Best Observability Tools for Grafana

Cloud-based solution for observability that helps businesses manage and track workload and performance through a single dashboard. Monitor all the services you run on your cloud without compromising cost, granularity or scale. Groundcover is a cloud-native APM solution that makes observability easy so you can focus on creating world-class products. Groundcover's proprietary sensor unlocks unprecedented granularity for all your applications. This eliminates the need for costly changes in code and development cycles, ensuring monitoring continuity.

IBM Instana sets the benchmark for incident prevention, offering comprehensive full-stack visibility with one-second precision and a notification time of just three seconds. In the current landscape of rapidly evolving and intricate cloud infrastructures, the financial repercussions of an hour of downtime can soar into the six-figure range or more. Conventional application performance monitoring (APM) tools often fall short, lacking the speed and depth required to effectively address and contextualize technical issues, and they usually necessitate extensive training for super users before they can be utilized effectively. In contrast, IBM Instana Observability transcends the limitations of standard APM tools by making observability accessible to a wider audience, enabling individuals from DevOps, SRE, platform engineering, ITOps, and development teams to obtain the necessary data and context without barriers. The Instana Dynamic APM functions through a specialized agent architecture, utilizing sensors—automated, lightweight programs specifically designed to monitor particular entities and ensure optimal performance. As a result, organizations can respond to incidents proactively and maintain a higher level of service continuity.

InfluxDB is a purpose-built data platform designed to handle all time series data, from users, sensors, applications and infrastructure — seamlessly collecting, storing, visualizing, and turning insight into action. With a library of more than 250 open source Telegraf plugins, importing and monitoring data from any system is easy. InfluxDB empowers developers to build transformative IoT, monitoring and analytics services and applications. InfluxDB’s flexible architecture fits any implementation — whether in the cloud, at the edge or on-premises — and its versatility, accessibility and supporting tools (client libraries, APIs, etc.) make it easy for developers at any level to quickly build applications and services with time series data. Optimized for developer efficiency and productivity, the InfluxDB platform gives builders time to focus on the features and functionalities that give their internal projects value and their applications a competitive edge. To get started, InfluxData offers free training through InfluxDB University.

Edge Delta is a new way to do observability. We are the only provider that processes your data as it's created and gives DevOps, platform engineers and SRE teams the freedom to route it anywhere. As a result, customers can make observability costs predictable, surface the most useful insights, and shape your data however they need. Our primary differentiator is our distributed architecture. We are the only observability provider that pushes data processing upstream to the infrastructure level, enabling users to process their logs and metrics as soon as they’re created at the source. Data processing includes: * Shaping, enriching, and filtering data * Creating log analytics * Distilling metrics libraries into the most useful data * Detecting anomalies and triggering alerts We combine our distributed approach with a column-oriented backend to help users store and analyze massive data volumes without impacting performance or cost. By using Edge Delta, customers can reduce observability costs without sacrificing visibility. Additionally, they can surface insights and trigger alerts before data leaves their environment.

InsightFinder Unified Intelligence Engine platform (UIE) provides human-centered AI solutions to identify root causes of incidents and prevent them from happening. InsightFinder uses patented self-tuning, unsupervised machine learning to continuously learn from logs, traces and triage threads of DevOps Engineers and SREs to identify root causes and predict future incidents. Companies of all sizes have adopted the platform and found that they can predict business-impacting incidents hours ahead of time with clearly identified root causes. You can get a complete overview of your IT Ops environment, including trends and patterns as well as team activities. You can also view calculations that show overall downtime savings, cost-of-labor savings, and the number of incidents solved.

Azure Managed Grafana offers a comprehensive, fully managed platform for monitoring and analytics needs. Backed by Grafana Enterprise, it delivers customizable and extensible data visualizations. Users can swiftly deploy Grafana dashboards with inherent high availability while managing access through Azure's security features. It supports a broad array of data sources, enabling connections to various data repositories both within Azure and beyond. By integrating charts, logs, and alerts, users can achieve a unified overview of their applications and infrastructure. Additionally, it allows for the correlation of data across different datasets, enhancing analysis capabilities. Users can easily share Grafana dashboards with colleagues and external partners, fostering collaboration in monitoring and troubleshooting solutions. This makes Azure Managed Grafana an invaluable tool for teams seeking to improve their operational efficiency and data-driven decision-making.

OpenLIT serves as an observability tool that is fully integrated with OpenTelemetry, specifically tailored for application monitoring. It simplifies the integration of observability into AI projects, requiring only a single line of code for setup. This tool is compatible with leading LLM libraries, such as those from OpenAI and HuggingFace, making its implementation feel both easy and intuitive. Users can monitor LLM and GPU performance, along with associated costs, to optimize efficiency and scalability effectively. The platform streams data for visualization, enabling rapid decision-making and adjustments without compromising application performance. OpenLIT's user interface is designed to provide a clear view of LLM expenses, token usage, performance metrics, and user interactions. Additionally, it facilitates seamless connections to widely-used observability platforms like Datadog and Grafana Cloud for automatic data export. This comprehensive approach ensures that your applications are consistently monitored, allowing for proactive management of resources and performance. With OpenLIT, developers can focus on enhancing their AI models while the tool manages observability seamlessly.

Langtrace is an open-source observability solution designed to gather and evaluate traces and metrics, aiming to enhance your LLM applications. It prioritizes security with its cloud platform being SOC 2 Type II certified, ensuring your data remains highly protected. The tool is compatible with a variety of popular LLMs, frameworks, and vector databases. Additionally, Langtrace offers the option for self-hosting and adheres to the OpenTelemetry standard, allowing traces to be utilized by any observability tool of your preference and thus avoiding vendor lock-in. Gain comprehensive visibility and insights into your complete ML pipeline, whether working with a RAG or a fine-tuned model, as it effectively captures traces and logs across frameworks, vector databases, and LLM requests. Create annotated golden datasets through traced LLM interactions, which can then be leveraged for ongoing testing and improvement of your AI applications. Langtrace comes equipped with heuristic, statistical, and model-based evaluations to facilitate this enhancement process, thereby ensuring that your systems evolve alongside the latest advancements in technology. With its robust features, Langtrace empowers developers to maintain high performance and reliability in their machine learning projects.

Identify and resolve performance issues and errors within your microservices architecture. Establish connections between root causes by analyzing traces, logs, and metrics. Reduce your costs associated with OpenTelemetry traces through Aspecto's integrated remote sampling feature. The way OTel data is visualized plays a crucial role in enhancing your troubleshooting efficiency. Transition seamlessly from a broad overview to intricate details using top-tier visualization tools. Link logs directly to their corresponding traces effortlessly, maintaining context to expedite issue resolution. Utilize filters, free-text searches, and grouping options to navigate your trace data swiftly and accurately locate the source of the problem. Optimize expenses by sampling only essential data, allowing for trace sampling based on programming languages, libraries, specific routes, and error occurrences. Implement data privacy measures to obscure sensitive information within traces, specific routes, or other critical areas. Moreover, integrate your everyday tools with your operational workflow, including logs, error monitoring, and external event APIs, to create a cohesive and efficient system for managing and troubleshooting issues. This holistic approach not only improves visibility but also empowers teams to tackle problems proactively.

Tetragon is an adaptable security observability and runtime enforcement tool designed for Kubernetes, leveraging eBPF to implement policies and filtering that minimize observation overhead while enabling the tracking of any process and real-time policy enforcement. With eBPF technology, Tetragon achieves profound observability with minimal performance impact, effectively reducing risks without the delays associated with user-space processing. Building on Cilium's architecture, Tetragon identifies workload identities, including namespace and pod metadata, offering capabilities that exceed conventional observability methods. It provides a selection of pre-defined policy libraries that facilitate quick deployment and enhance operational insights, streamlining both setup time and complexity when scaling. Furthermore, Tetragon actively prevents harmful actions at the kernel level, effectively closing off opportunities for exploitation while avoiding vulnerabilities related to TOCTOU attack vectors. The entire process of synchronous monitoring, filtering, and enforcement takes place within the kernel through the use of eBPF, ensuring a secure environment for workloads. This integrated approach not only enhances security but also optimizes performance across Kubernetes deployments.

Amazon Managed Grafana is a comprehensive service designed to streamline the visualization and analysis of operational data on a large scale. This platform enables users to establish workspaces, which are isolated Grafana servers that can be automatically provisioned, configured, scaled, and maintained. These dedicated workspaces facilitate the visualization and analysis of operational data sourced from a variety of channels, including AWS services like Amazon CloudWatch, AWS X-Ray, and Amazon Managed Service for Prometheus, as well as external data providers. The service is fully integrated with AWS security features, ensuring adherence to corporate security policies. Furthermore, Amazon Managed Grafana allows for seamless migration from self-hosted Grafana systems, enabling users to keep their existing dashboards and settings intact. It also includes collaborative tools such as live dashboard viewing and modification, version control, and sharing options, which significantly boost team efficiency. Overall, Amazon Managed Grafana stands out by simplifying complex data operations while enhancing collaborative efforts within teams.

Observo AI is an innovative platform tailored for managing large-scale telemetry data within security and DevOps environments. Utilizing advanced machine learning techniques and agentic AI, it automates the optimization of data, allowing companies to handle AI-generated information in a manner that is not only more efficient but also secure and budget-friendly. The platform claims to cut data processing expenses by over 50%, while improving incident response speeds by upwards of 40%. Among its capabilities are smart data deduplication and compression, real-time anomaly detection, and the intelligent routing of data to suitable storage or analytical tools. Additionally, it enhances data streams with contextual insights, which boosts the accuracy of threat detection and helps reduce the occurrence of false positives. Observo AI also features a cloud-based searchable data lake that streamlines data storage and retrieval, making it easier for organizations to access critical information when needed. This comprehensive approach ensures that enterprises can keep pace with the evolving landscape of cybersecurity threats.