Private Markets, Angel and Secondary Investing, Sacra

Sacra

Bringing transparency to private markets so investors, founders, and employees can build on conviction.

Sacra delivers deeply researched insights on the private companies shaping tomorrow's markets. By surfacing the data, context, and operator perspective that are missing from private markets today, we help secondary buyers and sellers price equity accurately and unlock liquidity for the people building these businesses.

Private Markets, Angel and Secondary Investing

Bringing transparency to private markets so investors, founders, and employees can build on conviction. Sacra delivers deeply researched insights on the private companies shaping tomorrow's markets. By surfacing the data, context, and operator perspective that are missing from private markets today, we help secondary buyers and sellers price equity accurately and unlock liquidity for the people building these businesses.

Private Capital Markets

<ul><li>The MLOps landscape has evolved from targeting researchers who preferred simple Python APIs and quick setup to serving DevOps professionals who need enterprise-grade telemetry, monitoring and deployment tools that can handle high-volume production workloads.</li><li>LLM deployment differs fundamentally from traditional software deployment because models are massive files (often 10-20GB) that must be loaded across CPU and GPU memory, creating new technical challenges around memory management, version control, and continuous deployment that existing DevOps tools weren't built to handle.</li><li>Building on microservices and serverless, compound AI systems merge together multiple custom AI models, LLMs and diffusion models with databases and external APIs—spawning a new generation of tools like Outerport and Modal ($32M raised) that let developers automatically deploy and run multi-LLM systems across CPU/GPU architectures without worrying about infrastructure.</li></ul>

The MLOps landscape has evolved from targeting researchers who preferred simple Python APIs and quick setup to serving DevOps professionals who need enterprise-grade telemetry, monitoring and deployment tools that can handle high-volume production workloads.LLM deployment differs fundamentally from traditional software deployment because models are massive files (often 10-20GB) that must be loaded across CPU and GPU memory, creating new technical challenges around memory management, version control, and continuous deployment that existing DevOps tools weren't built to handle.Building on microservices and serverless, compound AI systems merge together multiple custom AI models, LLMs and diffusion models with databases and external APIs—spawning a new generation of tools like Outerport and Modal ($32M raised) that let developers automatically deploy and run multi-LLM systems across CPU/GPU architectures without worrying about infrastructure.

Towaki Takikawa, CEO And co-founder Of Outerport, On The Rise Of DevOps For LLMs