Systems | Development | Analytics | API | Testing

How to Set Up End-to-End Performance Testing for Multi-Cloud

Relying solely on a single cloud provider’s published benchmarks can lead to unexpected performance gaps. The Q1 2026 Backblaze report demonstrated that performance varies widely by region and provider – for example, AWS led in US-East file transfers, while Cloudflare R2 and Wasabi outperformed in EU-Central. Without validating real-world performance in every region and for each vendor in your stack, you risk missing critical blind spots that directly affect user experience.

Quantum Computing's Impact on Cloud Testing

Quantum computing is beginning to influence complex scenarios in cloud testing, offering the potential to process vast possibilities in parallel. However, as of 2026, the technology remains experimental. Early hardware is limited in scale and prone to errors, so broad business impact is still several years away. Teams running large-scale performance testing or cryptography simulations should monitor quantum’s progress, but avoid overcommitting resources until the technology matures.

Cheap Tokens, Exploding Bills: The Hidden Cost of AI Agents

80% cheaper AI model. Nearly 2X the data warehouse bill. Switching to a cheaper AI model can cut your token costs by 80%, but it might accidentally double your public cloud data warehouse bill. In this video, we break down the "Agentic Cost Shift"—how autonomous AI agents generate hidden, high-compute SQL queries that drive up data lakehouse costs—and how to fix it using Cloudera. See where the real cost of AI is shifting.

What's Next Is Not More AI. It's Better Foundations.

The next real advantage in artificial intelligence will not come from the next AI tool or application. It will come from a stronger data foundation beneath it. At Hitachi Vantara we work every day with customers on the data supporting their systems. That vantage point has led me to a simple conclusion: the leaders who pull ahead will not be the ones with the most advanced AI. The leaders will be those whose data foundations are strong enough so that AI can be trusted to act.

Top 17 AI Testing Tools

Generating tests isn’t the hard part anymore. Ask an LLM to create Playwright tests, and it will produce hundreds of them in no time. The problem lies in deciding whether those tests are useful, maintaining them as your application changes, and knowing if you’ve even chosen the right tool in the first place. This article compares 17 AI testing tools across 5 categories. We’ll look at what each of them does, where it fits, what makes it different, and where it falls short.

Why do my apps keep crashing? 6 causes and how to fix them

An app crash is one of the fastest ways to drain both users and rankings. Many users will uninstall an app after just one crash, and both the App Store and Google Play will penalize crash-prone or unstable apps. At Bugfender, we’ve been investigating app crashes since 2014. In this post we’re going to share all our key learnings, so you can: You’ll come away with a reliable, repeatable way to anticipate app crashes – wherever they happen. This post is generally platform-agnostic.

Testing at AI speed: We built a drift detection capability, then used it on ourselves

Drift detection stays narrow on purpose. It checks contract conformance – structure, status codes, schema – and deliberately leaves workflows and business logic to functional and end-to-end tests. That’s what makes it a fast add to our pipeline to prevent API drift. That narrow scope keeps it cheap to run. Fast execution, low flakiness, low maintenance cost – the kind of checks that make you more confident shipping. Catching drift early is the whole point.

Introducing coverage gap detection: How the Zephyr Agent for Rovo ends duplicate test cases

AI has changed how fast testing teams can move, yet sorting through duplicate test cases still trips teams up. Ask the SmartBear Zephyr Agent for Rovo to generate test cases for a requirement, and coverage that used to take an afternoon comes back in a single conversation. That speed is a genuine win for teams building faster than ever, but as AI generates more test coverage, how do you make sure all of it stays worth keeping?

Thought Tank: Marketing in the Age of Agents

Join us for a live broadcast of The Thought Tank: Marketing in the Age of Agents. Our host and CMO Micheline Nijmeh sits down with Katie Marcham, SVP Marketing EMEA at ThoughtSpot, to pull back the curtain on what it actually looks like to run a modern marketing organization on live data in one of the most complex, relationship-driven markets in the world. They'll cover the transformation Katie's led over the past year: leaner teams, smarter tools, and a tighter partnership with EMEA sales, all grounded in what the data is showing in real time.

Insurance Underwriting Automation: Architecture, AI Models, and ROI (2026)

Insurance underwriting automation integrates data ingestion, validation, risk scoring, decisioning, and policy workflows. It connects core insurance platforms with rules engines, AI models, and external data sources. Insurance underwriting automation in 2026 looks markedly different from earlier pilots.