AIOps

If using ML to optimize your SaaS application is not part of your cloud journey, you are in for a BIG surprise. Wasted money, degrading top and bottom-line growth, and a team exhausted by the vicious loop of triaging SLO violations once a new deployment hits production.

 You will be wasting money as you throw more resources at the problem by simply overprovisioning. But that’s not all! You will also lose valuable development time as you end up troubleshooting performance lags once your code gets deployed in production. Additionally, your shareholders will be unhappy with the bottom-line results as ever-increasing cloud spend demonstrates inefficient management of spending and managing operational costs. Most importantly, your top-line growth will take a hit as customers migrate to your competitors focused on developing customer-centric features and delivering a more reliable SaaS application. 

Only AIOps will address all of these issues. You need to deliver your application with the best user experience at the lowest cost. The only way to do that is automating the tail end of your CI/CD pipeline with Continuous Optimization (CO). By properly using AI to tune the thousands of parameters that affect the performance and cost of your application, you can ensure your app will scale up and down in line with user traffic. Also, your config files will be updated with the optimal settings every time there is a change in the environment around your code being deployed. Automating performance tuning will get your team back to what they love….building new features for your customers. 

Opsani Delivers True Autonomous Ops

Today, most organizations move from traditional static infrastructure, physical systems to a dynamic mixed footprint of on-premise, hybrid-cloud, private and public, and virtualized resources. With this migration, their infrastructure constantly needs to be scaled and reconfigured. These dynamic environments create a spaghetti factory of individual but interdependent systems. Only true autonomous ops can address this complexity at the most granular level. With true autonomous ops, you can ensure interdependent microservices are not creating bottlenecks in the data flow. You need real-time autonomic tuning and predictive learning moving dynamically with the requests from your customers as your traffic scales up and down.  

Implemented correctly, ML can autonomously and continuously tune the parameters around your code that affect the performance and cost of your application. Every time new code is pushed, your cloud provider offers new resources, and at every new middleware library update, your config files are autonomously updated to ensure you are running optimally. This needs to happen where the action is – in real-time. Otherwise, you are just optimizing for a peak load and missing the opportunity to rightsize during idle or low traffic times, creating overprovisioning waste.  

When Opsani was founded, our vision was to eliminate our customers’ worries about performance tuning the thousands of parameters involved in delivering their application with the best user experience at the lowest cost. Manual tuning of microservice architectures is impossible, beyond human scale, with all the thousands of settings required to deliver the application.   

True AIOps is baking in the autonomous capability to discover new services as they are deployed. They use AI to optimize the parameters for delivering the application and then autonomously promote the new configuration to the rest of the cluster.

Our customers understand the value of what Opsani can deliver: automating the tail-end of the CI/CD pipeline with Continuous Optimization (CO), more resilience, more excellent reliability….all at the lowest cost. To learn more about autonomy check our blog The Future is Autonomous, Are You Ready?