Blog
Why Does Azure Cosmos DB Cost $68K/Year for a 5TB Feature Store?
Last week an engineer sent me their Azure bill. Cosmos DB alone: ~$5,700/month. For a read-heavy feature store serving ML models. For the same workload, a BoulderKV-style architecture (object storage + local cache) comes out to ~$2,500/month under reasonable assumptions. “Is this normal?” Yes, if you’re running Cosmos DB at scale with multi-region replication. A Real Workload Specs: 5TB of feature data (embeddings, signals, model inputs) 10K reads/sec (every inference hits the store) 100 writes/sec (feature updates) 3 regions (East US, North Europe, East Asia) 95% reads, 5% writes (classic pattern) Nothing exotic. Standard production setup. Let’s see what it costs.
February 1, 2026
When Cosmos DB Gets Expensive for Feature Stores
A data engineer messaged me last week: “We’re spending $4K/month on Cosmos DB for our feature store. Reads are steady, but the bill feels off.” I asked about their architecture. Almost 90% of the bill was from loading data, not serving reads. This is a classic Cosmos DB surprise. The Feature Store Pattern Here’s the standard ML feature store architecture: Daily Spark job computes features from raw data Write snapshot to Blob (parquet files, checkpoints) Load snapshot into Cosmos DB for serving ML models read features during inference The workflow makes sense. Blob is cheap for batch writes. Cosmos DB is fast for point reads. But step 3–loading 1TB into Cosmos DB every day–is where the bill explodes.
February 1, 2026
When DynamoDB Gets Expensive for Feature Stores
A data engineer messaged me last month: “We’re spending $20K/month on DynamoDB for our feature store. We’re doing thousands of reads per second, but something feels off about the bill.” I asked about their architecture. Turns out, 92% of their bill was from loading data, not serving reads. This is shockingly common. The Feature Store Pattern Here’s the standard ML feature store architecture at most companies:
December 30, 2025
Why Does DynamoDB Cost $330K for a 5TB Feature Store?
Last week an engineer at a Series B startup sent me their AWS bill. DynamoDB alone: $27,000/month. For a feature store serving ML models. “Is this normal?” Yes, and it’s completely avoidable. If you’re running read-heavy workloads on DynamoDB at scale, you’re probably paying 10-50x more than you need to. A Real Workload Here’s production infrastructure at thousands of companies: ML feature store serving models. Specs:
December 28, 2025
Introducing BoulderKV: A Global, Read-Optimized Key-Value Store
We’re excited to introduce BoulderKV, a global, read-optimized key-value store built on object storage. The Problem Modern applications need to serve data globally with low latency, but traditional databases can be expensive and complex to scale worldwide. Meanwhile, many workloads don’t need strong consistency—they need durability, availability, and predictable costs at scale. Our Solution BoulderKV is designed specifically for workloads where: Reads dominate writes Datasets are large, but only a small fraction is frequently accessed Eventual consistency is acceptable Global distribution matters By building on object storage (like S3), BoulderKV delivers:
December 19, 2025