November 2025 Summaries

Groundcover has officially launched its Datadog Migration feature, making it generally available to simplify the process of transitioning from legacy observability vendors to their platform. This tool addresses the significant challenge of migration fear by offering a fully automated, self-service solution that eliminates the need for consultants and ensures zero downtime and switching costs. It efficiently maps the entire environment, including dashboards, monitors, and integrations, while ensuring pixel-perfect dashboard translations and clear validation flows to maintain workflow integrity. The new Integrations Center supports multiple platforms like AWS, GCP, and Azure, allowing teams to configure setups directly through the UI or via API and Terraform. The tool promises significant improvements, such as 90% faster migration times and up to 70% lower total cost of ownership, positioning it as a viable and fear-free option for teams seeking to modernize their observability infrastructure.

Shahar Azulay, CEO of groundcover, announces the integration of Amazon Bedrock into their LLM Observability feature, enhancing real-time visibility for engineering and platform teams without requiring any SDKs or code changes. This feature supports Bedrock's approach to data handling, where AWS ensures data security and ownership by keeping traffic within a user's VPC and creating dedicated model copies for enterprise data. Groundcover's BYOC model guarantees that all telemetry stays within the user's cloud environment, ensuring data privacy and control. The use of eBPF allows for comprehensive, frictionless observability at the kernel layer, providing full visibility of AI interactions in real time. Furthermore, groundcover's support for Bedrock AgentCore offers insights into the logic and decision-making of AI workflows, moving beyond simple model interactions to understanding the reasoning paths of AI agents. As LLM applications evolve into more autonomous systems, the need for such visibility becomes crucial for operating AI with confidence, marking a shift away from the opaque "black box" era of AI.

Kubernetes Secrets offer a secure method to store and manage sensitive data, such as passwords and certificates, within a Kubernetes cluster, thereby simplifying the deployment and management of workloads requiring authentication or encryption. These Secrets are stored as key-value pairs in Etcd and managed declaratively, but they are only encoded using Base64, not encrypted, necessitating additional security measures like enabling encryption in Etcd or using external secrets managers like HashiCorp Vault for enhanced security. Various types of Kubernetes Secrets exist, including opaque, basic authentication, TLS, registry, and bootstrap token Secrets, each serving specific use cases, from managing authentication data to encrypting network traffic. Best practices for managing Kubernetes Secrets involve selecting the appropriate type of Secret, updating and removing outdated Secrets regularly, enabling encryption, and monitoring events related to Secrets to mitigate risks such as unauthorized access. Groundcover enhances visibility into Kubernetes clusters by tracking metrics and performance trends, aiding in efficient Secrets management and troubleshooting.

Engineering teams often start using Datadog, a popular observability tool, during their early growth stages due to its recognition and effectiveness in monitoring metrics, traces, and alerts. However, as companies scale, they encounter high costs associated with Datadog's volume-based pricing model, leading to limited visibility and expensive bills. In response, some teams consider switching to open-source solutions but face challenges in maintaining these complex systems. The text suggests that the real problem lies in the SaaS observability model, which isn't suited for rapidly growing companies. It proposes using Bring Your Own Cloud (BYOC) and eBPF technology as alternatives, offering flat costs and full visibility without the drawbacks of traditional SaaS tools, thus advocating for a shift to more scalable observability solutions that align with company growth.

At re:Invent 2025 in Las Vegas, groundcover invites attendees to engage in discussions about observability needs and to participate in a speaking session titled "Tracing the Untraceable: Full-Stack Observability for LLMs and Agents," which addresses the challenges organizations face when deploying LLM workflows without adequate monitoring. The session will focus on monitoring techniques for LLM applications, including token usage and response latency, using AWS Bedrock and other commercial LLM stacks, without requiring instrumentation. Groundcover offers an AWS-native solution, providing complete Kubernetes stack visibility with eBPF instrumentation, allowing teams to reduce costs with predictable pricing and troubleshoot infrastructure issues efficiently. The platform integrates with AWS CloudWatch and is compatible with various Kubernetes and Linux distributions, ensuring comprehensive observability across applications and infrastructure.

Groundcover seeks to streamline the migration from legacy observability vendors by minimizing user effort, particularly in mapping and validating metrics across various resources. To address challenges such as closing gaps in metric collection and ensuring comprehensive mapping, Groundcover adheres to the Prometheus convention with added prefixes for its metrics. A significant technical hurdle involved transitioning to cAdvisor for container metrics, which initially posed performance issues due to frequent file operations. By creatively using the Go compiler directive to modify cAdvisor's private function pointer at runtime, Groundcover improved performance, achieving a 90% reduction in file operations while benefiting from cAdvisor's extensive coverage. The company also automates the translation of legacy queries and metrics into their format, simplifying transitions without manual intervention.

Migrating dashboards in observability platforms is a complex process due to numerous nuances and configurations, akin to rearranging the layout and furniture of a house to fit a new space. This process involves translating metrics, queries, and layouts accurately to ensure functionality and maintain the user's preferences, which can be challenging without careful planning and expertise. Groundcover addresses these challenges by making opinionated, deterministic decisions to facilitate seamless translations of other vendors' dashboards, such as adopting a 24-column layout for easier coordinate and width translation. Particular attention is given to migrating variables, requiring reverse engineering of value calculations to prevent discrepancies. The goal is to provide a quick and painless migration experience that enables users to enjoy their new platform without dwelling on the complexities involved in the transition.

Kubernetes Secrets are a crucial component for securely managing sensitive information like passwords, tokens, and certificates within a Kubernetes cluster, helping to simplify tasks that require authentication, authorization, or encryption by storing data as key-value pairs in Etcd. Kubernetes Secrets can be categorized into types such as Opaque, Basic Authentication, TLS, Registry, and Bootstrap Token, each serving specific use cases like HTTP authentication or traffic encryption. Despite their utility, Kubernetes Secrets face limitations, including the lack of default encryption and the complexity of access control, prompting some administrators to use external solutions like HashiCorp Vault for enhanced security features such as dynamic Secrets generation and encryption. Best practices for managing Kubernetes Secrets include enabling encryption in Etcd, choosing the appropriate type of Secret based on use case, and regularly updating and removing outdated Secrets, while monitoring and alerting can be achieved through Kubernetes's audit logging feature. Groundcover provides visibility into Kubernetes clusters to assist in troubleshooting and efficiently managing Secrets by tracking metrics and performance trends, ensuring that workloads can access the necessary Secrets securely.

Amir Sheffer, a Product Manager, discusses the complexities of data querying as groundcover develops a migration tool from legacy vendors to its platform. The effort involved revisiting their querying framework to handle every edge case and translate queries from other observability vendors into groundcover's languages. Utilizing ClickHouse for logs, traces, and events, and VictoriaMetrics for metrics, they settled on MetricsQL for metrics querying due to its power and expressiveness. They introduced a MetricsQL query builder that seamlessly switches between builder and code modes to enhance user experience. For logs, traces, and events, they created a middleware query language inspired by Victoria's LogsQL to optimize both simple and complex queries, ensuring the entire platform speaks the same language. The development process involved extensive optimization and deep-dive sessions, resulting in a robust framework that serves 95% of use cases while providing flexibility for the remaining 5%. As they continue to expand the framework's reach into areas like RUM session analytics and security use cases, they acknowledge that data querying remains an unsolved problem, but believe they have crafted a strong solution.

Groundcover introduces a new standard for integration management by enhancing observability platforms with streamlined configurations and comprehensive data correlation. By focusing on empowering customers to manage their ecosystems, the platform simplifies the integration setup process, allowing users to add, duplicate, and manage configurations directly from a SaaS interface. It supports Infrastructure-as-Code management through Terraform and offers the flexibility to pause and resume integrations based on specific needs, such as temporary testing environments. This approach aims to provide full visibility into performance and the ability to set up monitors and dashboards, facilitating dynamic control over the environment in line with evolving requirements. This initiative marks the beginning of a journey to reshape integration management, reflecting a commitment to data-driven leadership and innovative solutions for scaling startups.

Groundcover has launched an automated migration experience for transitioning from other vendors, beginning with Datadog, to its own observability platform. This move comes after much internal debate, with the company ultimately deciding that the consolidation and improvement of observability tools are crucial in an era where AI rapidly evolves software. The migration process not only transfers assets but also aims to enhance existing systems by adopting a modern, open-source, and cost-efficient ecosystem, leveraging technologies like eBPF. Groundcover's approach seeks to address issues such as the escalating costs and vendor lock-in associated with traditional observability solutions, offering a path to seamlessly adopt updated standards without the constraints of legacy systems. This development is a strategic attempt to redefine observability practices by ensuring all data is centralized and accessible without proprietary limitations, thereby enabling future scalability and adaptability.

Groundcover, a company specializing in observability solutions, has developed an AI-powered migration tool designed to automate the transition from legacy observability vendors like Datadog to their platform. This tool addresses the significant challenges and costs associated with migrating enterprise systems, which often involve extensive manual processes, platform differences, and integration complexities. By offering a self-service, automated solution, Groundcover aims to eliminate the traditional barriers to switching vendors, such as high costs and long migration times, thereby providing a cost-effective alternative to expensive legacy systems. The company plans to showcase this tool at industry events, emphasizing its potential to disrupt the observability market by reducing vendor lock-in and enabling enterprises to adopt more competitive pricing models without compromising on data visibility and operational efficiency.

Groundcover has introduced a new Migrations feature designed to ease the transition from legacy observability systems, focusing on transparency, clarity, and control to instill user confidence. This feature employs preview modes to prevent surprises and insights panels that streamline decision-making, while a modern visual design makes complex steps more approachable. Initially starting with a linear wizard flow, the design evolved to allow non-linear progress, recognizing that migrations can be complex and require flexibility. The tool accommodates multi-threaded workflows with saved states and visual indicators, acknowledging that migrations often involve multiple stakeholders and extended discussions. Instead of a binary progress state, a battery-style progress graph provides a more nuanced view of migration status, emphasizing quality over quantity. Groundcover's approach is innovative, creating a fresh design system tailored to current team workflows rather than replicating outdated patterns. Overall, the Migrations tool aims to ensure clarity and safety throughout the migration process, aligning with the fast-paced and dynamic nature of the cloud-native world.