Analysis, notes, and updates from the team behind Falcon.
Charging on a meter is the wrong incentive.
Do you know the cost of each workload? Can you lower it? More teams than ever can measure workload costs. The harder challenge is lowering unit cost as workload volume grows.
Everyone is running the right playbook — more governance, more discipline. Waste rises anyway.
Ask one question of your data platform contract: what happens when a system wastes compute?
Every data platform decision you've made assumes you can be fast or cheap — not both. That tradeoff was made by the market, not by a law of computing.
The industry already left the JVM — the incumbents rebuilt their engines in C++. They fixed the physics, and kept the pricing.
Two mandates are landing on the same desk. Both are right. They collide in one place — your compute layer.
Apache Spark is open-source software. The license costs "nothing". So how is running Spark one of the most expensive line items on your infrastructure budget?
A multibillion-dollar e-commerce platform provider replaced the engine behind the Databricks Photon pipelines that run its commercial transaction analytics. Four production pipelines, compared head-to-head.
How much of your data does your AI actually see? Most teams we meet sit at Stage 2–3, with an AI-at-scale strategy priced out of reach.
Where is your team today? Most teams we meet sit at Stage 2–3 — real progress, inside the same ceiling.
Lowering the per-query cost before you reach the valley of death.
A structured engagement that benchmarks Falcon against your actual production workloads — on your data, in your architecture, before any commercial commitment.
Consumption pricing is cost-effective for prototyping, not scalability. That's why we built Falcon the way we did.
The license is free. The compute bill that follows it is not — and at production scale, it sets the ceiling on what your team can build.
The founding story of the Falcon engine: two failed versions, a language decision made over a winter holiday, and the architectural conflict that took three rewrites to resolve.
The prototype works. Everyone agrees it should ship. Then the production cost analysis kills it — and the pattern repeats across government and commercial programs alike.
Straight answers to the questions data engineering and platform teams ask before moving production compute off the JVM.
The operational sequence for moving a live batch or streaming pipeline off Databricks compute and onto Haevek's Falcon Compute Platform, without an outage.
Let Databricks do what Databricks does best. Move batch and streaming tasks to Haevek's Falcon Compute Platform to save on the base load.
Benchmarks on your real data in 2-4 weeks. No replatforming.