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Full-stack AI cloud offering GPU compute, inference, fine-tuning and sovereign data centers for large-scale AI and HPC workloads.
Google's open-source TypeScript framework for building scalable web apps, featuring signals, reactivity and first-party tooling.
Low-code integration platform for connecting thousands of APIs into workflows and AI agents, including an MCP tool server.
AI prototyping tool that generates UI matching your design system, letting product teams test features fast.
No public pricing
No public pricing
No public pricing
No public pricing
No public pricing
- ✦Fast tensor operations
- ✦Differentiable tensors for gradient-based optimization
- ✦Network connectivity
- ✦Integration with Bun and Flashlight
- ✦Support for GPU computation with CUDA (Linux) and CPU computation (macOS)
- ✦On-demand GPU and CPU compute
- ✦Autoscaling inference endpoints
- ✦Serverless fine-tuning pipelines
- ✦Managed Kubernetes and Slurm
- ✦AI-optimized storage and RDMA networking
- ✦Sovereign, sustainable data centers
- ✦Fleet operations and observability
- ✦Signals-based fine-grained reactivity
- ✦Built-in control flow and deferrable views
- ✦Server-side rendering and hydration
- ✦First-party routing, forms and dependency injection
- ✦AI-forward tooling and MCP resources
- ✦In-browser tutorials and playground
- ✦Visual and code-based workflow builder
- ✦Prebuilt AI agent builder and deployment
- ✦Managed authentication across thousands of apps
- ✦MCP server exposing integrations as agent tools
- ✦Scheduled and event-triggered workflows
- ✦Connect SDK for embedding integrations into other products
- ✦AI UI generation from prompts
- ✦Match existing styling and design systems
- ✦Rapid, high-fidelity prototyping
- ✦Live team editing and sharing
- ✦Enterprise security and compliance
- →Creating and manipulating datasets
- →Training small machine learning models
- →Implementing advanced training and inference logic
- →Building applications that require tensor computations
- →Large-scale model training and fine-tuning
- →Deploying inference at scale
- →Running HPC and GPU workloads
- →Sovereign or compliant AI infrastructure
- →Building scalable single-page apps
- →Enterprise web application development
- →Performance-critical front ends
- →Learning modern web development
- →Building AI agents that call external APIs and tools
- →Automating cross-app workflows such as Slack, Gmail, or Sheets notifications
- →Embedding third-party integrations into a SaaS product
- →Prototyping event-driven automations without heavy infrastructure
- →Prototype new product features
- →Test designs with customers
- →Build design-system-consistent mockups