惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

Martin Fowler
Martin Fowler
有赞技术团队
有赞技术团队
博客园_首页
H
Help Net Security
GbyAI
GbyAI
aimingoo的专栏
aimingoo的专栏
V
Visual Studio Blog
The Cloudflare Blog
腾讯CDC
Jina AI
Jina AI
Last Week in AI
Last Week in AI
月光博客
月光博客
博客园 - 叶小钗
Google DeepMind News
Google DeepMind News
B
Blog RSS Feed
Blog — PlanetScale
Blog — PlanetScale
人人都是产品经理
人人都是产品经理
Engineering at Meta
Engineering at Meta
Y
Y Combinator Blog
Hugging Face - Blog
Hugging Face - Blog
博客园 - 聂微东
爱范儿
爱范儿
N
Netflix TechBlog - Medium
F
Fortinet All Blogs

DEV Community

Authentication Security Deep Dive: From Brute Force to Salted Hashing (With Java Examples) Why AI Systems Don’t Fail — They Drift Spilling beans for how i learn for exam😁"Reinforcement Learning Cheat Sheet" I Replaced Chrome with Safari for AI Browser Automation. Here's What Broke (and What Finally Worked) How Python Borrows Other People's Work The $40 Architecture: Processing 1 Billion API Requests with 99.99% Uptime Vibe Coding: A Workflow Guide (From Zero to SaaS) Most webhook security guides protect the wrong side. The scary part is delivery. Headless CMS for TanStack Start: Build a Blog with Cosmic EU Age Verification App "Hacked in 2 Minutes" — What Actually Happened Comfy Cloud’s delete function does not actually remove files Running AI Models on GPU Cloud Servers: A Beginner Guide Event-driven media intelligence with AWS Step Functions and Bedrock I scored 500 AI prompts across 8 quality dimensions — here's what broke How to Call Google Gemini API from Next.js (Free Tier, No Backend Needed) The Portal Protocol: Reclaiming Human Connection in the Age of AI How to Fix Your Team's Scattered Knowledge Problem With a Self-Hosted Forum Intro to tc Cloud Functors: A Graph-First Mental Model for the Modern Cloud Designing Multi-Tenant Backends With Both Ownership and Team Access I Built a Neumorphic CSS Library with 77+ Components — Here's What I Learned PostgreSQL Performance Optimization: Why Connection Pooling Is Critical at Scale Cómo construí un SaaS multi-rubro para gestionar expensas en Argentina con FastAPI + Vue 3 🚀 I Built an Ethical Hacking Scanner Tool – Open Source Project I Replaced /usage and /context in Claude Code With a Single Statusline A Pythonic Way to Handle Emails (IMAP/SMTP) with Auto-Discovery and AI-Ready Design I Collected 8.9 Million Polymarket Price Points — Here's What I Found About How Markets Really Move EcoTrack AI — Carbon Footprint Tracker & Dashboard Everyone's Using AI. No One Agrees How. 5 self-hosted ebook managers worth trying in 2026 Building Your First AI Agent with LangChain: From Chatbot to Autonomous Assistant
Introducing Nova AI Ops: The AI-Native Operating System f...
Samson Tanimawo · 2026-06-24 · via DEV Community
Cover image for Introducing Nova AI Ops: The AI-Native Operating System for SRE Teams

Samson Tanimawo

Hey, I'm Samson — and I built Nova AI Ops because I was tired of being paged at 3 AM.

For years, I watched SRE teams (including my own) drown in the same problems:

  • 12+ monitoring tools that don't talk to each other
  • $50,000/month bills from Datadog + PagerDuty + Splunk + New Relic + OpsGenie + half a dozen more
  • 300+ alerts per day, most of which were noise
  • 47-minute MTTRs because engineers had to check 6 dashboards during every incident
  • On-call burnout so bad that 40% of the team left in a year

I kept thinking: this has to be a tooling problem, not a people problem.

So I built Nova AI Ops.

What Nova AI Ops Is

Nova AI Ops is one AI-native platform that replaces your entire monitoring and incident response stack. One install command, zero config files, 500+ integrations.

But the real difference isn't the consolidation — it's the AI.

We built a fleet of 100 specialized AI agents that work in parallel:

  • Detection agents watch metrics, logs, traces, and events 24/7
  • Correlation agents group related alerts (94% noise reduction in production)
  • Diagnosis agents match incoming incidents against 10,000+ historical patterns
  • Remediation agents execute 954 pre-built runbooks with automatic rollback
  • Approval Queue for high-risk actions that need a human in the loop

The result? 78% of incidents auto-resolve without anyone getting paged. The 22% that do reach humans arrive with root cause analysis, blast radius preview, suggested fix, and one-click approval.

The Numbers That Matter

Here's what we see in production environments:

Metric Before Nova After Nova
Alerts per day 300+ ~18
MTTR 47 min 3 min
Auto-resolution rate 0% 78%
Monitoring bill $50K/mo $29/user
Dashboards during incidents 6+ 1
Tools replaced - 12+

What Makes It "AI-Native"

Most "AIOps" tools bolt ML onto existing monitoring. They make alerts smarter but still wake you up.

Nova was built AI-first from day one:

  1. Agents think, not just alert. When an incident fires, the diagnosis agent walks your dependency graph, pulls relevant logs, queries historical patterns, and produces a ranked list of likely causes — before any human opens the dashboard.

  2. What-If simulation. Before enabling auto-remediation on a runbook, you can replay any past incident through it to see exactly what would have happened. No surprises in production.

  3. Baseline learning. Nova observes your services for 14 days and learns what "normal" looks like. A traffic spike that would trigger a static threshold gets ignored if it matches Tuesday morning patterns.

  4. Safety rails. Every remediation runs in a sandboxed context with automatic rollback if validation fails. High-risk actions (database changes, prod deploys) always require human approval via the Approval Queue.

Who It's For

Nova AI Ops is for you if:

  • You're an SRE, DevOps engineer, or platform engineer
  • You run production workloads on AWS, GCP, Azure, or Kubernetes
  • You're tired of tool sprawl, alert fatigue, or on-call burnout
  • You want AI that actually does work, not AI that just summarizes dashboards

What We Replace

One Nova subscription replaces:

  • Datadog (metrics + APM + infra monitoring)
  • PagerDuty / OpsGenie / VictorOps (on-call + alerting)
  • Splunk / Elastic (log management)
  • New Relic / Dynatrace (APM)
  • Grafana (dashboards, but you can keep yours if you want)
  • Custom runbook tools and incident management platforms

Everything in one dashboard, one bill, one AI platform.

Pricing (Because I Hate "Contact Sales" Pages)

  • Basic: Free forever, 1 user, core monitoring
  • Standard: $29/user/mo, up to 10 users, AI Copilot, on-call scheduling
  • Pro: $50/user/mo, unlimited users, full AI auto-remediation, 90-day retention

No enterprise sales call. Self-serve upgrade. Free trial at novaaiops.com — no credit card required.

What's Coming Next

Over the next few weeks, I'll be posting deep-dives on dev.to about:

  • How we built the AI agent fleet architecture
  • The correlation engine that cuts alert noise by 94%
  • Runbook automation patterns that actually work in production
  • Post-mortem best practices from 200+ real incidents
  • Monitoring costs and why your bill is too high
  • On-call wellness and fair rotation design

If any of this resonates, follow along. I write honestly about what works and what doesn't — no marketing fluff.

Try It

If you want to see it in action, novaaiops.com has a free trial with no credit card required. Install takes under a minute:

curl -fsSL https://get.novaaiops.com/install.sh | sudo bash

And if you just want to talk shop about SRE, monitoring, or AI agents, drop a comment. I read every reply.

Happy to answer any questions about the platform, the tech, or the decision to build it in the first place.

— Samson


Written by Dr. Samson Tanimawo
BSc · MSc · MBA · PhD
Founder & CEO, Nova AI Ops. https://novaaiops.com