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Compare 9 incident management tools: PagerDuty, Opsgenie, Incident.io, Rootly, FireHydrant, BetterStack, Grafana OnCall, Squadcast, and Last9. Features, pricing, and which fits your team. Product Marketing Manager.

Onboarding new engineers to complex Kubernetes environments is expensive. Junior engineers need to learn cluster architecture, understand organizational conventions, navigate internal documentation, and build relationships with senior team members who can answer questions. The process takes weeks or months, and during that time, senior engineers spend significant time mentoring instead of working on complex problems.

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.

For eight years, the survey behind the SRE Report has used a consistent methodology. That consistency allows us to track how reliability work evolves over time, rather than relying on snapshots. One of the most stable questions in the survey asks respondents to estimate how much of their work, on average, is spent on toil. Between 2020 and 2024, responses showed a gradual decline in reported toil.

Everyone wants autonomous incident response. Most teams are building it wrong. ‍ The ultimate goal of autonomy in SRE and DevOps is the capacity of a system to not only detect incidents but to resolve them independently through intelligent self-regulation. However, true autonomy isn't born from automating random, isolated tasks. It requires a stable foundation: a Reference Architecture.

Policy changes in Kubernetes are supposed to improve security, enforce standards, or optimize resource usage. But when a policy change triggers cascading pod failures across multiple namespaces, the investigation becomes a race to identify what changed before more workloads are affected.

You might expect an AI-SRE agent to target 100% reliable services, ones that never fail. It turns out that past a certain point, however, increasing reliability is worse for a service (and its users) rather than better! Extreme reliability comes at a non-linear cost: maximizing stability limits how fast new features can be developed, dramatically increases the operational cost, and reduces the features a team can afford to offer.

The promise of Artificial Intelligence in Site Reliability Engineering (SRE) is seductive: an autonomous system that never sleeps, instantly detects anomalies, and fixes broken infrastructure while humans focus on high-value work. However, the gap between a demo-ready chatbot and a production-grade Autonomous AI SRE is vast. In complex, noisy environments like Kubernetes, a “naive” implementation of Large Language Models (LLMs) is not just ineffective, it can be dangerous.

Gartner predicts that AI agents will be implemented in 60% of all IT operations tools by 2028, up from fewer than 5% at the end of 2024. This acceleration has sparked an explosion of AI SRE solutions, from enterprise platforms to open-source alternatives, all promising faster root cause analysis and reduced MTTR.