The article nails the high-end scaling challenges but misses the gritty early-stage realities. As a broke solo founder, I'm living in the trenches where BigQuery’s $5-per-terabyte scan cost and generous free tier are nothing short of survival-grade. At this stage, it’s not about optimization — it’s about optionality. A fully managed, serverless warehouse like BigQuery means I can validate ideas without managing infrastructure or burning cash I don’t have. Is it scalable? Not in the long run. But right now, it’s what lets me move fast with zero DevOps overhead.
The transition from survival to scale doesn’t happen all at once. When some early capital comes in — call it $20K to $30K — I’ll migrate to Snowflake for its auto-suspend compute, efficient concurrency handling, and deeper ecosystem. Paired with orchestration tools like Tobiko or SQLMesh, I can implement incremental processing to cut down warehouse transformation costs. Full-table recomputation is fine when you’ve got a $300K data budget, but not when every dollar counts.
At mid-scale (~$300K in spend), I’ll take a hybrid approach: ClickHouse Cloud for real-time OLAP, Snowflake for curated batch datasets, and Iceberg as the open table format glue. Starburst will handle federation, letting me query across silos and preserve performance. At this point, I'm not choosing tools based on branding — I’m choosing based on query cost, tail latency, and governance overhead. Real-time responsiveness will define user experience for an AI-native platform like Hologram, and ClickHouse blows traditional cloud warehouses out of the water for OLAP workloads.
By the time Hologram reaches hyperscale, there’s no question: the stack goes in-house. I’ll run Iceberg on S3, train on self-hosted Spark, serve real-time workloads through ClickHouse, and archive logs with Hydrolix. Every component is modular, open, and chosen for its economic unit cost per query, per job, and per insight. Governance will no longer be a checkbox — it’ll be a survival need. At this point, I’ll be optimizing down to the DAG level in Tobiko and enforcing lineage, versioning, and reproducibility like a government agency.
What the article got right is the pressure on modern data teams to justify spend. What it missed is the uneven terrain founders have to navigate from zero to scale. The path to intelligent AI infra is modular, staged, and budget-bound. It’s not sexy — but it’s how winners are built.
The article nails the high-end scaling challenges but misses the gritty early-stage realities. As a broke solo founder, I'm living in the trenches where BigQuery’s $5-per-terabyte scan cost and generous free tier are nothing short of survival-grade. At this stage, it’s not about optimization — it’s about optionality. A fully managed, serverless warehouse like BigQuery means I can validate ideas without managing infrastructure or burning cash I don’t have. Is it scalable? Not in the long run. But right now, it’s what lets me move fast with zero DevOps overhead.
The transition from survival to scale doesn’t happen all at once. When some early capital comes in — call it $20K to $30K — I’ll migrate to Snowflake for its auto-suspend compute, efficient concurrency handling, and deeper ecosystem. Paired with orchestration tools like Tobiko or SQLMesh, I can implement incremental processing to cut down warehouse transformation costs. Full-table recomputation is fine when you’ve got a $300K data budget, but not when every dollar counts.
At mid-scale (~$300K in spend), I’ll take a hybrid approach: ClickHouse Cloud for real-time OLAP, Snowflake for curated batch datasets, and Iceberg as the open table format glue. Starburst will handle federation, letting me query across silos and preserve performance. At this point, I'm not choosing tools based on branding — I’m choosing based on query cost, tail latency, and governance overhead. Real-time responsiveness will define user experience for an AI-native platform like Hologram, and ClickHouse blows traditional cloud warehouses out of the water for OLAP workloads.
By the time Hologram reaches hyperscale, there’s no question: the stack goes in-house. I’ll run Iceberg on S3, train on self-hosted Spark, serve real-time workloads through ClickHouse, and archive logs with Hydrolix. Every component is modular, open, and chosen for its economic unit cost per query, per job, and per insight. Governance will no longer be a checkbox — it’ll be a survival need. At this point, I’ll be optimizing down to the DAG level in Tobiko and enforcing lineage, versioning, and reproducibility like a government agency.
What the article got right is the pressure on modern data teams to justify spend. What it missed is the uneven terrain founders have to navigate from zero to scale. The path to intelligent AI infra is modular, staged, and budget-bound. It’s not sexy — but it’s how winners are built.
Great insights. Indeed a very complex set of tools depending on scale and budget. Thanks for sharing.