60% GPU utilization and 3-second response times? GPU utilization is the wrong signal for LLM inference. Here's why TTFT, KV-cache pressure, and queue depth - not utilization - predict user-facing latency.
Predict GPU hardware failures 48–72 hours in advance. A guide to the five rate-based signals — ECC error trends, XID events, thermal ramp, row remap exhaustion, PCIe downtraining — and how to combine them into a composite health score.
Flat per-token pricing is wrong by 10–50× per request. Prefill vs decode, batch sharing, and cache effects break the math. How to attribute real GPU cost - compute, energy, and dollars - to each inference request.
How database partitioning works in PostgreSQL and MySQL. Range, list, and hash partitioning with SQL examples and guidance on when to partition vs shard. Prathamesh works as an evangelist at Last9, runs SRE stories - where SRE and DevOps folks share their stories, and maintains o11y.wiki - a glossary of all terms related to observability.
How database sharding works, common strategies (hash, range, directory), shard key selection, and the operational cost of running a sharded database in production. Prathamesh works as an evangelist at Last9, runs SRE stories - where SRE and DevOps folks share their stories, and maintains o11y.wiki - a glossary of all terms related to observability.
Tune PostgreSQL and MySQL for production with connection pooling, memory configuration, write path optimization, vacuum management, and lock contention fixes. Technical Product Manager at Last9.
Distributed traces track how your system processed a single request — not what your customers did over time. Confusing the two leads to poorly instrumented systems.
Find and fix slow SQL queries using execution plans, missing index detection, N+1 pattern fixes, and pagination strategies for PostgreSQL and MySQL. Product Marketing Manager.
How database indexes work, when to use B-tree vs hash indexes, clustered vs non-clustered indexes, and how to tell if your indexes are actually helping.