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Sematext Cloud provides all-in-one observability solutions for modern software-based businesses. It provides key insights into both front-end and back-end performance. Sematext includes infrastructure, synthetic monitoring, transaction tracking, log management, and real user & synthetic monitoring. Sematext provides full-stack visibility for businesses by quickly and easily exposing key performance issues through a single Cloud solution or On-Premise.

NeuBird's premier offering, Hawkeye (Agentic AI SRE), is an innovative Site Reliability Engineering platform powered by artificial intelligence that revolutionizes IT operations through the continuous observation of telemetry derived from your entire observability stack, including logs, metrics, traces, alerts, and incident tickets. It enables the detection of problems, thorough root cause analysis, and offers or automates effective solutions in real-time, eliminating the need for manual investigation. Designed specifically for enterprise-scale environments, Hawkeye delivers secure integration with a variety of existing monitoring and incident management systems, such as DataDog, Splunk, PagerDuty, Prometheus, ServiceNow, AWS CloudWatch, Azure Monitor, and several others. By correlating signals from diverse sources and reasoning in a manner similar to a human engineer, it uncovers actionable insights that can significantly decrease the mean time to resolution (MTTR) by nearly 90%. Operating continuously, Hawkeye can be deployed as a Software as a Service (SaaS) or within a customer's Virtual Private Cloud (VPC), equipped with robust enterprise security measures, and provides features like autonomous incident response and advanced pattern recognition, making it a comprehensive solution for modern IT challenges. Additionally, its ability to adapt and learn from ongoing operations ensures that organizations can maintain high availability and performance levels in a rapidly evolving technological landscape.

All data sources, including topology, monitoring, change, and observation tools, are aggregated. BigPanda's Open Box Machine Learning will combine the data into a limited number of actionable insights. This allows incidents to be detected as they occur, before they become outages. Automatically identifying the root cause of problems can speed up incident and outage resolution. BigPanda identifies both root cause changes and infrastructure-related root causes. Rapidly resolve outages and incidents. BigPanda automates the incident response process, including ticketing, notification, tickets, incident triage, and war room creation. Integrating BigPanda and enterprise runbook automation tools will accelerate remediation. Every company's lifeblood is its applications and cloud services. Everyone is affected when there is an outage. BigPanda consolidates AIOps market leadership with $190M in funding and a $1.2B valuation

Epsagon allows teams to instantly visualize, understand, and optimize their microservice architectures. With our unique lightweight auto-instrumentation, gaps in data and manual work associated with other APM solutions are eliminated, providing significant reductions in issue detection, root cause analysis and resolution times. Epsagon can increase development speed and reduce application downtime.

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.

Splunk AppDynamics is a comprehensive observability and security platform designed to optimize hybrid and on-prem applications. Unlike siloed monitoring tools, it connects application performance to measurable business outcomes such as revenue, conversions, and operational efficiency. The solution empowers teams to track critical business transactions like logins, shopping cart activity, and order processing, providing real-time visibility into bottlenecks. With AI-powered anomaly detection and root cause analysis, it ensures that performance issues are identified quickly and accurately. AppDynamics extends beyond performance monitoring by securing applications at runtime, blocking threats, and exposing vulnerabilities before they escalate. Its specialized support for SAP environments enables rapid issue detection, tracing down to ABAP code or database queries. Digital Experience Monitoring adds a customer-focused lens, offering web, mobile, and synthetic insights into user journeys. By combining business performance analytics, runtime security, and full-stack observability, Splunk AppDynamics helps organizations maximize reliability and deliver superior digital experiences.

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.

Deductive AI is an innovative platform that transforms the way organizations address intricate system failures. By seamlessly integrating your entire codebase with telemetry data, which includes metrics, events, logs, and traces, it enables teams to identify the root causes of problems with remarkable speed and accuracy. This platform simplifies the debugging process, significantly minimizing downtime and enhancing overall system dependability. With its ability to integrate with your codebase and existing observability tools, Deductive AI constructs a comprehensive knowledge graph that is driven by a code-aware reasoning engine, effectively diagnosing root issues similar to a seasoned engineer. It rapidly generates a knowledge graph containing millions of nodes, revealing intricate connections between the codebase and telemetry data. Furthermore, it orchestrates numerous specialized AI agents to meticulously search for, uncover, and analyze the subtle indicators of root causes dispersed across all linked sources, ensuring a thorough investigative process. This level of automation not only accelerates troubleshooting but also empowers teams to maintain higher system performance and reliability.

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.

StackPilot is a next-generation incident response solution designed to reduce engineering toil and accelerate bug resolution. Acting as an AI-powered copilot, it plugs into your monitoring and logging ecosystem to immediately act on alerts. When issues occur, StackPilot cross-references code commits, stack traces, and system data to identify root causes with precision. It then auto-generates a pull request containing a recommended fix, saving engineers countless hours of manual debugging. Beyond incident resolution, the platform builds real-time incident timelines and turns troubleshooting steps into standardized runbooks for future use. Setup takes just minutes, requiring only a GitHub and monitoring tool connection. The platform is built with privacy-first principles—your data never leaves your environment and is not used for AI training. Teams using StackPilot benefit from reduced mean time to resolution (MTTR), stronger reliability, and higher developer productivity.

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.

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.

Traversal is an innovative AI-driven Site Reliability Engineering (SRE) solution that functions round the clock, autonomously identifying, addressing, and even preventing production issues. It meticulously analyzes logs, metrics, traces, and your codebase to pinpoint the root causes of errors or delays, quickly highlighting the impacted areas, critical bottleneck services, and potential root causes with relevant evidence in a matter of minutes. Leveraging advancements in causal machine learning, reasoning from large language models, and intelligent AI agents, Traversal proactively resolves problems before alerts are triggered, ensuring seamless operations. Tailored for complex organizations and vital infrastructure, it accommodates diverse data types, supports bring-your-own models, and offers optional on-premises deployment for added flexibility. With its straightforward integration into existing systems requiring only read-only access—without the need for agents, sidecars, or any write operations to production—Traversal guarantees data privacy and control. By effortlessly fitting into your observability framework, it not only accelerates the resolution process but also significantly reduces downtime, further enhancing operational efficiency and reliability. Furthermore, its ability to adapt to various environments makes it a versatile asset for businesses striving for uninterrupted service delivery.

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.

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.

Sift serves as a comprehensive observability platform specifically designed for contemporary, mission-critical hardware systems, equipping engineers with the necessary infrastructure and tools to efficiently ingest, store, normalize, and analyze high-frequency, high-cardinality telemetry and event data sourced from design, validation, manufacturing, and operations, all centralized into a single, coherent source of truth instead of relying on disjointed dashboards and scripts. By bringing various data types together, Sift aligns signals from different subsystems and organizes information to facilitate rapid searches, visual assessments, and traceability, thereby enabling teams to identify anomalies, conduct root-cause analysis, automate validation processes, and troubleshoot hardware with precision in real-time. Additionally, it enhances automated data reviews, allows for no-code visualization and querying of extensive datasets, supports ongoing anomaly detection, and integrates seamlessly with engineering workflows, including CI/CD pipelines and tools, thereby fostering telemetry governance, collaboration, and knowledge capture across previously isolated teams. This holistic approach not only improves operational efficiency but also empowers teams to make informed decisions based on rich, actionable insights derived from their telemetry data.

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.

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.

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.

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.

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.

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.

Functions independently to manage regular alerts and actions, thereby minimizing escalations and mitigating burnout. It intelligently modifies thresholds and dashboards to proactively avert incidents and updates runbooks with each new occurrence. This efficiency can save on-call engineers as much as 20 hours weekly, allowing them to focus on development tasks. It manages all alerts, conducts root cause analysis, resolves incidents, and ensures that the on-call experience is stress-free. By automating root cause analysis and incident response, it can reduce Mean Time to Resolution (MTTR) by up to 80%. With comprehensive incident summaries and hypotheses accessible prior to logging in, users will enjoy quicker response times and significantly enhanced uptime. Getting started is quick and easy with production-ready AI that is secure and adept in utilizing all necessary production tools just like a seasoned software engineer. Additionally, it automatically maps your production environment, comprehends code, and tracks modifications seamlessly without requiring any prior training. This innovative approach not only streamlines operations but also enhances overall productivity and efficiency within the team.

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.

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

VirtualMetric is a comprehensive data monitoring solution that provides organizations with real-time insights into security, network, and server performance. Using its advanced DataStream pipeline, VirtualMetric efficiently collects and processes security logs, reducing the burden on SIEM systems by filtering irrelevant data and enabling faster threat detection. The platform supports a wide range of systems, offering automatic log discovery and transformation across environments. With features like zero data loss and compliance storage, VirtualMetric ensures that organizations can meet security and regulatory requirements while minimizing storage costs and enhancing overall IT operations.

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.

Komodor simplifies the troubleshooting process for Kubernetes, equipping you with all the essential tools to resolve issues confidently. It oversees your entire Kubernetes ecosystem, detects problems, reveals their underlying causes, and provides the necessary context for effective and independent troubleshooting. The platform automatically identifies anomalies, deployment failures, misconfigurations, bottlenecks, and various health-related issues. It enables you to recognize potential problems before they escalate and impact end-users. By utilizing pre-designed playbooks, you can enhance root cause analysis, avoid disruptive escalations, and conserve valuable developer time. Moreover, it offers clear remediation guidance that empowers every team member to act like a seasoned troubleshooting expert, fostering a more resilient operational environment. This proactive approach not only enhances team efficiency but also significantly improves overall system reliability.

Shield34 stands out as the sole web automation framework that ensures complete compatibility with Selenium, allowing users to seamlessly continue utilizing their existing Selenium scripts while also enabling the creation of new ones through the Selenium API. It effectively tackles the notorious issue of flaky tests by implementing self-healing technology, intelligent defenses, error recovery protocols, and dynamic element locators. Furthermore, it offers AI-driven anomaly detection and root cause analysis, which facilitates a swift examination of failed tests to identify what changed and triggered the failure. By eliminating flaky tests, which often present significant challenges, Shield34 incorporates sophisticated defense-and-recovery AI algorithms into each Selenium command, including dynamic element locators, thereby reducing false positives and promoting self-healing alongside maintenance-free testing. Additionally, with its real-time root cause analysis capabilities powered by AI, Shield34 can swiftly identify the underlying reasons for test failures, minimizing the burden of debugging and the effort required to replicate issues. Ultimately, users can relish a more intelligent version of Selenium, as it effortlessly integrates with your existing testing framework while enhancing overall efficiency.

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.

Vision is easy to implement and use, featuring a streamlined architecture that minimizes expenses while delivering action-oriented, instantaneous root cause analysis. Its software-based probes possess unlimited scalability across the network, presenting broadcasters, network operators, and content distributors with a cost-effective solution for achieving direct analytical insights at the critical last mile. By elevating content distribution reliability, Vision allows for the monitoring of more points than ever before in real-time, ensuring an unparalleled level of fault tolerance and redundancy through hot-swap backups, load balancing, and clustering. Built for continuous operation, Vision facilitates comprehensive root cause analysis, capturing video of each incident 24/7 and maintaining a time-correlated trend history. When implemented across the entire network, Vision reveals an accurate perspective on channel delivery all the way to the last mile, empowering users to make informed decisions and enhance overall performance. This innovative approach not only strengthens operational efficiency but also significantly improves service quality for end-users.

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.

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.

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.

RTEAM is an innovative real-time platform that empowers users to effectively set up alerts and manage exceptions. The alerts serve as instant notifications for urgent issues that require prompt action across various sectors like fieldwork, operations, and dispatch. Simultaneously, exceptions are recorded in real time for subsequent review and analysis. The platform includes a structured workflow process that ensures the timely gathering of pertinent information, which significantly boosts the quality and precision of data essential for conducting root cause analyses. Key performance indicators such as response time, turnaround time, chute time, nature of the problems, and instances of transport refusals are crucial for identifying areas where training could be beneficial. Users can seamlessly monitor exceptions as they arise and assign reason codes through a user-friendly workflow. By analyzing the aggregated results, teams can identify underlying causes and devise effective action plans to address them, ultimately improving operational efficiency and service quality. This comprehensive approach facilitates continuous improvement in processes and enhances overall effectiveness.

Security and observability tailored for Kubernetes environments. Implementing security and observability as code is essential for modern cloud-native applications. This approach encompasses cloud-native security as code for various elements, including hosts, virtual machines, containers, Kubernetes components, workloads, and services, ensuring protection for both north-south and east-west traffic while facilitating enterprise security measures and maintaining continuous compliance. Furthermore, Kubernetes-native observability as code allows for the gathering of real-time telemetry, enhanced with context from Kubernetes, offering a dynamic view of interactions among components from hosts to services. This enables swift troubleshooting through machine learning-driven detection of anomalies and performance issues. Utilizing a single framework, organizations can effectively secure, monitor, and address challenges in multi-cluster, multi-cloud, and hybrid-cloud environments operating on either Linux or Windows containers. With the ability to update and deploy security policies in mere seconds, businesses can promptly enforce compliance and address any emerging issues. This streamlined process is vital for maintaining the integrity and performance of cloud-native infrastructures.

PlayerZero is an innovative platform that utilizes artificial intelligence to enhance software quality by enabling engineering, QA, and support teams to effectively monitor, diagnose, and resolve issues prior to them affecting users. It achieves this by leveraging advanced AI algorithms and semantic graph analysis to merge various data signals from source code, runtime metrics, customer feedback, documentation, and historical records, providing teams with a comprehensive understanding of their software's functionality, the reasons behind any malfunctions, and strategies for improvement. The platform features autonomous debugging agents that can independently triage issues, perform root cause analyses, and propose solutions, resulting in fewer escalations and faster resolution times, all while maintaining essential audit trails, governance, and approval processes. Additionally, PlayerZero boasts a feature called CodeSim, which employs the Sim-1 model to simulate code changes and forecast their effects, thereby empowering developers with predictive insights. This combination of tools and capabilities equips organizations to enhance their software development lifecycle significantly.

Sensai offers a cutting-edge AI-driven platform for detecting anomalies, performing root cause analysis, and forecasting issues, which allows for immediate problem resolution. The Sensai AI solution greatly enhances system uptime and accelerates the identification of root causes. By equipping IT leaders with the tools to effectively manage service level agreements (SLAs), it boosts both performance and profitability. Additionally, it automates and simplifies the processes of anomaly detection, prediction, root cause analysis, and resolution. With its comprehensive perspective and integrated analytics, Sensai seamlessly connects with third-party tools. Users benefit from pre-trained algorithms and models available from the outset, ensuring a swift and efficient implementation. This holistic approach helps organizations maintain operational efficiency while proactively addressing potential disruptions.

Receive precise notifications of potential failures weeks or even months ahead by utilizing real-time information and predictive analytics. Make use of an integrated approach that includes prescriptive maintenance, root cause analysis, and RAM analysis to tackle problems at various levels, including equipment, process, and system. Efficiently implement automated Asset Performance Management solutions using minimal intervention machine learning techniques to foresee asset failures and minimize downtime across the entire plant, across systems, or in multiple sites. This proactive strategy not only enhances operational efficiency but also significantly boosts overall productivity.

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.

Safeguard business service-level agreements by utilizing dashboards that enable monitoring of service health, troubleshooting alerts, and conducting root cause analyses. Enhance mean time to resolution (MTTR) through real-time event correlation, automated incident prioritization, and seamless integrations with IT service management (ITSM) and orchestration tools. Leverage advanced analytics, including anomaly detection, adaptive thresholding, and predictive health scoring, to keep an eye on key performance indicators (KPIs) and proactively avert potential issues up to 30 minutes ahead of time. Track performance in alignment with business operations through ready-made dashboards that not only display service health but also visually link services to their underlying infrastructure. Employ side-by-side comparisons of various services while correlating metrics over time to uncover root causes effectively. Utilize machine learning algorithms alongside historical service health scores to forecast future incidents accurately. Implement adaptive thresholding and anomaly detection techniques that automatically refine rules based on previously observed behaviors, ensuring that your alerts remain relevant and timely. This continuous monitoring and adjustment of thresholds can significantly enhance operational efficiency.

Expertune PlantTriage stands out as an award-winning PID tuning software, crafted by a team boasting extensive expertise in controls spanning hundreds of years. This innovative tool continuously observes your plant, swiftly pinpointing issues as they arise. It evaluates and ranks information based on both technical and economic considerations, assisting in uncovering the root causes of problems while offering a comprehensive suite of analytical tools aimed at addressing them effectively. With round-the-clock surveillance, Expertune PlantTriage diligently monitors processes every day of the year, detecting issues in real time. It quickly highlights control loops that significantly influence business profitability, production efficiency, reliability, and overall quality. Leveraging a Big Data methodology, the root cause identification feature expedites the discovery of the underlying reasons for disruptions. Additionally, through Active Model Capture Technology, it enables automatic tuning of control loops for peak performance, achieving optimal settings in mere minutes and simulating the precise response characteristics desired. In summary, Expertune PlantTriage not only enhances operational efficiency but also empowers organizations to maintain high-quality standards.

Arize's machine-learning observability platform automatically detects and diagnoses problems and improves models. Machine learning systems are essential for businesses and customers, but often fail to perform in real life. Arize is an end to-end platform for observing and solving issues in your AI models. Seamlessly enable observation for any model, on any platform, in any environment. SDKs that are lightweight for sending production, validation, or training data. You can link real-time ground truth with predictions, or delay. You can gain confidence in your models' performance once they are deployed. Identify and prevent any performance or prediction drift issues, as well as quality issues, before they become serious. Even the most complex models can be reduced in time to resolution (MTTR). Flexible, easy-to use tools for root cause analysis are available.

Integrating observability with automated orchestration enables the development of self-managed and resilient applications on a large scale. Every moment, vast amounts of data pour in from observability and monitoring systems, collecting metrics, logs, and traces from all elements of intricate and changing applications. However, the challenge remains for humans to interpret and troubleshoot this information. They find themselves in a continuous loop of addressing alerts, pinpointing root issues, and deciding on effective remediation strategies. This traditional approach has not fundamentally evolved over the decades, remaining labor-intensive, reactive, and expensive. Causely transforms this scenario by eliminating the need for human intervention in troubleshooting, as it captures causality within software, effectively bridging the divide between observability and actionable insights. For the first time, the entire process of detecting, analyzing root causes, and resolving application defects is entirely automated. With Causely, issues are detected and addressed in real-time, ensuring that applications can scale while maintaining optimal performance. Ultimately, this innovative approach not only enhances efficiency but also redefines how software reliability is achieved in modern environments.

Implement a comprehensive service assurance framework that incorporates automated root cause analysis via event correlation, machine learning (ML), and topology. Oracle Unified Assurance can function as an overlay to consolidate monitoring across pre-existing assurance tools or serve as an independent assurance platform. The solution is designed for seamless integration into multivendor networks and management systems, or it can be utilized in a hybrid configuration. By leveraging Oracle’s Unified Assurance solution, businesses can achieve automation in assurance processes and facilitate both assisted and fully automated closed-loop operations. This framework enables dynamic, end-to-end assurance for large-scale 5G solutions, ensuring high-quality service and enhancing customer experience through ML analytics. Streamline operations via comprehensive network and service assurance by integrating with current tools, while also making the most of existing investments with a strategic plan to optimize tools and establish a foundation for autonomous operations, ultimately leading to improved efficiency and service reliability.