| Overview |
Scalable AI compute platform built on Ray for deploying and fine-tuning large language models in production. |
Google's suite of cloud computing services for hosting, computing, storage, machine learning, and data analytics. |
| Pricing |
Pay-per-use ($$-$$$$) |
Pay_per_use (Pay-as-you-go) |
| Key Features |
- Ray-based
- Auto-scaling
- Fine-tuning
- Managed endpoints
- Multi-model
- GPU clusters
|
- Compute Engine
- Cloud Storage
- BigQuery
- Kubernetes Engine
- Cloud Functions
- Vertex AI
- Cloud Run
- Firestore
|
| Pros |
- Built on Ray
- Excellent scaling
- Production-grade
- Fine-tuning support
|
- Strong data and AI capabilities
- Good Kubernetes support
- Competitive pricing
- Sustained use discounts
|
| Cons |
- Complex setup
- Higher learning curve
- Enterprise-focused pricing
|
- Smaller market share
- Less enterprise features
- Support can be expensive
- Service stability concerns
|
