Systems | Development | Analytics | API | Testing

The best analytics tools for SaaS companies in 2026 are Databox for cross-source reporting and AI analysis across revenue, marketing, and product data; Amplitude, Mixpanel, PostHog, and Heap for product analytics; Power BI for Microsoft-stack teams with a data function; and ChartMogul and Baremetrics for subscription revenue metrics. Eight tools, three jobs. Which job is yours decides the shortlist, and the tradeoffs below decide the pick.

Every organization says it wants self-service analytics, but very few have it. What actually stalls true self-service analytics is the invisible work that has to happen before anyone can ask a data question: the semantic layer, query engine, security model, and analytics app. That work lands on your product and data teams, who are already time-strapped to deliver core features and strategy, and it lands on them again every time a new question, a new tenant, or a new dashboard request arrives.

Bruno is an open source, offline-first API client that stores your API collections as plain text files inside your Git repo. It gives you a fast GUI for building and sending REST, GraphQL, gRPC, and WebSocket requests, plus a scripting layer for writing tests against the responses. This guide covers Bruno API testing from install to CI, so by the end you can build a request, assert on its response, and run the whole suite from the command line.

Modern Security Operations Centers (SOCs) are transitioning towards agentic architectures. Traditionally, SOCs relied on rigid, rule-based automation and high human effort. We built an Agentic SOC powered by AI agents that don't just follow rigid, hardcoded rules, but also autonomously investigate, reason, and act like human analysts.

Before every session, I used to type some version of this: Sixty-two words of setup, and all of it gone the moment I closed the window. Here is the same request in week two, after the assistant had learned how my project works: That is the whole feature. Everything below is how it works and what I learned building it. THE SHORT ANSWER.

Kong AI Gateway already gives platform teams a central place to route, observe, and enforce policy across AI traffic. Security teams working alongside those platform teams often need another set of answers: Which agents are connected? Which attack paths actually succeed against them? What happened across the full session or tool chain? And if an agent is compromised, can its next action be stopped before it reaches an enterprise system? The integration is designed to be complementary.

A prospective renter checks an apartment on Zillow, then on Apartments.com, and sees two different prices. They are not comparing two units. They are looking at one property described by two systems that disagree. Most property platforms treat that gap as a data-quality nuisance to clean up after the fact. It has quietly become something else: a signal that shapes whether customers, syndication partners, and now AI search tools trust the platform at all.

Detecting API performance issues before they impact users is the difference between APIs that scale reliably and those that quietly erode user trust. When users encounter lag, failed requests, or timeouts, the consequences are immediate: lost transactions, customer frustration, and brand damage. Teams that build scalable APIs recognize that performance is not just a technical metric – it’s fundamental to business health and reputation.

N|Solid Runtime has been downloaded more than 1.15 million times in 2026, bringing total downloads to more than 2.4 million. In August alone, download activity increased from 105,619 to 209,052, a 98% month-over-month increase.

Learn how dbt helps APAC retail and CPG teams test, govern, and trace their data before it drives critical inventory, promotion, and reordering decisions.