Best Observability Tools

New Relic empowers enterprises with intelligent observability solutions, providing unmatched visibility across your tech stack. Our AI-driven, unified data platform centralizes telemetry from front-end to infrastructure, enabling real-time insights and proactive issue resolution. With advanced tools like automated alerts, customizable dashboards, and full-stack analysis, New Relic helps you optimize performance, reduce downtime, and deliver superior digital experiences. Simplifying enterprise-scale observability, New Relic turns your system data into a strategic asset, driving operational efficiency and innovation. Start your observability transformation today.

NetBrain helps IT teams halve MTTR and prevent outages with AI-driven automation. Trusted by 2,500+ enterprises worldwide, our no-code, intent-based platform turns manual network operations into intelligent automation, keeping networks running smoothly and efficiently.​ Top use cases: - Automated Troubleshooting - Automated Change Management - Network AIOps - Network Assessment - Network Visibility - Network Observability - Network Security

Langfuse is an open source LLM engineering platform to help teams collaboratively debug, analyze and iterate on their LLM Applications. Observability: Instrument your app and start ingesting traces to Langfuse Langfuse UI: Inspect and debug complex logs and user sessions Prompts: Manage, version and deploy prompts from within Langfuse Analytics: Track metrics (LLM cost, latency, quality) and gain insights from dashboards & data exports Evals: Collect and calculate scores for your LLM completions Experiments: Track and test app behavior before deploying a new version Why Langfuse? - Open source - Model and framework agnostic - Built for production - Incrementally adoptable - start with a single LLM call or integration, then expand to full tracing of complex chains/agents - Use GET API to build downstream use cases and export data

Edge Delta is a new way to do observability that helps developers and operations teams monitor datasets and create telemetry pipelines. We process your log data as it's created and give you the freedom to route it anywhere. 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. 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.

Automatically discover issues, diagnose problems, and improve models with Arize’s machine learning observability platform. Machine learning systems address mission critical needs for businesses and their customers every day, yet often fail to perform in the real world. Arize is an end-to-end observability platform to accelerate detecting and resolving issues for your AI models at large. Seamlessly enable observability for any model, from any platform, in any environment. Lightweight SDKs to send training, validation, and production datasets. Link real-time or delayed ground truth to predictions. Gain foresight and confidence that your models will perform as expected once deployed. Proactively catch any performance degradation, data/prediction drift, and quality issues before they spiral. Reduce the time to resolution (MTTR) for even the most complex models with flexible, easy-to-use tools for root cause analysis.

Rely on the most widely deployed observability platform available, built on the proven Elastic Stack (also known as the ELK Stack) to converge silos, delivering unified visibility and actionable insights. To effectively monitor and gain insights across your distributed systems, you need to have all your observability data in one stack. Break down silos by bringing together the application, infrastructure, and user data into a unified solution for end-to-end observability and alerting. Combine limitless telemetry data collection and search-powered problem resolution in a unified solution for optimal operational and business results. Converge data silos by ingesting all your telemetry data (metrics, logs, and traces) from any source in an open, extensible, and scalable platform. Accelerate problem resolution with automatic anomaly detection powered by machine learning and rich data analytics.

InsightFinder Unified Intelligence Engine (UIE) platform provides human-centered AI solutions for identifying incident root causes, and predicting and preventing production incidents. Powered by patented self-tuning unsupervised machine learning, InsightFinder continuously learns from metric time series, logs, traces, and triage threads from SREs and DevOps Engineers to bubble up root causes and predict incidents from the source. Companies of all sizes have embraced the platform and seen that business-impacting incidents can be predicted hours ahead with clearly pinpointed root causes. Survey a comprehensive overview of your IT Ops ecosystem, including patterns, trends, and team activities. Also view calculations that demonstrate overall downtime savings, cost of labor savings, and number of incidents resolved.

Langtrace is an open source observability tool that collects and analyzes traces and metrics to help you improve your LLM apps. Langtrace ensures the highest level of security. Our cloud platform is SOC 2 Type II certified, ensuring top-tier protection for your data. Supports popular LLMs, frameworks, and vector databases. Langtrace can be self-hosted and supports OpenTelemetry standard traces, which can be ingested by any observability tool of your choice, resulting in no vendor lock-in. Get visibility and insights into your entire ML pipeline, whether it is a RAG or a fine-tuned model with traces and logs that cut across the framework, vectorDB, and LLM requests. Annotate and create golden datasets with traced LLM interactions, and use them to continuously test and enhance your AI applications. Langtrace includes built-in heuristic, statistical, and model-based evaluations to support this process.

Pydantic Logfire is an observability platform designed to simplify monitoring for Python applications by transforming logs into actionable insights. It provides performance insights, tracing, and visibility into application behavior, including request headers, body, and the full trace of execution. Pydantic Logfire integrates with popular libraries and is built on top of OpenTelemetry, making it easier to use while retaining the flexibility of OpenTelemetry's features. Developers can instrument their apps with structured data, and query-ready Python objects, and gain real-time insights through visualizations, dashboards, and alerts. Logfire also supports manual tracing, context logging, and exception capturing, providing a modern logging interface. It is tailored for developers seeking a streamlined, effective observability tool with out-of-the-box integrations and ease of use.

Dash0 is an OpenTelemetry-native observability platform that unifies metrics, logs, traces, and resources into one intuitive interface, enabling fast and context-rich monitoring without vendor lock-in. It centralizes Prometheus and OpenTelemetry metrics, supports powerful filtering of high-cardinality attributes, and provides heatmap drilldowns and detailed trace views to pinpoint errors and bottlenecks in real time. Users benefit from fully customizable dashboards built on Perses, with support for code-based configuration and Grafana import, plus seamless integration with predefined alerts, checks, and PromQL queries. Dash0's AI-enhanced tools, such as Log AI for automated severity inference and pattern extraction, enrich telemetry data without requiring users to even notice that AI is working behind the scenes. These AI capabilities power features like log classification, grouping, inferred severity tagging, and streamlined triage workflows through the SIFT framework.

Foxglove is a visualization, observability, and data management platform purpose-built for robotics and embodied AI development that centralizes and simplifies working with large, multimodal temporal datasets, including time series, sensor logs, imagery, lidar/point clouds, geospatial maps, and more, in a single, integrated workspace. It enables engineers to record, import, organize, stream, and visualize both live and recorded data from robots using intuitive, customizable dashboards with interactive panels for 3D scenes, plots, raw messages, images, and maps, helping users understand how robots sense, think, and act. Foxglove supports real-time connections to systems like ROS and ROS 2 via bridges and web sockets, enables cross-platform workflows (desktop app for Linux, Windows, and macOS), and facilitates rapid analysis, debugging, and performance optimization by synchronizing diverse data sources in time and space.

When organizations invest in an observability platform that unifies data, insights, and actions across IT, they can resolve problems faster, and eliminate data silos, resource-intensive war rooms, and alert fatigue. Riverbed IQ unified observability enables fast, effective decision-making across business and IT, codifying expert troubleshooting knowledge so junior staff can achieve more first-level resolutions, facilitating digital innovation, and continuously improving the digital experience for customers and employees. Broad-based telemetry brings together a unified view of performance and insights, which is the foundation of unified observability upon which all other capabilities are delivered. Riverbed IQ's approach to unified observability begins with our full-fidelity telemetry – across the network and infrastructure and including end-user experience metrics.

Launch production-ready apps with the LMOps stack for monitoring, model management, and more. Replace your OpenAI or other provider APIs with the Portkey endpoint. Manage prompts, engines, parameters, and versions in Portkey. Switch, test, and upgrade models with confidence! View your app performance & user level aggregate metics to optimise usage and API costs Keep your user data secure from attacks and inadvertent exposure. Get proactive alerts when things go bad. A/B test your models in the real world and deploy the best performers. We built apps on top of LLM APIs for the past 2 and a half years and realised that while building a PoC took a weekend, taking it to production & managing it was a pain! We're building Portkey to help you succeed in deploying large language models APIs in your applications. Regardless of you trying Portkey, we're always happy to help!

Middleware Observability & Management Software for Messaging, Event Processing, and Streaming Across Hybrid Cloud (MESH). - Complete observability and monitoring of Integration MESH with 360° Situational Awareness® - Securely manage, and automate configuration, administration, and deployment - Track, trace, and analyze transactions, messages and flows - Collect, monitor, and benchmark MESH performance meshIQ delivers granular access controls to manage configurations across the MESH to reduce downtime and quick recovery from outages. Provides the ability to find, browse, track, and trace messages to detect bottlenecks and speeding up root-cause analysis. Unlocks the integration blackbox to deliver visibility across the MESH infrastructure to visualize, analyze, report, and predict. Delivers the ability to trigger automated actions based on pre-defined criteria or intelligent actions determined by AI/ML.

VictoriaMetrics Anomaly Detection is a service that continuously scans time series stored in VictoriaMetrics and detects unexpected changes within data patterns in real time. It does so by utilizing user-configurable machine learning models. In the dynamic and complex world of system monitoring, VictoriaMetrics Anomaly Detection, a part of our Enterprise offering, is a pivotal tool for achieving advanced observability. It empowers SREs and DevOps teams by automating the intricate task of identifying abnormal behavior in time-series data. It goes beyond traditional threshold-based alerting, utilizing machine learning techniques to detect anomalies and minimize false positives, thus reducing alert fatigue. Providing simplified alerting mechanisms atop unified anomaly scores enables teams to spot and address potential issues faster, ensuring system reliability and operational efficiency.

Enhancing business performance by simplifying the identification and resolution of high-priority incidents. The WatchTower Platform is an observability solution that simplifies incident resolution in mainframe environments by integrating and correlating events, data flows, and metrics across IT silos. It offers a unified, user-friendly experience for operations teams to streamline workflows. Built on familiar AIOps solutions, WatchTower detects potential issues early, facilitating proactive avoidance. It also uses OpenTelemetry to stream mainframe data and insights to observability tools, enabling enterprise SREs to identify bottlenecks and enhance operational efficiency. WatchTower augments alerts with pertinent context, eliminating the need for multiple tool logins to collect critical information. WatchTower workflows expedite problem identification, investigation, and incident resolution, and simplify problem handover and escalation.

​Observo AI is an AI-native data pipeline platform designed to address the challenges of managing vast amounts of telemetry data in security and DevOps operations. By leveraging machine learning and agentic AI, Observo AI automates data optimization, enabling enterprises to process AI-generated data more efficiently, securely, and cost-effectively. It reduces data processing costs by over 50% and accelerates incident response times by more than 40%. Observo AI's features include intelligent data deduplication and compression, real-time anomaly detection, and dynamic data routing to appropriate storage or analysis tools. It also enriches data streams with contextual information to enhance threat detection accuracy while minimizing false positives. Observo AI offers a searchable cloud data lake for efficient data storage and retrieval.