Middleware

What Middleware does

Middleware is an AI-driven, full-stack cloud observability platform that detects, diagnoses, and automatically fixes issues across your entire system, including Kubernetes, logs, traces, metrics, and Real User Monitoring (RUM). Its Ops AI Agentic Mode identifies and resolves problems without human intervention, enabling engineering teams to reduce noise, eliminate manual troubleshooting, and achieve self-healing infrastructure. The platform unifies APM, logs, infrastructure, RUM, synthetics, databases, and LLM monitoring on one timeline, automatically correlating signals across the stack, pinpointing root causes, and providing actionable insights with suggested fixes. Middleware aims to streamline cloud-native complexity, provide end-to-end observability, and empower organizations to modernize their infrastructure stack with agile, scalable, and resource-efficient observability tools.

Products

• OpsAIAn AI SRE Agent that detects, diagnoses, and automatically fixes issues across the entire system, correlating signals, pinpointing root causes, and providing actionable insights with suggested fixes. It automates remediation to resolve critical issues before users are affected and can generate automatic PRs.
• Infrastructure MonitoringProvides deep system visibility, high-cardinality metrics, customized dashboards, correlated incident context, and alerts across any cloud environment and OS. It monitors hosts, containers, VMs, and cloud services in real time, visualizes infrastructure health, and correlates spikes with application impact. It includes Kubernetes and container observability.
• Container MonitoringDetects Kubernetes and container issues instantly with deep cluster-level visibility across the entire cloud infrastructure. It correlates container metrics, logs, traces, and infrastructure data to understand real-time impact and optimizes orchestration with unified cluster health dashboards.
• Log MonitoringCorrelates logs, traces, and metrics for seamless, one-click troubleshooting. It allows searching, filtering, and analyzing high-volume logs in real time to detect errors and anomalies. Includes AI-powered QueryGenie for natural language log queries and features like log anomaly detection and sensitive data masking.
• Database MonitoringAnalyzes every query to detect slow executions, identify resource-intensive patterns, and pinpoint database bottlenecks. It monitors MySQL, PostgreSQL, MongoDB, Cassandra, and Aurora from one unified performance dashboard, tracking CPU, memory, disk, and query throughput with alerts.
• Synthetic MonitoringTests APIs and browser journeys globally to proactively detect availability, latency, and performance issues. It validates HTTP, UDP, DNS, and WebSocket APIs and runs automated browser tests to simulate user workflows.
• Application Performance Monitoring (APM)Tracks real-time spans, identifies latency bottlenecks, and correlates traces across APIs and databases. It uses continuous profiling and live monitoring to resolve backend performance issues proactively, visualizing application health, dependencies, and traffic flows. Built on OpenTelemetry.
• Real User Monitoring (RUM)Gains deep visibility into real user experiences across web & mobile applications to detect performance issues early. It replays complete user sessions with correlated backend traces for faster end-to-end troubleshooting and includes privacy controls for sensitive user data.
• LLM ObservabilityMonitors LLM applications, AI agents, prompts, and AI workflows in real time. It tracks latency, token usage, costs, response quality, errors, and model performance, correlating AI interactions with traces, logs, metrics, and user sessions for faster debugging and root cause analysis.
• Digital Experience Monitoring (DEM)Ensures consistent user experience by monitoring and optimizing digital platforms. It includes RUM and Synthetic Monitoring tools to improve UX collaboration, handle and prioritize issues, and reduce MTTD and MTTR.
• Endpoint MonitoringTracks every API route in real traffic, showing which endpoints are slow or failing. It automatically discovers API routes from trace traffic, displays request volume, error rate, and P95/P99 latency for each endpoint, and can link with OpenAPI (Swagger) specs.
• Alerts and NotificationsProvides actionable alerts to maximize uptime. Features include anomaly and outlier detection, dynamic thresholds, pre-configured default alerts, root cause analysis and correlation, and multi-channel notifications and alert allocation.

Key capabilities

• AI-driven, full-stack cloud observability platform
• Ops AI Agentic Mode for automatic issue detection and fixing
• Infrastructure, Kubernetes, APM, Database, Log, Synthetic and Browser Monitoring
• Dashboard builder, Alerts, and browser testing
• Full-stack Observability with AI SRE Agent
• Unlimited Data Ingestion (during free trial)
• Unlimited RUM Sessions (during free trial)
• Unlimited Synthetic Checks (during free trial)
• 10 Browser Test Runs (during free trial)
• Unlimited Users (during free trial)
• Community Based Support (during free trial)
• 14 day retention (during free trial)
• Pay As You Go pricing model
• Error solving with OpsAI
• Ingestion Control & Data Pipeline
• Default 30 day retention (Pay As You Go)
• SSO and Security Features (Custom Pricing)
• Dedicated Slack/MS Teams Channel (Custom Pricing)
• BYOC (Bring Your Own Cloud) (Custom Pricing)
• Custom Data Retention (Custom Pricing)
• 24x7 Support (Custom Pricing)
• Root Cause Analysis
• Faster Issue Solving
• Automatic Issue Detection
• Automatic PR Generation
• Live Alerting
• Custom Dashboards
• User Facing Status Page
• Long-Term Querying
• Real-Time Live View
• Attribute Matching Search
• Fuzzy Matching
• Regex Search
• Correlate Traces & Metrics
• Log Anomaly Detection
• Log Patterns
• Any JSON Format support
• Manage ingestion volume
• Log Explorer
• Sensitive Data Masking
• Custom Log Attributes
• Unlimited Queries & Search
• Only Ingest What You Need
• Custom Log Directories
• Custom Log Attribute Extraction
• Unified APM, logs, infrastructure, RUM, synthetics, databases, and LLM monitoring
• Auto-instrumentation across infrastructure, applications, databases, Kubernetes, and Linux
• Visual network policies and CRD monitoring
• Real-time infrastructure health visualization
• Deep cluster-level visibility for containers
• Correlation of container metrics, logs, traces, and infrastructure data
• Unified cluster health dashboards
• AI-powered QueryGenie for natural language log queries
• Analysis of slow database executions and resource-intensive patterns
• Monitoring of MySQL, PostgreSQL, MongoDB, Cassandra, and Aurora
• API and browser journey testing globally
• Validation of HTTP, UDP, DNS, and WebSocket APIs
• Automated browser tests
• Real-time span tracking and latency bottleneck identification
• Continuous profiling and live monitoring for backend performance
• Visualization of application health, dependencies, and traffic flows
• Deep visibility into real user experiences
• User session replay with correlated backend traces
• Privacy controls for sensitive user data
• Real-time monitoring of LLM applications, AI agents, prompts, and AI workflows
• Tracking of latency, token usage, costs, response quality, errors, and model performance for LLMs
• Correlation of AI interactions with traces, logs, metrics, and user sessions
• Frontend to Backend Correlation
• Single-Click Troubleshooting
• Unified Application Insights
• Comprehensive Monitoring (server, network, database, application, user behavior)
• Proactive Issue Detection (unusual patterns, anomalies, error data analysis, user interaction simulation)
• Seamless Collaboration and Communication (single platform, flexible reporting, ITSM integration)
• Cost-Effective Infrastructure Management (optimize cloud resource utilization)
• Unified Monitoring Platform (metrics, logs, KPI, traces, data)
• Real-time Visibility
• Sophisticated Visualization Tools
• Custom Attributes
• 24/7 Global Customer Support
• End-to-end distributed tracing
• Automated root cause analysis (RCA)
• Performance profiling & application monitoring
• Service observability & dependency mapping
• Data ingestion control & open standards
• OpenAPI (Swagger) integration for Endpoint Monitoring
• Runtime parameter filtering for Endpoints
• Drill-down into traces and endpoints
• Comparison of endpoint performance across services
• Anomaly & Outlier Detection for alerts
• Dynamic Thresholds for alerts
• Pre-Configured Default Alerts
• Multi-Channel Notifications and Alert Allocation

Use cases

• Detecting and fixing issues across entire systems (Kubernetes, logs, traces, metrics, RUM)
• Reducing noise and eliminating manual troubleshooting for engineering teams
• Achieving self-healing infrastructure
• Monitoring cloud infrastructure and applications
• Debugging and resolving issues faster
• Optimizing system performance and reducing latencies
• Gaining full visibility into application health and performance
• Identifying and resolving issues before they affect users
• Streamlining cloud-native complexity
• Modernizing IT infrastructure with scalable observability tools
• Correlating frontend to backend performance to identify and resolve issues
• Reducing Mean Time to Resolve (MTTR)
• Improving user experience
• Optimizing IT ecosystem with precision
• Reducing downtime and errors
• Improving overall system availability
• Optimizing infrastructure and cloud resource utilization
• Protecting sensitive data and intellectual property
• Ensuring adherence to regulatory requirements
• Identifying areas for improvement and optimizing systems for agility and responsiveness
• Monitoring server, network, and database performance in real-time
• Tracking application performance, latency, and errors with APM
• Monitoring user behavior and interactions in real-time with RUM
• Identifying unusual patterns and anomalies in real-time
• Capturing and analyzing error data for quick resolution
• Simulating user interactions to test application performance
• Streamlining incident management with ITSM tool integration
• Optimizing cloud resource utilization to reduce costs
• Ensuring optimal infrastructure provisioning
• Improving developer productivity
• Reducing support tickets
• Ensuring consistent user experience with Digital Experience Monitoring
• Safeguarding sensitive financial data with on-premise solutions
• Reducing customer churn
• Proactively detecting and resolving customer-facing issues
• Acting before issues become critical
• Informing targeted strategies with precise, customer-centric data
• Monitoring user interactions to identify areas for improvement
• Understanding which screens engage users the most
• Tracking user actions to optimize gameplay
• Running proactive tests to ensure API performance and availability
• Simulating user interactions from various locations, devices, and environments
• Receiving alerts and notifications for API errors and performance issues
• Catching front-end errors (browser, console, network)
• Identifying root causes with drill-down capabilities and error analysis
• Monitoring API errors with assertions
• Reducing Mean Time To Detect (MTTD) and Mean Time To Resolve (MTTR)
• Unifying and correlating telemetry data (logs, metrics, traces, KPIs)
• Integrating multiple data sources into a single platform
• Collecting, transforming, and routing observability data
• Proactively detecting, responding, and resolving issues to minimize downtime
• Improving system performance and health
• Integrating 200+ tools and services across tech stack
• Boosting efficiency and reducing errors
• Making faster and more informed decisions
• Streamlining data management
• Maximizing uptime
• Boosting operational efficiency
• Pinpointing issues easily with end-to-end distributed tracing
• Auto-instrumenting Kubernetes, infrastructure, applications, and databases
• Automatically discovering applications and databases
• Visualizing service dependencies and trace requests end-to-end
• Correlating traces, logs, and metrics in a single view
• Tracking Latency, Error rate, Traffic, and Saturation (LETS) in real time
• Continuously profiling resources and analyzing code and database queries
• Tracking dependencies and monitoring end-to-end request flows
• Visualizing frontend-to-backend-to-database dependencies
• Correlating traces and logs via TraceID
• Automated root cause analysis for application errors
• Isolating error-prone services and surfacing connected dependencies
• Tracking real-time service metrics
• Monitoring downstream dependencies
• Visualizing service communication and architecture
• Tracking every API route in real traffic
• Linking OpenAPI (Swagger) specs for definitions and parameters
• Filtering endpoints by runtime parameters
• Drilling into traces and endpoints for span breakdown
• Comparing endpoint performance across services and releases
• Identifying unusual patterns and outliers in system behavior for alerts
• Setting adaptive thresholds for alerts
• Using pre-built alerts for common issues
• Automatically linking related alerts and data to pinpoint root causes
• Routing alerts to the right teams and channels

AI approach

Middleware leverages AI, specifically an "Ops AI Agentic Mode" and "AI SRE Agent," to detect, diagnose, and automatically fix issues across various observability components like Kubernetes, logs, traces, metrics, and RUM. The AI is used for real-time issue detection and resolution, anomaly detection, log pattern analysis, and generating precise log queries from natural language (QueryGenie). It also assists in root cause analysis and can generate automatic PRs. The company states it builds many components of its stack, including streaming, compression, storage, and querying, and uses Otel as a framework for its agents.

Tech named: AI SRE Agent, Ops AI Agentic Mode, OpsAI, AI-powered QueryGenie, AI anomaly detection, machine learning alerts

Industries served

• Software Development
• Retail

What it says sets it apart

• AI-driven, full-stack observability platform with Ops AI Agentic Mode for automatic issue detection and fixing
• Reduces time spent debugging and resolving issues by nearly 90%
• Does not stop at detecting issues; actually helps fix problems in production
• Cost-effective pricing with unlimited data ingestion, RUM sessions, and synthetic checks in free trial
• Flexible pricing that adapts to business growth (Pay As You Go, Custom Pricing)
• Leverages OpenTelemetry as a framework for agents but builds proprietary components for streaming, compression, storage, querying, and UI
• Supports many other data collectors and origins like Prometheus, databases, etc.
• Unifies APM, logs, infrastructure, RUM, synthetics, databases, and LLM monitoring on one timeline
• OpsAI automatically correlates signals across the stack, pinpoints root causes, and provides actionable insights with suggested fixes
• Offers LLM Observability for monitoring AI applications, agents, prompts, and workflows
• Provides Frontend to Backend Correlation to eliminate silos and resolve issues faster
• Offers 450+ built-in integrations across various categories
• Strong commitment to GDPR compliance and data security (encryption, access controls, audit trails, minimal data collection)
• Infrastructure risk prevention with AI anomaly detection and resource utilization forecasting
• Reduced MTTR with correlated telemetry across infrastructure metrics, logs, and distributed traces
• Query Genie for natural language log queries
• Built by engineers from Netflix, DigitalOcean, Google, Cisco, Freshworks, and Acquire
• Open Source Commitment drives rapid development
• Low-cost, high-capacity cloud solutions that evolve with business scale
• Unified observability platform replacing multiple monitoring tools (e.g., Datadog, New Relic)
• User-friendly interface and easier onboarding compared to competitors
• Reliable alerting system compared to competitors