and Documentation Quality | Google Cloud provides documentation and support resources. | Details not available. |
Overall Comparison
Training Speed: Vertex AI wins; Integration: Einstein AI wins; Language Support: Vertex AI wins; AutoML: Both offer
Pros and Cons
Google Vertex AI
Pros:
- Model Training Speed: Utilizes distributed computing with GPUs and TPUs to accelerate model training; Reduction Server optimizes bandwidth and latency during multi-node distributed training; TiDE model offers faster training times for forecasting.
- Pre-trained Models Availability: Model Garden provides pre-built models; Generative AI models like PaLM 2 are available with improved multilingual, reasoning, and coding capabilities.
- Custom Model Deployment Options: Deploy custom models using pre-built or custom containers; Supports frameworks such as TensorFlow, PyTorch, XGBoost, and scikit-learn; Deploy to public endpoints using Google Cloud console, gcloud CLI, or Vertex AI API.
- Integration with Existing Cloud Infrastructure: Integrates with Google Cloud services like BigQuery, Dataflow, and Cloud Storage for data processing and storage.
- Scalability for Large Datasets: Integrates with BigQuery and Dataflow for data ingestion and transformation; Supports distributed training and hyperparameter tuning; Vertex AI vector search can scale to billions of embeddings and hundreds of thousands of queries per second while maintaining low latency.
- Support for Multiple Programming Languages: Supports languages like Python, C++, C#, Go, Java, JavaScript, Ruby, Scala, Swift, and TypeScript.
- Data Security and Compliance Features: Implements Google Cloud security controls, including data encryption in transit; Meets regulatory requirements like HIPAA, PCI-DSS, and SOC 2; Data logging can be disabled.
- Cost-Effectiveness and Pricing Models: Pricing is based on resource consumption; Committed use discounts and sustained use discounts can help reduce costs; Vertex AI vector search can be more cost-effective than competing solutions.
- Real-time Prediction Capabilities: Allows creation of custom ML models and deployment for real-time online predictions; Streaming Ingestion for Feature Store enables low-latency retrieval of feature values.
- Model Explainability Tools: Explainable AI offers feature-based and example-based explanations; Provides feature attributions and integrates with Vertex AI Vizier for hyperparameter tuning.
- Automated Machine Learning (AutoML) Features: Automates aspects of the ML workflow, including data preparation, feature engineering, model training, and hyperparameter tuning; Supports various data types.
- Customer Support and Documentation Quality: Google Cloud provides documentation and support resources.
Cons:
- No major disadvantages reported.
Salesforce Einstein AI
Pros:
- Automated machine learning model training and deployment.
- Offers pre-trained models for lead scoring and opportunity insights.
- Supports custom model deployment with a user-friendly interface.
- Seamlessly integrates with Salesforce CRM.
- Scales to handle large datasets.
- Provides data security and compliance features.
- Offers real-time prediction capabilities.
- Includes model explainability tools.
- Simplifies model development with AutoML features.
Cons:
- Model training speed comparison with other platforms is not available.
- Specific programming languages supported are not specified.
- Pricing model comparison with other platforms is not available.
- Details on customer support responsiveness and documentation comprehensiveness are not available.
User Experiences and Feedback
Google Vertex AI
What Users Love
- No highlights reported.
Common Complaints
- No major complaints reported.
Value Perception
- No value feedback reported.
Salesforce Einstein AI
What Users Love
- No highlights reported.
Common Complaints
- No major complaints reported.
Value Perception
- No value feedback reported.