Prometheus VS DevOS
Compare Prometheus VS DevOS and see what are their differences
A centralized log management and SIEM platform built for compliance, auditing, anomaly detection, and infrastructure visibility.
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Prometheus
Grafana
Datadog
NewRelic
Zabbix
Splunk
Kubernetes
Dynatrace
DevOS
SRE.ai
y0
DevOS is what happens when you stop thinking of AI as "a tool" and start thinking of it as "an employee." The premise: AI coding agents in 2026 are technically remarkable. Devin can ship features autonomously. Cursor + Claude Code accelerate every senior engineer. Copilot writes half the boilerplate. But every existing AI agent product treats AI as a tool you call โ invoked from an IDE, prompted in a chat, given a one-shot task. That's the wrong frame for how teams actually work. Real teams don't have "tools" โ they have employees. Employees: Have specialized roles (frontend, backend, QA, design, copy, sysadmin) Take tickets off a sprint board Attend standups and report blockers Hand off work to teammates with context Open PRs, get code reviews, respond to comments Have a track record visible across sprints DevOS treats AI agents the same way. Three layers in one product: 1. The marketplace. Browse specialized AI agents by role. Each comes with role-specific prompting, tool integrations, and a track record. Hire as many as your team's working style needs. Agents are configured for their role โ a "frontend dev" agent ships React/Vue with the team's design system patterns; a "copywriter" agent ships marketing pages in your brand voice; a "QA" agent writes Playwright tests and triages bugs. 2. The sprint board. Linear/Jira-style kanban with full agile machinery: sprints, epics, backlog grooming, sprint planning, retrospectives. Agents appear as assignable team members. Drag a ticket onto an agent the same way you'd assign it to a human dev. The agent picks it up, posts an estimate, asks clarifying questions if needed, and starts working. 3. The communication layer. Standups are automated โ every morning agents post what they shipped yesterday, what they're doing today, and what's blocking them. PRs are opened in your real repo. Code reviews happen in real GitHub / GitLab. Slack/Discord/Telegram integration so humans can talk to agents like teammates
Prometheus DevOSBased on our record, Prometheus seems to be more popular. It has been mentiond 300 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
Prometheus scrapes metrics from the stack. Node exporter covers the host, cAdvisor covers containers, and individual services expose their own endpoints where supported. The main value isn't dashboards (though those exist) - it's having a queryable record of system state over time, and a place to hook alerts when something drifts. - Source: dev.to / 2 months ago
Prometheus is the industry-standard time-series database for infrastructure metrics. Paired with Grafana for visualization and Alertmanager for routing, it forms the backbone of monitoring at companies from startups to Netflix-scale deployments. This isn't a single tool โ it's an ecosystem. - Source: dev.to / 2 months ago
To monitor and analyze rate limiting metrics, we're using a combination of Redis and Prometheus. We're storing rate limiting metrics in Redis and then using Prometheus to scrape the metrics and display them in a dashboard. Here's an example of how we're storing rate limiting metrics in Redis:. - Source: dev.to / 3 months ago
In this post, we compare two forecasting models, Chronos (ChronosโBolt) and Toto, on telemetry from Prometheus and OpenSearch. We judge them with two easy metrics: MASE for point accuracy and CRPS for the quality of uncertainty. - Source: dev.to / 3 months ago
For monitoring infrastructure, Prometheus and Grafana are widely used for pipeline metrics collection and alerting. For orchestration that includes built-in run observability, Apache Airflow tracks run history, task durations, and failure states in a web UI. Python with SQLAlchemy is the standard stack for custom pipeline implementation with relational state management. - Source: dev.to / 4 months ago
Grafana - Data visualization & Monitoring with support for Graphite, InfluxDB, Prometheus, Elasticsearch and many more databases
SRE.ai - AI agents to simplify and automate Salesforce devops
Datadog - See metrics from all of your apps, tools & services in one place with Datadog's cloud monitoring as a service solution. Try it for free.
y0 - AI agents that code, browse, and build for you
NewRelic - New Relic is a Software Analytics company that makes sense of billions of metrics across millions of apps. We help the people who build modern software understand the stories their data is trying to tell them.
Zabbix - Track, record, alert and visualize performance and availability of IT resources