Alternatives to Aspecto

Grafana Labs provides an open and composable observability stack built around Grafana, the leading open source technology for dashboards and visualization. Recognized as a 2025 Gartner® Magic Quadrant™ Leader for Observability Platforms and positioned furthest to the right for Completeness of Vision, Grafana Labs supports over 25M users and 5,000+ customers. Grafana Cloud delivers the full power of Grafana’s open and composable observability stack—without the overhead of managing infrastructure. As a fully managed SaaS offering from Grafana Labs, it unifies metrics, logs, and traces in one place, giving engineering teams real-time visibility into systems and applications. Built around the LGTM Stack—Loki for logs, Grafana for visualization, Tempo for traces, and Mimir for metrics—Grafana Cloud provides a scalable foundation for modern observability. With built-in integrations for Kubernetes, cloud services, CI/CD pipelines, and OpenTelemetry, Grafana Cloud accelerates time to value while reducing operational complexity. Grafana Cloud also supports OLAP-style analytics through integrations with data warehouses and analytical engines like BigQuery, ClickHouse, and Druid—enabling multi-dimensional exploration across observability and business data. Teams gain access to powerful features like Adaptive Metrics for cost optimization, incident response workflows, and synthetic monitoring for performance testing—all within a secure, globally distributed platform. Whether you’re modernizing infrastructure, scaling observability, or driving SLO-based performance, Grafana Cloud delivers the insights you need—fast, flexible, and vendor-neutral.

Splunk Observability Cloud serves as an all-encompassing platform for real-time monitoring and observability, aimed at enabling organizations to achieve complete insight into their cloud-native infrastructures, applications, and services. By merging metrics, logs, and traces into a single solution, it delivers uninterrupted end-to-end visibility across intricate architectures. The platform's robust analytics, powered by AI-driven insights and customizable dashboards, empower teams to swiftly pinpoint and address performance challenges, minimize downtime, and enhance system reliability. Supporting a diverse array of integrations, it offers real-time, high-resolution data for proactive monitoring purposes. Consequently, IT and DevOps teams can effectively identify anomalies, optimize performance, and maintain the health and efficiency of both cloud and hybrid environments, ultimately fostering greater operational excellence.

The Dynatrace software intelligence platform revolutionizes the way organizations operate by offering a unique combination of observability, automation, and intelligence all within a single framework. Say goodbye to cumbersome toolkits and embrace a unified platform that enhances automation across your dynamic multicloud environments while facilitating collaboration among various teams. This platform fosters synergy between business, development, and operations through a comprehensive array of tailored use cases centralized in one location. It enables you to effectively manage and integrate even the most intricate multicloud scenarios, boasting seamless compatibility with all leading cloud platforms and technologies. Gain an expansive understanding of your environment that encompasses metrics, logs, and traces, complemented by a detailed topological model that includes distributed tracing, code-level insights, entity relationships, and user experience data—all presented in context. By integrating Dynatrace’s open API into your current ecosystem, you can streamline automation across all aspects, from development and deployment to cloud operations and business workflows, ultimately leading to increased efficiency and innovation. This cohesive approach not only simplifies management but also drives measurable improvements in performance and responsiveness across the board.

Cloud Trace serves as a comprehensive distributed tracing system that gathers latency metrics from applications and presents this data within the Google Cloud Console. This tool enables users to monitor the flow of requests throughout their applications while providing near real-time insights into performance. It automatically evaluates all traces from the application to produce detailed latency reports, helping to identify any performance issues. Additionally, Cloud Trace is capable of capturing traces from various environments, including VMs, containers, and App Engine projects. With Cloud Trace, users can delve into specific latency details for individual requests or review the cumulative latency across the entire application. The platform offers a range of tools and filters to facilitate the swift detection of bottlenecks and their underlying causes. This system is built upon the same principles that Google employs to ensure its services operate seamlessly at a massive scale, reflecting a robust and reliable solution for performance monitoring. As such, it becomes an essential resource for developers aiming to optimize their applications effectively.

Google Cloud Observability is designed to give you full visibility into the health and performance of your applications. Through the collection of key telemetry data, such as metrics, logs, and traces, the platform empowers you to proactively detect and address issues, keeping your applications reliable and available. With tools for monitoring, troubleshooting, and debugging, Google Cloud's observability services make it easier to analyze complex, distributed systems and respond to unexpected changes efficiently. The ability to view performance patterns and gain actionable insights helps you optimize your strategies and maintain seamless operations across your environment.

Built on the open-source framework OpenTelemetry, TelemetryHub is the ultimate observability guide, providing data in a single pane of glass for all logs, metrics, and tracing data. A simple, reliable full-stack application monitoring tool that visualizes your complex telemetry data in a consumable format with no propriety configuration or customizations required. TelemetryHub is an easy-to-use and affordable full-stack observability solution provided by Scout APM, an established Application Performance Monitoring tool.

You can innovate faster in the cloud, improve user experience and future-proof applications. Splunk is designed for cloud-native enterprises and helps you solve current problems. Splunk helps you detect any problem before it becomes a customer problem. Our AI-driven Directed Problemshooting reduces MTTR. Flexible, open-source instrumentation eliminates lock-in. Optimize performance by seeing all of your application and using AI-driven analytics. You must observe everything in order to deliver an excellent end-user experience. NoSample™, full-fidelity trace ingestion allows you to leverage all your trace data and identify any anomalies. Directed Troubleshooting reduces MTTR to quickly identify service dependencies, correlations with the underlying infrastructure, and root-cause errors mapping. You can break down and examine any transaction by any dimension or metric. You can quickly and easily see how your application behaves in different regions, hosts or versions.

Observability platforms that utilize distributed tracing, like Jaeger, play a crucial role in the functioning of contemporary software applications designed with a microservices architecture. By tracking the movement of requests and data through a distributed system, Jaeger provides visibility into how these requests interact with various services, which can often lead to delays or errors. This platform adeptly links these different elements, enabling users to pinpoint performance issues, diagnose errors, and enhance the overall reliability of applications. Furthermore, Jaeger stands out as a fully open source solution that is designed to be cloud-native and capable of scaling indefinitely. Its ability to provide deep insights into complex systems makes it an invaluable tool for developers aiming to optimize application performance.

Pydantic Logfire serves as an observability solution aimed at enhancing the monitoring of Python applications by converting logs into practical insights. It offers valuable performance metrics, tracing capabilities, and a comprehensive view of application dynamics, which encompasses request headers, bodies, and detailed execution traces. Built upon OpenTelemetry, Pydantic Logfire seamlessly integrates with widely-used libraries, ensuring user-friendliness while maintaining the adaptability of OpenTelemetry’s functionalities. Developers can enrich their applications with structured data and easily queryable Python objects, allowing them to obtain real-time insights through a variety of visualizations, dashboards, and alert systems. In addition, Logfire facilitates manual tracing, context logging, and exception handling, presenting a contemporary logging framework. This tool is specifically designed for developers in search of a streamlined and efficient observability solution, boasting ready-to-use integrations and user-centric features. Its flexibility and comprehensive capabilities make it a valuable asset for anyone looking to improve their application's monitoring strategy.

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.

Gain comprehensive insight into your cloud-native and distributed applications, encompassing everything from microservices to serverless setups, allowing for swift identification and resolution of underlying issues. Effortlessly integrate Application Performance Management (APM) to automatically detect anomalies, visualize service dependencies, and streamline the investigation of outliers and unusual behaviors. Enhance your application code with robust support for widely-used programming languages, OpenTelemetry, and distributed tracing methodologies. Recognize performance bottlenecks through automated, curated visual representations of all dependencies, which include cloud services, messaging systems, data storage, and third-party services along with their performance metrics. Investigate anomalies in detail, diving into transaction specifics and various metrics for a more profound analysis of your application’s performance. By employing these strategies, you can ensure that your services run optimally and deliver a superior user experience.

OpenTelemetry provides high-quality, widely accessible, and portable telemetry for enhanced observability. It consists of a suite of tools, APIs, and SDKs designed to help you instrument, generate, collect, and export telemetry data, including metrics, logs, and traces, which are essential for evaluating your software's performance and behavior. This framework is available in multiple programming languages, making it versatile and suitable for diverse applications. You can effortlessly create and gather telemetry data from your software and services, subsequently forwarding it to various analytical tools for deeper insights. OpenTelemetry seamlessly integrates with well-known libraries and frameworks like Spring, ASP.NET Core, and Express, among others. The process of installation and integration is streamlined, often requiring just a few lines of code to get started. As a completely free and open-source solution, OpenTelemetry enjoys widespread adoption and support from major players in the observability industry, ensuring a robust community and continual improvements. This makes it an appealing choice for developers seeking to enhance their software monitoring capabilities.

Maximizing your application's performance is a breeze with the FREE trial of Prefix, which incorporates OpenTelemetry. This state-of-the-art open-source observability protocol allows OTel Prefix to enhance application development through seamless ingestion of universal telemetry data, unparalleled observability, and extensive language support. By empowering developers with the capabilities of OpenTelemetry, OTel Prefix propels performance optimization efforts for your entire DevOps team. With exceptional visibility into user environments, new technologies, frameworks, and architectures, OTel Prefix streamlines every phase of code development, app creation, and ongoing performance improvements. Featuring Summary Dashboards, integrated logs, distributed tracing, intelligent suggestions, and the convenient ability to navigate between logs and traces, Prefix equips developers with robust APM tools that can significantly enhance their workflow. As such, utilizing OTel Prefix can lead to not only improved performance but also a more efficient development process overall.

Open source continuous profiling allows you to identify and resolve your most critical performance challenges across code, infrastructure, and CI/CD pipelines. It offers the ability to tag data based on dimensions that are significant to your organization. This solution facilitates the economical and efficient storage of vast amounts of high cardinality profiling data. With FlameQL, users can execute custom queries to swiftly select and aggregate profiles, making analysis straightforward and efficient. You can thoroughly examine application performance profiles using our extensive suite of profiling tools. Gain insights into CPU and memory resource utilization at any moment, enabling you to detect performance issues before your customers notice them. The platform also consolidates profiles from various external profiling tools into a single centralized repository for easier management. Moreover, by linking to your OpenTelemetry tracing data, you can obtain request-specific or span-specific profiles, which significantly enrich other observability data such as traces and logs, ensuring a comprehensive understanding of application performance. This holistic approach fosters proactive monitoring and enhances overall system reliability.

Phoenix serves as a comprehensive open-source observability toolkit tailored for experimentation, evaluation, and troubleshooting purposes. It empowers AI engineers and data scientists to swiftly visualize their datasets, assess performance metrics, identify problems, and export relevant data for enhancements. Developed by Arize AI, the creators of a leading AI observability platform, alongside a dedicated group of core contributors, Phoenix is compatible with OpenTelemetry and OpenInference instrumentation standards. The primary package is known as arize-phoenix, and several auxiliary packages cater to specialized applications. Furthermore, our semantic layer enhances LLM telemetry within OpenTelemetry, facilitating the automatic instrumentation of widely-used packages. This versatile library supports tracing for AI applications, allowing for both manual instrumentation and seamless integrations with tools like LlamaIndex, Langchain, and OpenAI. By employing LLM tracing, Phoenix meticulously logs the routes taken by requests as they navigate through various stages or components of an LLM application, thus providing a clearer understanding of system performance and potential bottlenecks. Ultimately, Phoenix aims to streamline the development process, enabling users to maximize the efficiency and reliability of their AI solutions.

Fluent Bit is capable of reading data from both local files and network devices, while also extracting metrics in the Prometheus format from your server environment. It automatically tags all events to facilitate filtering, routing, parsing, modification, and output rules effectively. With its built-in reliability features, you can rest assured that in the event of a network or server failure, you can seamlessly resume operations without any risk of losing data. Rather than simply acting as a direct substitute, Fluent Bit significantly enhances your observability framework by optimizing your current logging infrastructure and streamlining the processing of metrics and traces. Additionally, it adheres to a vendor-neutral philosophy, allowing for smooth integration with various ecosystems, including Prometheus and OpenTelemetry. Highly regarded by prominent cloud service providers, financial institutions, and businesses requiring a robust telemetry agent, Fluent Bit adeptly handles a variety of data formats and sources while ensuring excellent performance and reliability. This positions it as a versatile solution that can adapt to the evolving needs of modern data-driven environments.

A specialized application performance monitoring tool tailored for distributed systems, particularly optimized for microservices, cloud-native environments, and containerized architectures like Kubernetes. One SkyWalking cluster has the capacity to collect and analyze over 100 billion pieces of telemetry data. It boasts capabilities for log formatting, metric extraction, and the implementation of diverse sampling policies via a high-performance script pipeline. Additionally, it allows for the configuration of alarm rules that can be service-centric, deployment-centric, or API-centric. The tool also has the functionality to forward alarms and all telemetry data to third-party services. Furthermore, it is compatible with various metrics, traces, and logs from established ecosystems, including Zipkin, OpenTelemetry, Prometheus, Zabbix, and Fluentd, ensuring seamless integration and comprehensive monitoring across different platforms. This adaptability makes it an essential tool for organizations looking to optimize their distributed systems effectively.

SigNoz serves as an open-source alternative to Datadog and New Relic, providing a comprehensive solution for all your observability requirements. This all-in-one platform encompasses APM, logs, metrics, exceptions, alerts, and customizable dashboards, all enhanced by an advanced query builder. With SigNoz, there's no need to juggle multiple tools for monitoring traces, metrics, and logs. It comes equipped with impressive pre-built charts and a robust query builder that allows you to explore your data in depth. By adopting an open-source standard, users can avoid vendor lock-in and enjoy greater flexibility. You can utilize OpenTelemetry's auto-instrumentation libraries, enabling you to begin with minimal to no coding changes. OpenTelemetry stands out as a comprehensive solution for all telemetry requirements, establishing a unified standard for telemetry signals that boosts productivity and ensures consistency among teams. Users can compose queries across all telemetry signals, perform aggregates, and implement filters and formulas to gain deeper insights from their information. SigNoz leverages ClickHouse, a high-performance open-source distributed columnar database, which ensures that data ingestion and aggregation processes are remarkably fast. This makes it an ideal choice for teams looking to enhance their observability practices without compromising on performance.

Application Performance Management Get complete visibility into the health and performance of applications. Detect and resolve performance issues no matter where they occur in the entire stack. No sampling. No blindspots. Log Analytics Search and analyze event data across your applications, infrastructure, security, or business without worrying about indexing, data tiers, retention policies, or cost. Keep all log data always hot. Infrastructure Monitoring Capture metrics across your infrastructure – cloud, Kubernetes, serverless, applications or from over 400 pre-built integrations. Visualize the entire stack and troubleshoot performance issues in real-time. O11y AI Investigate and resolve incidents faster with O11y Investigator. Use natural language to explore observability data with O11y Copilot, generate Regular Expressions effortlessly with O11y Regex, and obtain precise answers with O11y GPT. Observe for Snowflake Comprehensive observability into Snowflake workloads. Optimize performance and resource utilization. Deliver secure and compliant operations.

Dash0 serves as a comprehensive observability platform rooted in OpenTelemetry, amalgamating metrics, logs, traces, and resources into a single, user-friendly interface that facilitates swift and context-aware monitoring while avoiding vendor lock-in. It consolidates metrics from Prometheus and OpenTelemetry, offering robust filtering options for high-cardinality attributes, alongside heatmap drilldowns and intricate trace visualizations to help identify errors and bottlenecks immediately. Users can take advantage of fully customizable dashboards powered by Perses, featuring code-based configuration and the ability to import from Grafana, in addition to smooth integration with pre-established alerts, checks, and PromQL queries. The platform's AI-driven tools, including Log AI for automated severity inference and pattern extraction, enhance telemetry data seamlessly, allowing users to benefit from sophisticated analytics without noticing the underlying AI processes. These artificial intelligence features facilitate log classification, grouping, inferred severity tagging, and efficient triage workflows using the SIFT framework, ultimately improving the overall monitoring experience. Additionally, Dash0 empowers teams to respond proactively to system issues, ensuring optimal performance and reliability across their applications.

Enhanced high-definition APM visibility through real user monitoring, synthetic monitoring, and OpenTelemetry offers a solution that is scalable, user-friendly, and simplifies the integration of insights from end users, applications, networks, and the cloud-native space. The rise of microservices within containerized environments on dynamic cloud infrastructures has resulted in a highly transient and distributed landscape at an unprecedented scale. Traditional methods of enhancing APM, which rely on sampled transactions, partial traces, and aggregate metrics, have become ineffective, as legacy APM solutions struggle to identify the reasons behind slow or stalling critical business applications. The Riverbed platform provides cohesive visibility across the contemporary application landscape, ensuring ease of deployment and management, while facilitating quicker resolution of even the most challenging performance issues. Riverbed APM is thoroughly designed for the cloud-native environment, offering extensive monitoring and observability for transactions that operate on the latest cloud and application infrastructures, ultimately enhancing operational efficiency and user experience. This comprehensive approach not only addresses current performance challenges but also positions organizations to adapt to future technological advancements seamlessly.

Leverage the most extensively utilized observability platform, founded on the reliable Elastic Stack (commonly referred to as the ELK Stack), to integrate disparate data sources, providing cohesive visibility and actionable insights. To truly monitor and extract insights from your distributed systems, it is essential to consolidate all your observability data within a single framework. Eliminate data silos by merging application, infrastructure, and user information into a holistic solution that facilitates comprehensive observability and alerting. By integrating limitless telemetry data collection with search-driven problem-solving capabilities, you can achieve superior operational and business outcomes. Unify your data silos by assimilating all telemetry data, including metrics, logs, and traces, from any source into a platform that is open, extensible, and scalable. Enhance the speed of problem resolution through automatic anomaly detection that leverages machine learning and sophisticated data analytics, ensuring you stay ahead in today's fast-paced environment. This integrated approach not only streamlines processes but also empowers teams to make informed decisions swiftly.

Apica offers a unified platform for efficient data management, addressing complexity and cost challenges. The Apica Ascent platform enables users to collect, control, store, and observe data while swiftly identifying and resolving performance issues. Key features include: *Real-time telemetry data analysis *Automated root cause analysis using machine learning *Fleet tool for automated agent management *Flow tool for AI/ML-powered pipeline optimization *Store for unlimited, cost-effective data storage *Observe for modern observability management, including MELT data handling and dashboard creation This comprehensive solution streamlines troubleshooting in complex distributed systems and integrates synthetic and real data seamlessly

It aids in collecting timing information essential for diagnosing latency issues within service architectures. Its functionalities encompass both the gathering and retrieval of this data. When you have a trace ID from a log, you can easily navigate directly to it. If you don't have a trace ID, queries can be made using various parameters such as service names, operation titles, tags, and duration. Additionally, notable data is summarized, including the proportion of time spent on each service and the success or failure of operations. The Zipkin user interface also features a dependency diagram that illustrates the volume of traced requests processed by each application. This visualization can be instrumental in recognizing overall patterns, including error trajectories and interactions with outdated services. Overall, this tool not only simplifies the troubleshooting process but also enhances the understanding of service interactions within complex architectures.

Bindplane is an advanced telemetry pipeline solution based on OpenTelemetry, designed to streamline observability by centralizing the collection, processing, and routing of critical data. It supports a variety of environments such as Linux, Windows, and Kubernetes, making it easier for DevOps teams to manage telemetry at scale. Bindplane reduces log volume by 40%, enhancing cost efficiency and improving data quality. It also offers intelligent processing capabilities, data encryption, and compliance features, ensuring secure and efficient data management. With a no-code interface, the platform provides quick onboarding and intuitive controls for teams to leverage advanced observability tools.

Eliminate delays, pinpoint inefficiencies, and troubleshoot problems effectively. Become a part of a swiftly growing network of global businesses that are realizing up to 20 times the value and return on investment by utilizing KloudMate, far exceeding other observability platforms. Effortlessly track essential metrics, relationships, and identify irregularities through alerts and tracking issues. Swiftly find critical 'break-points' in your application development process to address problems proactively. Examine service maps for each component within your application while revealing complex connections and dependencies. Monitor every request and operation to gain comprehensive insights into execution pathways and performance indicators. Regardless of whether you are operating in a multi-cloud, hybrid, or private environment, take advantage of consolidated Infrastructure monitoring features to assess metrics and extract valuable insights. Enhance your debugging accuracy and speed with a holistic view of your system, ensuring that you can detect and remedy issues more quickly. This approach allows your team to maintain high performance and reliability in your applications.

OpenObserve is a robust open-source observability platform designed for managing logs, metrics, and traces, focusing on exceptional performance, scalability, and significantly reduced costs. It enables observability at a petabyte scale by incorporating features like columnar storage data compression and the flexibility of “bring your own bucket” storage options, including local disks and cloud services such as S3, GCS, and Azure Blob. Developed in Rust, it utilizes the DataFusion query engine for direct querying of Parquet files, and it boasts a stateless, horizontally scalable framework that employs caching strategies for both results and disk to ensure rapid performance even during peak loads. By adhering to open standards, including compatibility with OpenTelemetry and vendor-neutral APIs, OpenObserve seamlessly integrates into pre-existing monitoring and logging ecosystems. Its essential components encompass logs, metrics, traces, frontend monitoring, pipelines, alerts, and comprehensive dashboards for visualizations. Ultimately, OpenObserve empowers organizations to achieve efficient and cost-effective observability solutions in their operations.

TraceRoot.AI serves as an open-source, AI-driven observability and debugging platform that aims to assist engineering teams in swiftly addressing production challenges. By merging telemetry data into a unified correlated execution tree, it offers essential causal insights into failures. AI agents leverage this structured representation to summarize problems, identify probable root causes, and even propose actionable solutions or generate GitHub issues and pull requests. Users can engage in interactive trace exploration, featuring zoomable log clusters and detailed views on spans and latency, complemented by insights linked to the code itself. Additionally, lightweight SDKs for Python and TypeScript facilitate effortless instrumentation via OpenTelemetry, accommodating both self-hosted and cloud-based deployments. A key aspect of the platform is its human-in-the-loop interaction, which allows developers to influence the reasoning process by selecting relevant spans or logs, enabling them to validate the agent's reasoning with traceable context. This collaborative approach not only enhances debugging efficiency but also empowers teams with greater control over the issue resolution process.

Elevate your log management with Honeycomb, a platform designed specifically for contemporary development teams aiming to gain insights into application performance while enhancing log management capabilities. With Honeycomb’s rapid query functionality, you can uncover hidden issues across your system’s logs, metrics, and traces, utilizing interactive charts that provide an in-depth analysis of raw data that boasts high cardinality. You can set up Service Level Objectives (SLOs) that reflect user priorities, which helps in reducing unnecessary alerts and allows you to focus on what truly matters. By minimizing on-call responsibilities and speeding up code deployment, you can ensure customer satisfaction remains high. Identify the root causes of performance issues, optimize your code efficiently, and view your production environment in high resolution. Our SLOs will alert you when customers experience difficulties, enabling you to swiftly investigate the underlying problems—all from a single interface. Additionally, the Query Builder empowers you to dissect your data effortlessly, allowing you to visualize behavioral trends for both individual users and services, organized by various dimensions for enhanced analytical insights. This comprehensive approach ensures that your team can respond proactively to performance challenges while refining the overall user experience.

Kloudfuse is an observability platform powered by AI that efficiently scales while integrating various data sources, including metrics, logs, traces, events, and monitoring of digital experiences into a cohesive observability data lake. With support for more than 700 integrations, it facilitates seamless incorporation of both agent-based and open-source data without requiring any re-instrumentation, and it accommodates open query languages such as PromQL, LogQL, TraceQL, GraphQL, and SQL, while also allowing for the creation of custom workflows through notifications and webhooks. Organizations can easily deploy Kloudfuse within their Virtual Private Cloud (VPC) through a straightforward single-command installation and manage operations centrally using a control plane. The platform automatically collects and indexes telemetry data with smart facets, which helps deliver rapid search capabilities, context-aware alerts powered by machine learning, and service level objectives (SLOs) with minimized false positives. Users benefit from comprehensive visibility across the entire stack, enabling them to trace issues from user experience metrics and session replays all the way down to backend profiling, traces, and metrics, which makes troubleshooting more efficient. This holistic approach to observability ensures that teams can quickly identify and resolve code-level issues while maintaining a strong focus on enhancing user experience.

Tracing Analysis offers a comprehensive set of tools designed to assist developers in pinpointing performance issues within distributed applications. This capability enhances the efficiency of both development and troubleshooting processes for applications utilizing microservices architecture. The tools available facilitate the mapping of traces, provide insights into trace topologies, analyze dependencies among applications, and quantify request volumes. To access Tracing Analysis, activating the Log Service is necessary; however, there are no charges for the resources used by the Log Service specifically for Tracing Analysis. This streamlines the troubleshooting process for distributed applications, eliminating the need to log into each individual machine to gather logs. Additionally, it enables the integration of open-source SDKs to define tracking points, including those compatible with Zipkin, Jaeger, and OpenTracing. Furthermore, Tracing Analysis employs a pay-as-you-go billing model, ensuring that costs are only incurred as services are utilized. Ultimately, these tools empower developers to enhance application performance effectively.

Our ready-to-use solution empowers you to keep an eye on your cluster's health and resolve problems more swiftly with intuitive dashboards that function seamlessly. Coupled with transparent and budget-friendly pricing, initiating your journey is a breeze. ContainIQ operates three agents within your cluster: one single replica deployment that gathers metrics and events from the Kubernetes API, along with two daemon sets—one dedicated to capturing latency data for every pod on the node and the other focused on logging for all pods and containers. You can monitor latency metrics by microservice and path, including p95, p99, average response times, and requests per second (RPS). The system works immediately without the need for additional application packages or middleware. Set alerts to notify you of significant changes and utilize search functionality to filter by date ranges while observing data trends over time. You can see all incoming and outgoing requests along with their associated metadata. Additionally, visualize P99, P95, average latency, and error rates over time for each specific URL path, and correlate logs for a particular trace, which is invaluable for troubleshooting when issues occur. This comprehensive approach ensures you have all the tools needed to maintain optimal performance and swiftly diagnose any challenges that arise.

Uptrace is an observability platform built on OpenTelemetry that enables users to track, comprehend, and enhance intricate distributed systems effectively. With a single, streamlined dashboard, you can oversee your entire application stack efficiently. This setup provides a swift view of all services, hosts, and systems in one place. The distributed tracing feature allows you to follow the journey of a request as it flows through various services and components, highlighting the timing of each operation along with any logs and errors that arise in real-time. Through metrics, you can swiftly gauge, visualize, and monitor a variety of operations using tools such as percentiles, heatmaps, and histograms. By receiving alerts when your application experiences downtime or when a performance issue is detected, you can respond to incidents more promptly. Moreover, the platform allows you to monitor all aspects—spans, logs, errors, and metrics—using a unified query language, simplifying the observability process further. This comprehensive approach ensures that you have all the necessary insights to maintain optimal performance in your distributed systems.

Middleware Observability & management software for Messaging, event processing, and Streaming Across Hybrid Clouds (MESH). - 360 degree situational awareness® with complete observability of Integration MESH - Manage configuration, administration and deployment in a secure manner and automate them. - Track and trace transactions, messages, and flows - Collect data, monitor performance, and benchmark it meshIQ provides granular controls for managing configurations in the MESH, reducing downtime and allowing quick recovery after outages. It allows you to search, browse, track and trace messages in order to detect bottlenecks, speed up root cause analysis, and detect bottlenecks. Unlocks integration blackbox for visibility across MESH infrastructure in order to visualize, analyse, report and predict. Delivers the capability to trigger automated action based on predefined criteria or intelligent AI/ML actions.

Improving business outcomes involves making it easier to spot and address high-priority incidents. The WatchTower Platform serves as a comprehensive observability tool that streamlines incident resolution specifically within mainframe environments by effectively integrating and correlating events, data flows, and metrics across various IT silos. It provides a cohesive and intuitive interface for operations teams, allowing them to optimize their workflows. Leveraging established AIOps solutions, WatchTower is adept at detecting potential problems at an early stage, which aids in proactive mitigation. Additionally, it utilizes OpenTelemetry to transmit mainframe data and insights to observability tools, allowing enterprise SREs to pinpoint bottlenecks and improve operational effectiveness. By enhancing alerts with relevant context, WatchTower eliminates the necessity for logging into multiple tools to gather essential information. Its workflows expedite the processes of problem identification, investigation, and incident resolution, while also simplifying the handover and escalation of issues. With such capabilities, WatchTower not only enhances incident management but also empowers teams to proactively maintain high service availability.

Utilize the Oracle Cloud Observability and Management Platform to oversee, evaluate, and regulate multi-cloud applications and infrastructure with comprehensive visibility, integrated analytics, and automated solutions. Achieve total insight via infrastructure tracking, real user experience assessments, synthetic monitoring, and distributed tracing technologies. Expedite issue identification and resolution by leveraging data from diverse sources with user-friendly, interactive dashboards. Implement unified monitoring, capacity planning, and database management functionalities for both on-premises and cloud-based databases. Effectively deploy and oversee Oracle Cloud resources through Terraform-driven automation while managing data transfers seamlessly. Attain thorough application performance insights through real user experiences, synthetic observations, and distributed tracing methods. Streamlined database monitoring and administration capabilities enhance efficiency for both on-premises and cloud databases. Additionally, quickly analyze log information, troubleshoot challenges, and set up alerts using customizable triggers for proactive management and response. This comprehensive approach ensures that organizations can maintain optimal performance across all their cloud environments.

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.

Establish metrics and implement alerts for operational tasks while identifying and prioritizing the underlying causes for development issues. Complete the feedback loop within the DevOps framework. Set up your application to transmit traces seamlessly via Plumbr Agents. Capture comprehensive traces that encompass user interactions across the various microservices on the back end. Enjoy a hassle-free experience with no code modifications or sampling required! Plumbr APM leverages tracing to deliver valuable insights into application performance. With extensive expertise in Application Performance Management (APM) technology, including Java profiling, bytecode instrumentation (BCI), database monitoring, and real user monitoring, Plumbr empowers businesses. By utilizing tools like Java Profiling and BCI, organizations gain essential visibility into traditional Java and .NET enterprise applications, ensuring they can optimize performance effectively. Additionally, leveraging these insights enables proactive measures, leading to improved user satisfaction and operational efficiency.

gProfiler integrates various sampling profilers to create a comprehensive visualization of CPU usage, showcasing stack traces from processes in native applications, including those written in Golang, Java, and Python. Additionally, gProfiler offers the capability to send its findings to the Granulate Performance Studio, which consolidates data from various instances over time, providing an overall perspective on the performance of your entire cluster. To facilitate this result upload, users must first register on the website and obtain a unique token. This process ensures that you can effectively monitor and analyze performance metrics across your systems.

Effortless debugging for microservices allows for immediate identification of the code responsible for service failures, even in cases of elusive errors. Gain insights into each request, outlier, and issue without the need for extra logging or error reproduction efforts. Discover the fundamental causes of every error with comprehensive context derived from logs, metrics, traces, and instances of failed code execution. Benefit from seamless end-to-end tracing supported by automatic instrumentation, enabling a detailed view of logs, metrics, traces, and the history of code execution failures. Experience thorough performance monitoring that aids in swiftly pinpointing and eliminating application bottlenecks. Enjoy real-time topology discovery that provides complete visibility of dependencies across all services involved. Utilize highly adaptable dashboards and notification systems to detect issues before they reach end users. Furthermore, ensure that all failed tests and errors are documented automatically, making it easier to address each failure effectively and facilitating a rapid feedback loop between testing and development teams throughout the entire development process. This approach not only enhances collaboration but also significantly improves overall software quality.

Open source is a passion for engineers. We supercharged the top open-source monitoring tools, including Jaeger, Prometheus and ELK, and combined them into a scalable SaaS platform. You can collect and analyze all your logs, metrics, traces and other data on one platform for end to end monitoring. You can visualize your data using customizable and easy-to-use monitoring dashboards. Logz.io's AI/ML human-coach automatically detects and corrects any errors or exceptions in your logs. Alerting to Slack and PagerDuty, Gmail and other endpoints allows you to quickly respond to new events. Centralize your metrics at any scale on Prometheus-as-a-service. Unified with logs, traces. Just three lines of code are required to add to your Prometheus config file to start forwarding your metrics and data to Logz.io.

ZeroHack TRACE is an advanced cyber threat intelligence framework that utilizes decoy technology along with a variety of sensors to create and evaluate threat data effectively. It provides dynamic, customizable intelligent shifting sensors that can be easily reconfigured and possess self-healing capabilities. Equipped with a specialized deep packet inspection (DPI) engine, TRACE captures real-time data for in-depth user analysis. The processed data from honeynets significantly improves visualization and correlation, thereby empowering analysts to strengthen network security comprehensively. The Dynamic Intelligent Shifting Sensors (DISS) within ZeroHack TRACE enhance security further by periodically altering sensor locations to evade detection by malicious actors. Additionally, ZeroHack TRACE incorporates honeynets tailored to specific IT environments, ensuring optimal functionality. The sensors are designed to self-repair from attacks and automatically update, which drastically reduces the maintenance burden on customers. Each sensor is equipped with a deep packet inspection engine that facilitates real-time data capture, allowing for meticulous network monitoring and rapid threat identification. This innovative framework not only bolsters security measures but also adapts to the ever-evolving landscape of cyber threats.

Powerful features to monitor, debugging, and optimize performance. Lumigo automates distributed tracing and visualizes every transaction. This allows you to see the flow of transactions and identify correlate issues between services. You can easily see the input/output for each service, including third-party services. View the stack trace line by line to see parameters and values. You can see the payload for http and API calls. All this without any code changes Lumigo's Correlation Engine allows you to see only the relevant logs, debugging information and details related to transactions. All transaction metrics, logs, and trace information can be viewed in one place. Start with a lead, and zoom in on the information you are looking for. You can search the data, and not just logs. Integration to your AWS account in one click. Fully-automated distributed traceing with no code changes. Lumigo uses AWS Lambda Layers to facilitate seamless integration.

Enhance both your production and staging environments by integrating logs, metrics, and traces in real-time and on-demand directly from your IDE or command line interface. With Lightrun, you can significantly improve productivity and achieve complete code-level visibility. You can add logs and metrics instantly while services are operational, making it easier to debug complex architectures like monoliths, microservices, Kubernetes, Docker Swarm, ECS, and serverless applications. Quickly insert any missing log lines, instrument necessary metrics, or establish snapshots as needed without the hassle of recreating the production setup or redeploying. When you invoke instrumentation, the resulting data gets sent to your log analysis platform, IDE, or preferred APM tool. This allows for thorough analysis of code behavior to identify bottlenecks and errors without interrupting the running application. You can seamlessly incorporate extensive logs, snapshots, counters, timers, function durations, and much more without risking system stability. This streamlined approach lets you focus on coding rather than getting bogged down in debugging, eliminating the need for constant restarts or redeployments when troubleshooting. Ultimately, this results in a more efficient development workflow, allowing you to maintain momentum on your projects.

Langfuse is a free and open-source LLM engineering platform that helps teams to debug, analyze, and iterate their LLM Applications. Observability: Incorporate Langfuse into your app to start ingesting traces. Langfuse UI : inspect and debug complex logs, user sessions and user sessions Langfuse Prompts: Manage versions, deploy prompts and manage prompts within Langfuse Analytics: Track metrics such as cost, latency and quality (LLM) to gain insights through dashboards & data exports Evals: Calculate and collect scores for your LLM completions Experiments: Track app behavior and test it before deploying new versions Why Langfuse? - Open source - Models and frameworks are agnostic - Built for production - Incrementally adaptable - Start with a single LLM or integration call, then expand to the full tracing for complex chains/agents - Use GET to create downstream use cases and export the data