The four main business case benefits are: 1) Cloud Cost Savings, 2) Performance Gains, 3) Availability Gains, and 4) Engineering Time Savings. Each area compounds the others, providing measurable improvements in operational efficiency and business outcomes. (Source: Sedai blog, March 26, 2024)
Autonomous cloud management optimizes resource settings (CPU, memory) at the workload level, selects the best mix of infrastructure (e.g., AWS instance types), and maximizes purchasing discounts using ML-based traffic forecasts. Sedai customers have reported up to 50% reduction in cloud costs through autonomous optimization and rightsizing workloads. (Source: Sedai Buyer Personas.pptx)
Performance gains include reduced latency and improved application responsiveness. For customer-facing applications, lower latency can directly impact revenue (e.g., Amazon's benchmark: 100ms improvement = 1% revenue increase). Sedai's autonomous optimization has reduced latency by up to 75% for some customers, enhancing user experience and product quality. (Source: Sedai Buyer Personas.pptx)
Autonomous systems proactively prevent issues such as out-of-memory errors that could cause revenue-impacting incidents. Improvements are measured using metrics like Failed Customer Interactions (FCIs) or error rates. Sedai's proactive issue resolution has reduced failed customer interactions by up to 50%, ensuring higher availability and reliability. (Source: Sedai Buyer Personas.pptx)
Autonomous systems act as AI copilots for operations teams (SRE, DevOps, FinOps), automating repetitive actions such as scaling, capacity tweaks, and configuration management. Sedai customers have reported up to 6X productivity gains, freeing engineers to focus on innovation rather than manual operational tasks. (Source: Sedai Buyer Personas.pptx)
Key metrics include: percentage cloud cost savings, latency reduction, improvements in availability (e.g., reduction in failed customer interactions or error rates), and engineering time reclaimed. These metrics can be projected using pilot exercises or benchmarks from similar organizations. Sedai provides tools and guidance to help teams quantify these benefits for their environment. (Source: Sedai blog, March 26, 2024)
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March 26, 2024
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Operations teams looking to adopt autonomous cloud management need to show business value to get buy-in from leadership even when the technology "just makes sense". Gartner called out this potential disconnect with business as an obstacle to the adoption of autonomous workload optimization in their report "Hype Cycle for Monitoring and Observability, 2023" (written by Pankaj Prasad and Padraig Byrne).
A strong autonomous cloud business case covers cost savings, performance gains, availability improvements, and engineering time reclaimed — and each area compounds the others. Book a demo to quantify all four for your environment.
Here are four areas to consider including in your autonomous business case and ROI:
The most readily understood area of value. Business case benefits can be projected at three levels:
a) 𝗪𝗼𝗿𝗸𝗹𝗼𝗮𝗱: gains from finding the optimal resource settings (e.g., CPU, memory) that meet performance needs at least cost
b) 𝗜𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲: optimizing the mix of instance types (e.g., M5, C5 or R5 for AWS) based on this updated workload configuration
c) 𝗣𝘂𝗿𝗰𝗵𝗮𝘀𝗶𝗻𝗴: optimizing discounts based on ML-based traffic forecasts
Projections for each of these can be established by completing a pilot exercise on a representative set of workloads (later stage business case) or using general ranges from other companies' experiences (early stage).
Autonomous systems can also uncover latency gains by optimizing resources (somewhat of a mirror image to cost above). However, identifying the subset of services where latency impacts revenue is key when calculating the business value of latency gains. Examples:
a) 𝗖𝘂𝘀𝘁𝗼𝗺𝗲𝗿 𝗳𝗮𝗰𝗶𝗻𝗴 𝗮𝗽𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀 in industries like e-commerce and SaaS where customer experience and revenue are linked (over time) to page load speed.
b) 𝗣𝗿𝗼𝗰𝗲𝘀𝘀𝗶𝗻𝗴-𝗶𝗻𝘁𝗲𝗻𝘀𝗶𝘃𝗲 𝘄𝗼𝗿𝗸𝗹𝗼𝗮𝗱𝘀 where lower latency improves product quality (e.g., faster security scans) or time to revenue improves (e.g., drug discovery).Projecting the revenue impact of latency is also needed and reference to benchmarks (e.g., Amazon's 100ms = 1% of revenue) or internal data is needed.
Autonomous systems also optimize availability by preventing issues (e.g., out of memory) that could trigger revenue-impacting incidents (e.g. payments in e-commerce). Projecting improvements on a metric tied to customer experience (like Failed Customer Interactions (FCIs)) is ideal but errors can be used as a proxy, assuming you've started measuring availability on a request basis. As with performance, the value of reducing this metric is needed. Inhouse estimates or benchmarks can be used.
Time savings accrue as autonomous systems act as AI copilots for operations teams (e.g., SRE, DevOps, FInOps). A simple approach is to determine the volume of potential actions that the system will take and the typical time today needed to perform them.
If your team is constantly rewriting rules to keep up with workload changes, there is a better way. Book a demo to see what autonomous cloud management looks like in production.
Book a Sedai demo to reduce cloud costs, improve performance, and unlock measurable operational efficiency.
In the chart below we show how these four areas can be used to define a set of metrics that can be used to calculate the overall benefits in an autonomous cloud management business case:
To get help on creating an autonomous business case based on this approach and tailored for your organization, please contact us via website chat or our contact us page.