Operations | Monitoring | ITSM | DevOps | Cloud

The latest News and Information on API Development, Management, Monitoring, and related technologies.

LLM API Pricing Comparison In 2026: Every Major Model, Ranked By Cost

Compare LLM API pricing across OpenAI, Anthropic, Google, DeepSeek, and Mistral in 2026. Full pricing tables, hidden cost breakdowns, and proven strategies to cut AI spend. Written for engineering leads, platform teams, and FinOps practitioners evaluating or optimizing production AI costs.

What Architecture Ensures Long-Term Scalability in a Rails-Based B2B Platform?

Scalability is not a feature you add later; it is a choice made at the architectural level from day one. A Rails-based B2B platform that handles growing clients, data, and transactions without slowdowns or costly rewrites is built on a modular design, clear domain boundaries, background job processing, caching, and a database strategy that supports load distribution and horizontal scale. Get these foundations right, and you stay in control of growth instead of reacting to problems after they appear.

How a Marketing Intern Ended Up Running Claude in a Terminal

Before I ever ran Claude in my terminal, I thought I already understood AI tools pretty well. Like most people, I had used ChatGPT, Google Gemini, and Perplexity for everyday tasks. Such as helping with schoolwork, organizing ideas, summarizing information, or getting through something faster when time was tight. They were useful, but they still felt separate from how real work happened.

Detect, Communicate, Resolve: Checkly's Agentic Workflow End-to-End

Coding agents are the fastest-growing audience for the Checkly CLI, and we're doubling down on them. In this session, Stefan hands Claude a real e-commerce app, lets it set up monitoring with `npx checkly init`, generate Playwright tests through MCP, and walk an actual alert end-to-end with Rocky AI in the loop.

Two AI agents, one incident: Rocky AI comes to the terminal

A Playwright Check fails at 2 am. The login flow is broken. Until today, that alert triggered a human to get up, open the Checkly dashboard, copy Rocky AI root cause analysis (RCA), and then tell an agent to get to work. There were two AI agents, one incident, and no way for them to talk to each other. The extended checkly checks and new checkly rca CLI commands close that gap. Your coding agent can now pull Rocky AI's analysis into its ongoing work, read the diagnosis, and go fix the code.

Connecting Agents for Real-Time Root Cause Analysis with Checkly's Rocky AI

Rocky, Checkly's AI agent, monitors production sites and provides an analysis for every failing check. Previously, a coding agent couldn't access this analysis, leaving incidents and agents disconnected. Now, you can access all the analyses via the Checkly CLI (or API) and tell your coding agent, "Hey, I got a Checkly alert. Please investigate!" With Rocky's structured analysis delivered inline, the coding agent can start with a strong hypothesis, fix issues, and propose a PR in one session.