Vertex AI
Vertex AI is a unified machine learning platform from Google Cloud that helps you build, deploy, and scale high-quality AI models faster with fully managed tools and infrastructure.
Koyeb
Koyeb is a global serverless platform that provides developers with a high-performance infrastructure to deploy, scale, and manage applications and APIs across multiple regions without managing servers.
Quick Comparison
| Feature | Vertex AI | Koyeb |
|---|---|---|
| Website | cloud.google.com | koyeb.com |
| Pricing Model | Subscription | Freemium |
| Starting Price | Free | Free |
| FREE Trial | ✓ 90 days free trial | ✘ No free trial |
| Free Plan | ✘ No free plan | ✓ Has free plan |
| Product Demo | ✓ Request demo here | ✓ Request demo here |
| Deployment | ||
| Integrations | ||
| Target Users | ||
| Target Industries | ||
| Customer Count | 0 | 0 |
| Founded Year | 2021 | 2019 |
| Headquarters | Mountain View, USA | Paris, France |
Overview
Vertex AI
Vertex AI brings together Google Cloud's machine learning services into a single, cohesive environment where you can manage the entire development lifecycle. You can build models using your preferred frameworks, leverage pre-trained APIs for vision and language, or use generative AI capabilities to create custom applications. It simplifies the transition from experimental notebooks to production-ready pipelines by automating infrastructure management and scaling.
You can access powerful foundation models like Gemini to generate text, code, and images while maintaining full control over your data security. Whether you are a data scientist looking for deep customization or a developer needing quick API integration, the platform provides the specific tools required to move from idea to deployment. It integrates deeply with BigQuery and Cloud Storage, ensuring your data stays where it lives while you train and serve your models.
Koyeb
Koyeb is a developer-focused cloud platform that lets you deploy applications globally in minutes. You can run anything from simple web apps to complex microservices and APIs without the headache of managing underlying servers or complex infrastructure. The platform handles the heavy lifting of scaling, load balancing, and networking so you can focus entirely on writing your code.
You can connect your GitHub repository or deploy Docker containers directly to their high-performance edge network. It is designed for developers, startups, and growing tech teams who need the power of a global cloud with the simplicity of a modern serverless experience. Whether you are launching a side project or scaling a production-ready API, you get a unified interface to manage your entire stack.
Overview
Vertex AI Features
- Model Garden Discover and deploy a wide variety of first-party, open-source, and third-party models through a single, searchable interface.
- Generative AI Studio Test and customize foundation models like Gemini using your own prompts and data in a low-code environment.
- AutoML Capabilities Train high-quality models for images, tabular data, or text automatically without writing extensive code or managing infrastructure.
- Vertex AI Pipelines Automate your machine learning workflows to ensure consistent model training, evaluation, and deployment across your entire team.
- Feature Store Share and reuse machine learning features across different projects to reduce redundant data processing and improve model accuracy.
- Explainable AI Understand why your models make specific predictions with built-in tools that provide detailed insights into feature importance.
Koyeb Features
- Global Edge Network. Deploy your applications to multiple regions worldwide to ensure low latency and high performance for your global users.
- Native Docker Support. Run any Docker container or language of your choice with full support for popular frameworks and runtimes.
- Git-driven Deployment. Connect your GitHub or GitLab accounts to trigger automatic builds and deployments every time you push new code.
- Auto-scaling Capabilities. Scale your services up or down automatically based on real-time traffic demands to optimize performance and costs.
- Built-in Load Balancing. Distribute incoming traffic across your healthy instances automatically without configuring complex third-party load balancers.
- Global Private Networking. Connect your microservices securely over a private, encrypted network that spans across all available cloud regions.
- Managed Databases. Provision and scale managed PostgreSQL databases directly within the platform to simplify your application's data layer.
- Real-time Observability. Monitor your application health with integrated logs and metrics to troubleshoot issues and optimize performance instantly.
Pricing Comparison
Vertex AI Pricing
- $300 in free credits
- Access to all Google Cloud products
- No up-front commitment
- Valid for 90 days
- Standard support included
- Everything in Free Trial, plus:
- Custom machine types
- GPU and TPU acceleration
- Autoscaling infrastructure
- Enterprise-grade SLAs
- Volume-based discounts
Koyeb Pricing
- 512MB RAM
- 0.1 vCPU
- 2GB SSD storage
- Global edge network
- Public and private services
- Community support
- Everything in Nano, plus:
- Pay-as-you-go resources
- Increased RAM limits
- Custom domains with SSL
- Standard support
- No sleep mode for services
Pros & Cons
Vertex AI
Pros
- Deep integration with the broader Google Cloud ecosystem
- Access to industry-leading foundation models like Gemini
- Scales effortlessly from small experiments to enterprise production
- Unified interface reduces the need for multiple tools
Cons
- Complex pricing structure can be difficult to predict
- Steep learning curve for those new to Google Cloud
- Documentation can be overwhelming due to frequent updates
Koyeb
Pros
- Extremely fast deployment times from GitHub
- Simple and intuitive user interface
- Generous free tier for small projects
- High-performance infrastructure with low latency
- Transparent and predictable resource-based pricing
Cons
- Fewer regions compared to major hyperscalers
- Limited managed service variety beyond Postgres
- Learning curve for complex networking setups