Software and SaaS development
Digital products that solve, scale and evolve.
Insights
Guides, analysis and lessons on product, artificial intelligence, growth, communications and cloud.
Editorial themes
Digital products that solve, scale and evolve.
AI systems for real-world processes.
Demand, catalogue, partners and conversion.
Every conversation in the right channel.
Available, observable and ready services.
Design fallback rules that distinguish temporary from permanent errors, respect preferences and permissions, and prevent duplicate messages when switching channels.
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Compare cloud showback and chargeback, decide which costs you can allocate confidently, and plan a gradual transition that avoids disputes and harmful cuts.
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Choose the right test level, limit the scope, and agree on stop signals before you begin. This guide explains how to verify service recovery without unnecessary production changes.
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Prioritize testing based on a failure’s impact, likelihood, and detectability. This practical framework helps validate changes without slowing releases or leaving critical risks uncovered.
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Compare persistent migration with read-time transformation based on consistency, performance, and consumers. Use practical criteria to choose an approach and plan a reversible transition.
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A multi-region deployment can improve continuity or reduce latency, but it adds complexity. Assess data, dependencies, operating costs, and team capacity before deciding.
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Decide which features to keep, limit, or stop when a dependency fails. This practical framework helps protect data, inform users, and restore service safely.
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Set retry windows for failed messages according to their cause, urgency, and validity. A clear policy reduces duplicates, delays, and unnecessary manual reviews.
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Set how long each integration can wait, decide what happens when the limit expires, and recover uncertain outcomes without duplicating actions or leaving processes stuck.
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Set API quotas according to the consumer, operation, and available capacity. Learn how to respond when limits are exceeded and refine policies using metrics.
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A practical framework for deciding whether to fix an individual incident, eliminate its root cause, or redesign the process based on frequency, impact, and risk.
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Learn how to choose product analytics events and properties based on real decisions, reduce noise, and verify that your data is reliable before using it to guide product work.
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