Operations | Monitoring | ITSM | DevOps | Cloud

Datadog named Leader in 2025 Gartner Magic Quadrant for Observability Platforms

We are thrilled to announce that, for the fifth consecutive year, Datadog has been named a Leader in the 2025 Gartner Magic Quadrant for Observability Platforms. We believe that this recognition reflects our continued focus on helping customers observe, secure, and act on everything that matters across their technology stack.

What is Log Loss and Cross-Entropy

You're building a classification model, and your framework throws around terms like "log loss" and "cross-entropy loss." Are they the same thing? When should you use binary cross-entropy versus categorical cross-entropy? What about focal loss? This blog breaks down these loss functions with practical examples and real-world implementations.

Cloud Log Management: A Developer's Guide to Scalable Observability

As systems move to microservices, serverless, and multi-cloud setups, debugging gets harder. You’re no longer dealing with a single log file; you’re looking at logs from dozens of services, running across different environments. Traditional debugging methods like SSH-ing into servers or adding print statements don’t scale in these environments. Cloud log management tools help by collecting logs from all your services into one place.

How to improve your observability

Coroot was designed to solve the problem of time-consuming root cause analysis. It handles the full observability journey - from collecting telemetry automatically with zero code setup (thanks, eBPF!) to simplifying the role of SREs and DevOps everywhere with instant root cause analysis powered by AI. We also strongly believe that simple observability should be an innovation everyone can afford to benefit from: which is why our software is open source!

How to think about quality in the age of cheap prototypes

When AI makes prototyping incredibly cheap, your old quality standards become a bottleneck. The key mindset shift? Quality doesn't matter equally everywhere. You can experiment with lower-quality prototypes to learn faster, then apply high standards only to what customers actually see. This isn't about lowering standards - it's about applying the right quality mindset at the right stage. Stop letting perfectionism slow down your learning phase.

From painted doors to real prototypes - a mindset shift

The economics of building software are changing everything. For years, entrepreneurs used "painted doors" - fake features to test demand - because building was too expensive. But when AI drops development costs, you can create real prototypes and gather genuine user data instead of pretending. This mindset revolution treats experiments like cheap option contracts - the lower the cost, the more you can explore. Ready to abandon painted doors for unlimited experimentation?

Streamline API testing with Proxy Mock! Capture, mock, and replay API calls locally

Alan Mon introduces Proxy Mock, a powerful tool for capturing and replaying API calls. Learn how to effortlessly record inbound and outbound API requests and responses. The demonstration highlights how Proxy Mock operates entirely on your local machine, eliminating the need for cloud services or internet connectivity for testing. See how to set up Proxy Mock, inspect captured API calls (including request/response headers, body, and unique signatures), and leverage it to mock API responses for seamless local testing, ultimately boosting productivity and reducing the need for costly non-production environments.

How We Made Our Queries 99.5% Faster

We cut log-query scanning from ~100% of data blocks to < 1% by reorganizing how logs are stored in ClickHouse. Instead of relying on bloom-filter skip indexes, they generate a deterministic “resource fingerprint” (hash of cluster + namespace + pod, etc.) for every log source and sort the table by this fingerprint in the primary-key ORDER BY clause. This packs logs from the same pod/service contiguously, letting ClickHouse’s sparse primary-key index skip irrelevant blocks.