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The "Obvious" Math That's Wrong Engineer A: "Datadog is $15K/month. Prometheus is free. We should self-host." Engineer B: "But we'd need to pay an SRE to run it. That's $150K/year." Engineer A: "Prometheus doesn't need a full SRE. It's easy." Engineer B: "Famous last words." This conversation happens at every company. Both sides have points. The real math is more complex. The Total Cost Breakdown Managed (Datadog, New Relic, Dynatrace) : Licensing: $X/month (scales with hosts, events, logs) Integration time: 1-2 weeks per service Training: 1 day per new hire Ongoing: minimal Self-hosted (Prometheus + Grafana + Loki + Alertmanager) : Infrastructure: hosting costs (~$500-$5000/month depending on scale) Initial setup: 2-4 weeks of engineering time Ongoing maintenance: 10-20% of 1 FTE Upgrade costs: quarterly, each upgrade ~1 week Storage growth: ~20% per year Expertise: junior → senior SRE hire required The honest answer: managed is cheaper for teams under 50 engineers. Self-hosted becomes cheaper around 200+ engineers if you can run it well . The Real Variables It's not just licensing cost vs. hosting cost. These factors matter more: 1. Data volume growth Managed tools charge per GB ingested or per metric. If your logs 10x, your bill 10x's. Self-hosted scales linearly with compute. You control the growth. 2. Retention requirements Managed tools often charge extra for long retention. Self-hosted you store as much as your disk allows. 3. Cardinality Prometheus dies at high cardinality. Datadog handles it but charges more. High-cardinality metrics are where self-hosted breaks. 4. Incident rate Heavy incident load means heavy query load on your monitoring tools. Self-hosted needs bigger compute for this. 5. Team expertise If your team has never run Prometheus, you'll spend 6 months in the pit learning cardinality mistakes, retention tuning, and HA setups. That's not free. The Break-Even Calculation Rough calculation for a 50-engineer startup: Managed (Datadog) : - Licensi

The window is almost shut. On August 19, eight startups will take the stage at Stripe Tour Sydney in front of investors, global press, and the Australian tech community. One startup walks away with automatic entry into TechCrunch Disrupt in San Francisco — no application, no further competition, a guaranteed spot on the world’s most […]

From AI workflows to battery life and security, here's what it's really like to live with Vertu's luxury foldable every day.

I host a Postgres podcast, and I recently recorded a conversation with Panos Antonopoulos, a Distinguished Engineer at Microsoft who spent 13 years working on SQL Server before moving onto Postgres and HorizonDB. Panos told me that at a high level, Postgres felt very familiar as he started to work on it, that the concepts are very similar. Transactions, storage, & more—the fundamentals transfer surprisingly well. We also covered: The cleanliness of the Postgres codebase. How LLMs are making it easier to digest years of Postgres design discussions that are publicly available on the mailing lists. Why Postgres has become the default answer for so many workloads, and why more people seem to be asking, "Why not Postgres?" Shared-storage architectures and some of the work he's doing in Azure HorizonDB. One quote that stuck with me: "That was a shocking experience for me. I could understand new areas in Postgres much faster than I could for SQL." For people who have worked across multiple database systems (Oracle, SQL Server, MySQL, Postgres, etc.), I'm curious whether you've had a similar experience—or a completely different one. Podcast/transcript here if anyone is interested: https://talkingpostgres.com/episodes/working-on-postgres-after-13-years-on-sql-server-with-panagiotis-antonopoulos submitted by /u/clairegiordano [link] [留言]

Databricks has remade its image into an AI company and has published research on the cost savings of open weight AI models for coding.