I believe that a thin, robust, and fully utilized infrastructure has always been a hallmark of high professionalism. Over the years, I’ve gathered eclectic knowledge and tricks on cost-saving, whether in infrastructure or remote SaaS.

Here are my five essential tips for mastering FinOps:

1. Advanced Autoscaling: Leveraging Cloud Flexibility

Autoscaling enables us to fully exploit cloud flexibility, dynamically adjusting resources in real time based on demand. At Papaya, we go beyond basic metrics (CPU, memory, I/O), incorporating complex triggers such as queue size and requests per second for sophisticated scaling strategies. We fine-tune configurations using historical data and real-time monitoring by aligning autoscaling policies with application performance requirements and cost constraints and utilizing either cloud-provider autoscaling services or third-party tools. This approach ensures maximum cost-efficiency, with complex metrics offering a more accurate reflection of the system’s state.

Real World Example:
During a high-traffic event, our system scales up based on CPU usage and monitoring an upcoming event that will increase our requests. This nuanced approach allows us to allocate resources precisely when needed and not after effect, as scaling can take minutes. This approach helps us maintain performance without over-provisioning and incurring unnecessary costs. Conversely, as traffic subsides, resources are scaled down efficiently, ensuring we only pay for what we use.

2. Rightsize Resources: Avoiding Overprovisioning

For less-elastic infrastructure that can't autoscale, we conduct thorough utilization analysis and downscale underused resources. This continuous optimization process ensures full utilization without succumbing to the fear of overprovisioning. We prioritize actual needs over hypothetical peaks, reviewing and adjusting resource allocations to align with demand fluctuations. We gain deep insights into resource utilization patterns by leveraging cloud provider tools or third-party solutions. Additionally, we set up alerts to notify us of underutilized resources or potential cost-saving opportunities. This meticulous approach applies to all tiers: compute, storage, APM, metric providers, logs, and lifecycle stages of cloud resources.

Real World Example:
By analyzing utilization data, we might identify an AWS RDS instance consistently operating at 20% capacity. We then downsize to a more appropriate instance type, maintaining performance while reducing costs. This practice, applied across the board, ensures optimal resource allocation and cost efficiency.

3. Reserved Instances and Savings Plans: Committing to Save

Committing to usage through reserved instances or savings plans allows us to achieve substantial savings for predictable workloads. By committing to one or three-year terms, we significantly reduce expenses. Additionally, negotiating private pricing for high-volume usage or specific cloud features can lower monthly costs. For instance, securing private pricing for CDNs, outgoing traffic, and support can yield considerable savings.

We analyze usage patterns to identify resources suitable for reserved instances or savings plans, choosing commitment terms based on anticipated usage duration. Regularly evaluating our reserved instance or savings plan utilization ensures we maximize benefits. We explore options for modifying or exchanging reserved instances if our needs change and consider combining reserved instances with on-demand instances to maintain flexibility.

Real World Example:
By committing to a three-year savings plan for our most utilized compute resources, we reduced costs by 40%. Simultaneously, negotiating private pricing for our CDN usage and outgoing traffic resulted in additional monthly savings. This strategic approach allows us to balance long-term commitments with the flexibility of on-demand resources.

4. Data Transfer Optimization: Reducing Egress Costs

Minimizing data egress costs is a crucial aspect of our cost optimization strategy. With 1 million mobile requests per second resulting in significant cross-inter-AZ traffic, we strive to keep traffic within the virtual network (VNet) whenever possible. Utilizing content delivery networks (CDNs) and private endpoints and reducing the number of availability zones to minimize inter-AZ costs while maintaining good redundancy helps us lower data transfer fees.

By understanding our cloud provider’s data transfer pricing model, we identify potential cost drivers and compress data before transfer to reduce bandwidth consumption. Caching frequently accessed data minimizes the need for repeated transfers. We are considering using data transfer optimization services offered by our cloud provider or exploring third-party solutions.

5. Self-Management: Controlling Our Infrastructure

Finally, taking control of our infrastructure through self-management provides greater cost control and flexibility than managed services. By hosting our own Infrastructure-as-a-Service (IaaS) applications like Prometheus and the ELK stack, we carefully evaluate the trade-offs between self-management and managed services for various infrastructure components. Considering the expertise and resources required for self-management, we implement robust monitoring and management practices to ensure the reliability and performance of our self-hosted applications. By leveraging automation tools to streamline deployment, scaling, and maintenance tasks, we continually assess the cost-effectiveness of self-management as our needs evolve.

Real World Example:
By self-managing Prometheus and the ELK stack, we avoid the recurring costs of managed services while retaining full control over customization and scaling. However, we balance this with the need for skilled personnel and robust automation to maintain high availability and performance. Regular evaluations help us decide when self-management is advantageous and when transitioning to managed services might be more cost-effective.

Remember, FinOps is an ongoing journey. Monitor, analyze, and optimize your cloud usage to achieve cost efficiency and drive business value.

‍