Systems | Development | Analytics | API | Testing

The Observability Gap: Why Monitoring Data Should Drive Tests

Most teams already know a lot about production. They have dashboards. They have traces. They have alerts. They have enough telemetry to explain what happened after an incident and enough graphs to argue about it for the rest of the week. Then they go to test a change and start from scratch. The integration tests hit a hand-written mock that returns {"status": "ok"}. The load tests replay a CSV somebody exported months ago. Staging is close enough to production right up until it matters.

Why Native Observability is the Heart of Hybrid Cloud

In the current enterprise technology landscape, we’re witnessing an industry-wide scramble. As organizations shift from monolithic architectures to complex environments leveraging heterogeneous infrastructures, cloud-based data platforms are hitting a visibility—i.e., observability—wall. Their response has been a wave of reactive, multi-billion-dollar acquisitions designed to "bolt-on" the observability that they lack natively.

Why observability tools are missing critical debugging data (no matter how you sample)

There's a common belief in the observability space: if you just collect more data, you'll have what you need to debug any issue. The reality is more frustrating: even with 100% unsampled observability, you're still missing critical debugging data. There's a common belief in the observability space: if you just collect more data, you'll have what you need to debug any issue. The reality is more frustrating: even with 100% unsampled observability, you're still missing critical debugging data.

Moving Our Observability Data Collector from Sidecars to eBPF

For years, the Kubernetes sidecar pattern has been a practical way to capture observability data. Running a collector alongside each application pod gave us deep visibility into traffic, including full request and response payloads across supported protocols. However, as cloud-native environments have grown more complex, the limitations of sidecars—such as resource overhead, operational complexity, and scaling challenges—have become more apparent.

How to Do Full-Text Search Across All Application Traffic with Speedscale

Modern DevOps observability tools are excellent for monitoring system health, tracking distributed traces, and aggregating metrics. However, they lack the fidelity needed for full-text search across application traffic. While observability platforms excel at showing what happened and when, they often fall short when you need to find where a specific piece of data (like an email address, user ID, or transaction token) appears as it flows through your entire application stack.

Complete API Observability: Building Production-Grade Analytics for DreamFactory with Logstash, Elasticsearch, and Grafana

API observability is a critical operational requirement for production REST API platforms. DreamFactory, as an enterprise API generation and management platform, produces high-volume API traffic that demands robust logging, real-time analytics, and diagnostic capabilities. This guide demonstrates how to implement a complete observability stack using Logstash for log ingestion and processing, Elasticsearch for indexed storage and search, and Grafana for advanced visualization and alerting.

Expanded Observability, Orchestration, and Security with Kong Gateway 3.13

Discover expanded OpenTelemetry support, full cloud-native authentication, powerful Datakit orchestrations, and PCI DSS 4.0 attestation for Kong Konnect and Cloud Gateways. As API ecosystems grow more complex, maintaining visibility and security shouldn't be a hurdle. Kong Gateway 3.13 simplifies these challenges with expanded OpenTelemetry support and more flexible orchestration. These new capabilities not only make your APIs more observable but also make it easier to implement orchestration.

[Workshop 201] Beyond the Gateway: Governing Secure and Observable AI Agents

Join our 201-level session, "Beyond the Gateway: Governing Secure and Observable AI Agents," to move from managing simple AI calls to governing autonomous systems. Building on 101's foundation, this workshop provides the advanced blueprint to secure agent connections with granular, identity-aware policies and robust tool-use governance, ensuring you control what agents can access and which actions they can perform. We'll also dive into how to observe these autonomous interactions, using real-time behavior tracing and cost analysis to maintain performance and compliance.

How to Leverage Moesif Effectively for API Observability

You can make your API observability posture more powerful and beneficial by treating Moesif as an engineering implement. The platform automatically captures API traffic out-of-the-box and provides actionable analytics and visualizations. However, the degrees to which they precisely and empirically illustrate the data, depend on where and how you’ve integrated Moesif.