Frequently Asked Questions

No, and this is a common point of confusion. “Middleware” is also a generic computing term for software that sits between an operating system and an application, such as a message broker or an API gateway. Middleware, the company, is a distinct product: an observability platform for monitoring and debugging applications and infrastructure. The name refers to the company’s product category focus, not to that generic layer of software.

Middleware is an observability tool. It does not sit in the request path as a broker, proxy, or integration layer. It collects telemetry, logs, and events emitted by your applications and infrastructure through OpenTelemetry and other collectors, and gives you dashboards, alerts, and AI-driven root cause analysis on top of that data.

Middleware is used by engineering & DevOps teams of every size, from early-stage startups to mid-market and enterprise organizations. Small teams get started in minutes with a self-serve trial. Mid & Larger organizations get SSO, role-based access control, custom data retention, and dedicated support, with the option to bring your own cloud for full data residency control.

Middleware is OpenTelemetry-native, so there is no proprietary agent lock-in. Pricing is based on data ingestion rather than per-host or per-seat fees. OpsAI adds automated root cause analysis and auto-fix capabilities on top of the observability data, so teams can move from detection to resolution inside the same platform.

Middleware was founded in 2022 by Laduram Vishnoi and is headquartered in San Francisco, California. The company went through Y Combinator’s Winter 2023 batch.

No. Middleware supports auto-instrumentation for Python, Node.js, Java, .NET, and Go using the OpenTelemetry operator, so distributed tracing works without manual code changes. Manual SDK instrumentation is also available for teams that want finer control.

Middleware supports all major programming languages, including Java, Python, Node.js, Go, .NET, PHP, and Ruby,  and along with Kubernetes, containers, VMs, serverless platforms like AWS Lambda, and Windows IIS servers. It also ingests data from third-party agents and collectors, including the OTel Collector, Logstash, and Fluentd.

Most teams are instrumented and seeing data within minutes using the auto-instrumentation script. Teams migrating a large or complex stack from another platform typically plan for a few days to fully validate dashboards and alerts before cutting over.

Yes. You can book a demo with the team, or explore the interactive product tours available on individual product pages.

Yes. Teams that already run their own OTel collectors and pipelines can point their existing setup directly at Middleware. You keep your configuration and gain Middleware’s dashboards, alerting, anomaly detection, and OpsAI on top of it.

OpenTelemetry provides compatibility bridges that let OpenTracing-instrumented code interoperate with OpenTelemetry backends during migration. Middleware’s OpenTelemetry-native ingestion means once your code is instrumented with OpenTelemetry SDKs, it can send data to Middleware without further translation.

An OpenTelemetry-native platform avoids proprietary agent lock-in, since instrumentation follows an open, vendor-neutral standard. Teams can switch observability backends without re-instrumenting their code, and they benefit from the OTel ecosystem’s language coverage and community-maintained integrations rather than a single vendor’s closed agent.

OpsAI is Middleware’s AI SRE agent. It analyzes traces, logs, metrics, RUM sessions, and Kubernetes telemetry together to detect anomalies, surface root cause, and in many cases generate a pull request with a proposed fix. Issue detection is available at no extra cost, and root cause analysis and auto-fixes are billed by token usage.

When an alert fires, OpsAI pulls together the relevant traces, logs, metrics, and recent deployments into one incident view, and proposes a likely root cause instead of leaving the engineer to manually correlate signals across separate tools. For issues it can confidently resolve, it can generate a pull request with a suggested fix.

Yes. OpsAI takes on the first pass of correlating logs, traces, and metrics during an incident, which is typically the most time-consuming part of an on-call response. That gives on-call engineers a starting point instead of a blank dashboard, which shortens time to resolution and reduces the manual investigation load.

Yes. Because OpsAI investigates incidents using live telemetry rather than a fixed script, its root cause analysis and suggested fixes act as a running record of how issues were diagnosed, which helps newer engineers build context that would otherwise live only in senior teammates’ heads.

Yes. Middleware monitors Kubernetes clusters, nodes, pods, and workloads, and correlates that data with application traces and logs so you can trace an issue from a failing pod back to the request that triggered it.

Middleware Kubernetes monitoring auto-discovers clusters, nodes, and pods once the agent is installed, and streams resource usage, health status, and events in real time, correlated with the traces and logs coming from workloads running on those pods.

Yes. Middleware monitors managed Kubernetes across AWS EKS, Azure AKS, and Google GKE, along with self-managed clusters, in a single unified dashboard rather than requiring a separate view per cloud.

Yes. Middleware tracks Docker container status, exit codes, and daemon health, and correlates container state with the logs and metrics coming from inside the container, so you can see whether a crash or restart loop was caused by resource limits, a code error, or the host itself.

Middleware ingests logs, metrics, and traces from both AWS Fargate and Lambda through native AWS integrations, and correlates them with the rest of your stack on the same timeline, so serverless functions and containerized services can be debugged side by side rather than in separate tools.

Yes. Middleware’s LLM observability tracks prompts, completions, latency, and cost across LLM-powered applications, and correlates that data with the rest of your stack.

Yes. Synthetic monitoring covers HTTP, DNS, SSL, WebSocket, UDP, ICMP, TCP, and gRPC checks. Browser testing runs real user flows across browsers and devices, with session replay, step screenshots, and cross-browser checks, and both can power a public-facing status page.

Yes. Distributed tracing shows the full journey of a request across services, with execution time, a dependency graph, and frontend-to-backend correlation from a single script installation, so you can see where in the chain a request slowed down or failed.

Continuous profiling captures real-time, code-level performance data, such as CPU and memory usage by function, without manual triggering. Middleware links profiling data directly to distributed traces, so you can click from a slow trace straight into the flame graph for that exact request instead of switching tools.

Yes. Deployment tracking associates a version tag with your APM data, so you can see how a release affected error rates, latency, or resource usage compared to the version before it.

Yes. Session replay records real user sessions on web and mobile, including clicks, scrolls, load events, console errors, and network activity, and correlates them with backend traces and errors for faster debugging. Sensitive fields like emails, card numbers, and other personal data are masked by default.

Middleware counts a session as a single continuous period of user activity. All interactions, including page views, clicks, navigation events, and other tracked activities, are grouped into the same session. By default, a session remains active for up to 4 hours as long as the user continues interacting with your application. If the user is inactive for more than 15 minutes, the session expires. Any activity after the inactivity timeout or after the maximum 4-hour session duration starts a new session.

Yes. Real user monitoring extends to native Android, iOS, and React Native apps, tracking app start time, screen load performance, API calls, and crashes alongside your backend data.

Core Web Vitals are Google’s metrics for real-world page experience, including Largest Contentful Paint (LCP), First Input Delay or Interaction to Next Paint, and Cumulative Layout Shift (CLS). Middleware’s RUM tracks these alongside First Contentful Paint, page load time, error rates, and session counts, broken down by page and user segment.

Yes. RUM sessions carry the same trace identifiers as backend requests, so a slow page load or frontend error can be traced directly to the API call, database query, or service that caused it, without manually correlating timestamps across tools.

Yes. Middleware monitors databases including MySQL, SQL Server, PostgreSQL, MongoDB, Oracle, ClickHouse, Redis, Elasticsearch, and Cassandra, and correlates slow queries with the application traces that triggered them.

Yes. Middleware provides customizable dashboards, a unified query language across data sources, and live alerting so you can be notified through channels like Slack or Microsoft Teams when a metric crosses a threshold.

Yes. Middleware’s data pipeline lets you parse, filter, and drop data at the source or at ingestion time, so you only pay for and store the telemetry that is actually useful, rather than everything your services emit.

Yes. Middleware lets you build a status page powered by your synthetic checks and incidents, and publish it so customers and stakeholders can see service availability in real time.

Yes. Many teams migrate to Middleware from other observability platforms to consolidate multiple tools into one, often at lower cost. See how Middleware compares on the comparison hub.

Middleware and Datadog cover similar ground (APM, infrastructure, logs, RUM), but Middleware is OpenTelemetry-native with flat, data-volume-based pricing rather than Datadog’s per-host and per-feature pricing, and includes OpsAI to help engineers accelerate root cause analysis, investigate incidents using AI-powered insights, and reduce MTTR. See the full Middleware vs Datadog comparison for a detailed breakdown.

Both are full-stack observability platforms. Middleware differentiates on OpenTelemetry-native ingestion, usage-based pricing without a separate per-seat charge, and OpsAI’s automated root cause analysis. See the full Middleware vs New Relic comparison for details on APM and Kubernetes tracing specifically.

Grafana is primarily a visualization layer that typically pairs with separate backends like Loki, Mimir, and Tempo. Middleware is a unified platform that includes ingestion, storage, dashboards, alerting, and OpsAI out of the box. See the full Middleware vs Grafana comparison.

Yes. Middleware encrypts customer data in transit and at rest using industry-standard encryption and security best practices.

Middleware runs on industry-leading cloud providers with global availability zones. For teams with strict data residency or compliance requirements, Middleware also offers a bring your own cloud (BYOC) option on custom contracts.

Yes. Middleware supports unlimited users on every plan, with role-based access control and SSO available on paid plans, so there is no per-seat cost to add teammates.

Reach out through the contact page or book time with the team via a demo request.