Checkly vs Pieces: Which Is Better in 2026?

Checkly is a monitoring platform that uses code-based synthetic monitoring to continuously check that your APIs and critical user flows actually work in production — catching problems before your users do. Rather than just checking whether a server responds, Checkly runs real, scripted checks (including full browser-based end-to-end tests) against your application from around the world, verifying that key journeys — logging in, checking out, calling an API — behave correctly. This proactive, behavior-based monitoring gives teams confidence that their most important flows are working, not just that their servers are up.

The platform lets developers write checks as code, using familiar tools and frameworks, to monitor API endpoints and simulate real user interactions in a browser, running these checks on a schedule from multiple locations. When something breaks — an API returns errors, a checkout flow fails, performance degrades — Checkly alerts the team quickly, with the details needed to diagnose the issue. By embracing a "monitoring as code" approach that fits modern development workflows, and by testing real user-facing functionality, Checkly bridges the gap between testing and production monitoring in a way developers appreciate.

Checkly is used by development and DevOps teams that want to ensure their APIs and key user flows are reliable in production and prefer a code-based, developer-friendly approach to monitoring. By catching failures in critical journeys early — from anywhere in the world — it helps teams maintain reliability, protect the user experience, and resolve issues before they cause significant impact. Its synthetic, end-to-end focus complements traditional observability by verifying that things actually work as users would experience them. As applications grow more complex and reliability becomes ever more important, proactive synthetic monitoring of real flows is increasingly valuable. For development teams that want to monitor their APIs and critical user journeys in production with code-based synthetic checks, Checkly offers a powerful, modern and developer-friendly solution.

Pieces is an AI productivity tool for developers built around a powerful idea: giving you a persistent, on-device memory of your work so you can capture, organize and recall the code snippets, context and materials you encounter throughout your day. Developers constantly deal with fragments — useful code snippets, error messages, links, commands, conversations — scattered across editors, browsers and chats, and lose enormous time re-finding or reconstructing them. Pieces acts as an intelligent long-term memory that helps you save and instantly retrieve all of it.

The tool lets you capture snippets and context with rich metadata automatically added by AI, then find them again through smart search and AI assistance. But its more ambitious feature is the "Long-Term Memory" copilot, which can passively remember your workflow — what you were working on, what you looked at, what you did — so you can ask it questions later like "what was that solution I found yesterday?" and get answers grounded in your own past activity. This addresses the very real problem of context loss and constant re-orientation that fragments a developer's focus.

Crucially, Pieces emphasizes running AI on-device for privacy, so your code and context stay local, and it integrates across the tools developers use — editors like VS Code and JetBrains, browsers, and more — to fit naturally into existing workflows. It also offers an AI copilot you can chat with about your code and saved materials. This makes it appealing to developers who want to reduce the friction of context switching, retain hard-won knowledge, and have an AI assistant that actually knows what they've been doing. As AI copilots become standard, ones with persistent, personal memory of your work stand out. For developers who want to capture their snippets and context and never lose track of what they did, Pieces offers a thoughtful, privacy-conscious and genuinely useful AI memory.