⚡ Speed is Iceberg’s biggest Achilles’ heel. Most teams chase Velox or Gluten for Spark speed. Yet the real wins sit in fast I/O and layers untouched. We’ve spent years making lakehouse pipelines fast—but this isn’t a table-format debate. It’s slow I/O, shuffles, and reprocessing driving costs up. Speed is a stack: ⚡ SIMD 📊 Columnar processing 🔍 Query plans 💾 Fast I/O 🗄️ Metadata 📐 Storage layout 🔗 Indexes 🔄 Merging Full-stack engine: 3–4× faster than OSS Spark on Iceberg, better price/perf than Photon, no lock-in.

🧠 Anthropic and OpenAI may have taught Databricks and big data vendors a pricing lesson. 🤖 Frontier models price inference by tokens, not GPU FLOPs. it’s intuitive: bigger context or more work costs more. Cloud data platforms still price by compute: DBUs, EC2 hours, slot time. Ask: “How many DBUs will this query use on Databricks vs EMR?” 🤷 Ask: “How much data will it scan?” most engineers can answer fast.

Everyone assumes usage-based pricing in cloud data is fair and efficient. ⚖️ But it has a real problem: It can stop vendors for building faster engines. Traditional models priced on value—Oracle earned more for standout features. Now, with EMR or Databricks, bills hinge on compute usage. Customers win from compute efficiency (lower costs), but vendors lose revenue, pushing them to own the compute layer for pricing control. Sure, usage models offer flexibility, but they misalign incentives long-term. What's better? We need outcome-based pricing that rewards real value, like queries executed or data processed. 🚀📊

🚀 Quanton now also powers Apache Iceberg natively — delivering 3× faster Spark workloads! When we launched Quanton, the goal was ambitious: make Spark truly lakehouse-optimized — faster, smarter, and format-aware. 👇