Cloud services: Cloudflare Workers AI vs. Google Vertex AI
Quick Verdict
Both are strong platforms for AI inference, with Cloudflare Workers AI emphasizing low latency and Google Vertex AI offering broader model support and Google Cloud integration.
- Both platforms offer serverless inference and automatic scaling.
- Google Vertex AI provides broader model support, including Google's Gemini family and custom models.
- Cloudflare Workers AI focuses on low-latency inference with its global network.
- Google Vertex AI integrates deeply with the Google Cloud ecosystem, while Cloudflare Workers AI integrates with Cloudflare's services.
Key features – Side-by-Side
| Attribute | Cloudflare Workers AI | Google Vertex AI |
|---|---|---|
| Serverless Inference | Yes | Yes |
| Model Support | Open-source models (Llama, Stable Diffusion, Mistral) | Google's Gemini family, third-party models (Anthropic's Claude), open-source models (Llama 3.2), custom-trained models |
| Hardware Acceleration | GPUs | GPUs and TPUs |
| Integration | Cloudflare services (Vectorize, R2, AI Gateway) | Google Cloud services (BigQuery, Google Cloud Storage, Dataflow), Amazon S3 |
| Pricing | $0.011 per 1,000 Neurons, 10,000 free Neurons per day | Pay-as-you-go, billed for compute, storage, API calls |
| Deployment Options | Cloudflare's network | Public endpoints, Private Service Connect endpoints, GKE |
| Tools and SDKs | AI Gateway, Vectorize, AI Agents SDK, Workers, Pages, Wrangler CLI, REST API | Vertex AI SDK (Python), Colab Enterprise, Vertex AI Workbench, Terraform |
Overall Comparison
Inference: Serverless on both; Models: Vertex AI broader; Latency: Workers AI lower; Integration: Ecosystem-specific
Pros and Cons
Cloudflare Workers AI
Pros:
- Serverless inference capabilities
- Supports open-source AI models like Llama, Stable Diffusion, and Mistral
- Global network reach for low-latency AI inference
- Hardware acceleration with GPUs
- Integration with Cloudflare services like Vectorize and R2
- Pay-as-you-go pricing model
- Automatic scaling
- Security features like Firewall for AI
- One-click deployment from Hugging Face
- Support for custom models
- Tools and SDKs for AI development
Cons:
- Limitations on model size and complexity (though improving)
- May require custom requirements form for private models or higher limits
Google Vertex AI
Pros:
- Serverless inference capabilities
- Supports various AI models including Google's Gemini family, third-party models like Anthropic's Claude, and open-source models like Llama 3.2
- Global network reach for AI inference
- Hardware acceleration for AI workloads using GPUs and TPUs
- Integration with existing Google Cloud services like BigQuery, Google Cloud Storage, and Dataflow, and connects to Amazon S3
- Automatic scaling of resources based on demand
- Security features including IAM, Private Service Access, encryption with CMEKs, and DLP
- Multiple model deployment options including public endpoints, Private Service Connect endpoints, and GKE
- Supports both online and batch predictions
- Allows training custom models using any ML framework
- Provides tools such as the Vertex AI SDK (Python), Colab Enterprise, and Vertex AI Workbench
Cons:
- Pricing comparison with Cloudflare Workers AI not available
- Limitations on model size and complexity not specified
- Inference latency for different AI models not specified
User Experiences and Feedback
Cloudflare Workers AI
What Users Love
- Affordable solution for running inference
- Minimizes latency by running inference closer to users
- Easy to deploy AI models
- Integrates well with existing Cloudflare services
Common Complaints
- No major complaints reported.
Value Perception
- No value feedback reported.
Google Vertex AI
What Users Love
- Simplifies the ML lifecycle with tools for data preparation, model training, deployment, and monitoring.
- Offers AutoML and custom training options.
Common Complaints
- No major complaints reported.
Value Perception
- No value feedback reported.