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features:
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title: The Complete AI Infrastructure Lifecycle
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description: From research experiments to superintelligence-scale production. One platform, one codebase, any cloud.
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description: |
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From research experiments to superintelligence-scale production.
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One platform, one codebase, any cloud.
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- header: Pre-Training
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Supabase needed infrastructure that could scale without operational overhead. Terraform's HCL meant constant context switching between TypeScript (application services) and a proprietary configuration language (infrastructure).
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After migrating to Pulumi:
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- Regional expansion: 1 week to infrastructure readiness
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- Scale: 80,000 resources across 16 AWS regions
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- Team velocity: 1-2 people to 40+ active engineers
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- Multi-cloud: AWS + Cloudflare + GCP in single deployments
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- **Regional expansion**: 1 week to infrastructure readiness
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- **Scale**: 80,000 resources across 16 AWS regions
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- **Team velocity**: 1-2 people to 40+ active engineers
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- **Multi-cloud**: AWS + Cloudflare + GCP in single deployments
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Supabase powers AI application builders like Lovable, Bolt, and Vercel v0: 43,000+ databases launched daily, 100K+ API calls per second. The backend infrastructure runs entirely on Pulumi.
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quote: "\"With Pulumi, everything is TypeScript. Our infrastructure is code, not configuration.\""
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author: Paul Cioanca, Platform Engineer at Supabase
subheading: Also Trusted By Leading AI and Data Platforms
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- body: Snowflake manages 100K+ daily deployments across AWS, Azure, and GCP with Pulumi — massive-scale infrastructure supporting AI/ML workloads for thousands of enterprise customers.
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**Snowflake** manages 100K+ daily deployments across AWS, Azure, and GCP with Pulumi — massive-scale infrastructure supporting AI/ML workloads for thousands of enterprise customers.
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logo: /logos/pkg/snowflake.svg
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cta: Read the Story
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link: /case-studies/snowflake/
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- body: BMW enables 15,000 developers to access self-service infrastructure while maintaining enterprise governance.
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**BMW** enables 15,000 developers to access self-service infrastructure while maintaining enterprise governance.
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cta: Read the Story
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logo: /logos/customers/bmw.svg
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link: /case-studies/bmw/
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capabilities:
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title: Code-Native Infrastructure for Dynamic AI Workloads
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description: |
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Infrastructure written in Python, TypeScript, and Go. The same languages your ML engineers already know. No proprietary configuration languages.
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Infrastructure written in Python, TypeScript, and Go. The same languages your ML engineers already know.
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No proprietary configuration languages.
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building_blocks:
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title: "Why AI Infrastructure Requires Dynamic Orchestration"
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