How Zomato Uses AI in DevOps: The Future of Smart Software Delivery
In the fast-paced, hyper-competitive world of food delivery, Zomato has positioned itself as more than just a meal-ordering app — it’s a tech-driven ecosystem built to deliver a seamless experience to millions of users, across thousands of cities, in real time. Whether you’re ordering biryani in Bangalore or sushi in Surat, the app feels fast, reliable, and intuitive. But have you ever wondered what powers this kind of scale and precision behind the scenes?
It’s not just good code or a strong backend team. It’s the strategic fusion of DevOps and Artificial Intelligence (AI) — a powerful combo that allows Zomato to stay one step ahead of issues, push updates smoothly, optimize resources, and ensure near-zero downtime, even during massive spikes in demand.
Traditional DevOps brought us automation, CI/CD, and faster release cycles. But as systems become more complex, user expectations rise, and data scales exponentially, AI is emerging as the brain that makes DevOps even smarter — learning from past incidents, predicting future issues, and optimizing operations in real time.
In this blog, we’ll dive deep into how Zomato is embracing AI-powered DevOps to create an intelligent, self-improving infrastructure. From predictive autoscaling and intelligent incident response to anomaly detection and smarter CI/CD pipelines — you’ll see how Zomato uses AI not just to run its operations, but to continuously refine and revolutionize them.
And more importantly, we’ll explore what this means for you as a developer or DevOps engineer. Whether you’re building your first CI/CD pipeline or working in complex microservices, the lessons here are practical, actionable, and rooted in one of India’s most successful tech stories.
Let’s pull back the curtain on Zomato’s DevOps brain — and find out what smart automation truly looks like in the real world.
1. What is DevOps?
2. Why Introduce AI into DevOps?
3. Zomato’s Use of AI in DevOps
- Intelligent Monitoring & Anomaly Detection
- Predictive Autoscaling
- Smarter CI/CD Pipeline Optimization
- Automated Root Cause Analysis (RCA)
- AI-Powered ChatOps
4. Lessons for Developers and DevOps Engineers
5. Bonus!
1. What is DevOps?
At its core, DevOps is the practice of unifying Development (Dev) and Operations (Ops) teams to streamline the software delivery process. But it’s more than just a collaboration model — it’s a cultural and technical shift that transforms how software is built, tested, released, and maintained.
DevOps encourages continuous integration (CI) and continuous delivery (CD), allowing developers to push new code into production more frequently and reliably. It emphasizes automation at every stage — from code commits and testing to deployment and monitoring — reducing manual errors and speeding up delivery cycles.
Beyond tools and pipelines, DevOps is also about building a culture of shared responsibility, where developers and operations teams work side by side to ensure stability, performance, and rapid innovation. In today’s world of microservices, real-time user feedback, and cloud infrastructure, DevOps is no longer optional — it’s the engine that powers agile, scalable, and resilient software systems.
2. Why Introduce AI into DevOps?
While DevOps automates the software delivery pipeline, AI adds intelligence — making systems smarter, faster, and more resilient.
With AI, teams can:
– Predict failures before they happen
– Auto-remediate incidents without human intervention
– Dynamically allocate resources based on real-time demand
– Detect anomalies instantly across logs, metrics, and traffic
For high-scale platforms like Zomato, where downtime isn’t an option, AI helps DevOps move from reactive to proactive, ensuring smooth performance even under heavy load.
3. Zomato’s Use of AI in DevOps
Zomato delivers a seamless experience to millions of users by leveraging AI-driven DevOps practices that ensure speed, reliability, and intelligent automation. Here’s how AI is integrated across their DevOps ecosystem:
3.1. Intelligent Monitoring & Anomaly Detection
Zomato processes thousands of orders every minute, making real-time monitoring critical. AI models constantly analyze system metrics like server performance, API latency, and transaction volumes. Tools such as Prometheus and Grafana, enhanced with machine learning, detect abnormal behavior — such as unexpected traffic surges or slowdowns — and trigger alerts automatically.
Example:
If the number of restaurant listings in a city drops suddenly, AI can flag it as a system anomaly and notify the team instantly — even before users experience an issue.
3.2. Predictive Autoscaling
To handle traffic fluctuations — like peak lunch or dinner hours — Zomato uses AI to forecast demand patterns based on historical and real-time data. This enables automatic scaling of cloud infrastructure in anticipation of load, ensuring a lag-free user experience without overspending on server resources.
Outcome:
Better app performance during high traffic and optimized costs during low activity periods.
3.3. Smarter CI/CD Pipeline Optimization
Zomato ships updates frequently. AI is embedded into their CI/CD workflows to improve deployment efficiency. It can:
- Prioritize builds with the highest user impact
- Detect and isolate flaky or unstable test cases
- Predict build failures using commit history, code complexity, and past trends
Impact:
Fewer failed deployments, faster rollouts, and better developer productivity.
3.4. Automated Root Cause Analysis (RCA)
When incidents occur, Zomato’s AI systems automatically correlate logs, metrics, and traces across multiple services. This helps quickly identify the root cause — whether it’s a faulty config, a failed service, or an unusual traffic pattern.
🔥 Instead of hours spent digging through logs, AI can pinpoint the issue and suggest a fix — like identifying a recent change in the delivery-tracking service as the likely cause.
3.5. AI-Powered ChatOps
Zomato’s DevOps team uses AI-integrated bots within platforms like Slack to streamline operations. These bots can:
- Monitor deployment health
- Roll back faulty releases
- Share performance metrics instantly
- Respond to natural language queries like: “What’s the current error rate in Pune?”
Result:
Faster decision-making, instant visibility, and smoother collaboration across teams.
4. Lessons for Developers and DevOps Engineers
Zomato’s AI-powered DevOps journey offers valuable insights for anyone building or managing modern software systems. Whether you’re just starting out or already running pipelines in production, these takeaways can help you level up your approach:
– Don’t just automate — optimize intelligently.
Automation is the foundation, but intelligence is the differentiator. Use AI to prioritize what matters and continuously improve.
– Shift from reactive to proactive monitoring.
Traditional alerts come after something breaks. AI can spot early warning signs — giving you time to act before users are affected.
– Predict instead of just responding.
Use historical data, system patterns, and commit histories to predict outages, test failures, or performance bottlenecks.
– Embed AI into your deployment pipelines.
Integrate AI tools that analyze code quality, test stability, build performance, and resource usage — right within your CI/CD setup.
– Adopt a data-driven DevOps culture.
Every log, metric, and alert tells a story. AI helps decode that story faster — helping teams make smarter decisions and respond with speed.
5. Want to Go Deeper?
At CodeKerdos, we break down how real-world companies like Zomato use AI to scale and optimize DevOps — and show you how to apply those practices yourself.
Our hands-on DevOps + AI courses are designed for modern developers and engineers who want to build smarter systems that don’t just work, but adapt and evolve.
What you’ll gain:
– Real-world project experience
– Knowledge of AI-integrated monitoring tools
– Smart CI/CD automation techniques
– Root cause analysis using ML
And more!