Bottleneck #04: Cost Efficiency

Understand primary cost drivers

It is important to start with identifying the primary cost drivers. First, the cost optimization team should collect relevant invoices – these can be from cloud provider(s) and SaaS providers. It is useful to categorize the costs using analytical tools, whether a spreadsheet, a BI tool, or Jupyter notebooks. Analyzing the costs by aggregating across different dimensions can yield unique insights which can help identify and prioritize the work to achieve the greatest impact. For example:

Application/system: Some applications/systems may contribute to more costs than others. Tagging helps associate costs to different systems and helps identify which teams may be involved in the work effort.

Compute vs storage vs network: In general: compute costs tend to be higher than storage costs; network transfer costs can sometimes be a surprise high-costing item. This can help identify whether hosting strategies or architecture changes may be helpful.

Pre-production vs production (environment): Pre-production environments’ cost should be quite a bit lower than production’s. However, pre-production environments tend to have more lax access control, so it is not uncommon that they cost higher than expected. This could be indicative of too much data accumulating in non-prod environments, or even a lack of cleanup for temporary or PoC infrastructure.

Operational vs analytical: While there is no rule of thumb for how much a company’s operational systems should cost as compared to its analytical ones, engineering leadership should have a sense of the size and value of the operational vs analytical landscape in the company that can be compared with actual spending to identify an appropriate ratio.

Service / capability provider: ​​Across project management, product roadmapping, observability, incident management, and development tools, engineering leaders are often surprised by the number of tool subscriptions and licenses in use and how much they cost. This can help identify opportunities for consolidation, which may also lead to improved negotiating leverage and lower costs.

The results of the inventory of drivers and costs associated with them should provide the cost optimization team a much better idea what type of costs are the highest and how the company’s architecture is affecting them. This exercise is even more effective at identifying root causes when historical data is considered, e.g. costs from the past 3-6 months, to correlate changes in costs with specific product or technical decisions.

Identify cost-saving levers for the primary cost drivers

After identifying the costs, the trends and what are driving them, the next question is - what levers can we employ to reduce costs? Some of the more common methods are covered below. Naturally, the list below is far from exhaustive, and the right levers are often very situation-dependent.

Rightsizing: Rightsizing is the action of changing the resource configuration of a workload to be closer to its utilization.

Engineers often perform an estimation to see what resource configuration they need for a workload. As the workloads evolve over time, the initial exercise is rarely followed-up to see if the initial assumptions were correct or still apply, potentially leaving underutilized resources.

To rightsize VMs or containerized workloads, we compare utilization of CPU, memory, disk, etc. vs what was provisioned. At a higher level of abstraction, managed services such as Azure Synapse and DynamoDB have their own units for provisioned infrastructure and their own monitoring tools that would highlight any resource underutilization. Some tools go so far as to recommend optimal resource configuration for a given workload.

There are ways to save costs by changing resource configurations without strictly reducing resource allocation. Cloud providers have multiple instance types, and usually, more than one instance type can satisfy any particular resource requirement, at different price points. In AWS for example, new versions are generally cheaper, t3.small is ~10% lower than t2.small. Or for Azure, even though the specs on paper appear higher, E-series is cheaper than D-series – we helped a client save 30% off VM cost by swapping to E-series.

As a final tip: while rightsizing particular workloads, the cost optimization team should keep any pre-purchase commitments on their radar. Some pre-purchase commitments like Reserved Instances are tied to specific instance types or families, so while changing instance types for a particular workload could save cost for that specific workload, it could lead to part of the Reserved Instance commitment going unused or wasted.

Using ephemeral infrastructure: Frequently, compute resources operate longer than they need to. For example, interactive data analytics clusters used by data scientists who work in a particular timezone may be up 24/7, even though they are not used outside of the data scientists’ working hours. Similarly, we have seen development environments stay up all day, every day, whereas the engineers working on them use them only within their working hours.

Many managed services offer auto-termination or serverless compute options that ensure you are only paying for the compute time you actually use – all useful levers to keep in mind. For other, more infrastructure-level resources such as VMs and disks, you could automate shutting down or cleaning up of resources based on your set criteria (e.g. X minutes of idle time).

Engineering teams may look at moving to FaaS as a way to further adopt ephemeral computing. This needs to be thought about carefully, as it is a serious undertaking requiring significant architecture changes and a mature developer experience platform. We have seen companies introduce a lot of unnecessary complexity jumping into FaaS (at the extreme: lambda pinball).

Incorporating spot instances: The unit cost of spot instances can be up to ~70% lower than on-demand instances. The caveat, of course, is that the cloud provider can claim spot instances back at short notice, which risks the workloads running on them getting disrupted. Therefore, cloud providers generally recommend that spot instances are used for workloads that more easily recover from disruptions, such as stateless web services, CI/CD workload, and ad-hoc analytics clusters.

Even for the above workload types, recovering from the disruption takes time. If a particular workload is time-sensitive, spot instances may not be the best choice. Conversely, spot instances could be an easy fit for pre-production environments, where time-sensitivity is less stringent.

Leveraging commitment-based pricing: When a startup reaches scale and has a clear idea of its usage pattern, we advise teams to incorporate commitment-based pricing into their contract. On-demand prices are typically higher than prices you can get with pre-purchase commitments. However, even for scale-ups, on-demand pricing could still be useful for more experimental products and services where usage patterns have not stabilized.

There are multiple types of commitment-based pricing. They all come at a discount compared to the on-demand price, but have different characteristics. For cloud infrastructure, Reserved Instances are generally a usage commitment tied to a specific instance type or family. Savings Plans is a usage commitment tied to the usage of specific resource (e.g. compute) units per hour. Both offer commitment periods ranging from 1 to 3 years. Most managed services also have their own versions of commitment-based pricing.

Architectural design: With the popularity of microservices, companies are creating finer-grained architecture approaches. It is not uncommon for us to encounter 60 services at a mid-stage digital native.

However, APIs that aren’t designed with the consumer in mind send large payloads to the consumer, even though they need a small subset of that data. In addition, some services, instead of being able to perform certain tasks independently, form a distributed monolith, requiring multiple calls to other services to get its task done. As illustrated in these scenarios, improper domain boundaries or over-complicated architecture can show up as high network costs.

Refactoring your architecture or microservices design to improve the domain boundaries between systems will be a big project, but will have a large long-term impact in many ways, beyond reducing cost. For organizations not ready to embark on such a journey, and instead are looking for a tactical approach to combat the cost impact of these architectural issues, strategic caching can be employed to minimize chattiness.

Enforcing data archival and retention policy: The hot tier in any storage system is the most expensive tier for pure storage. For less frequently-used data, consider putting them in cool or cold or archive tier to keep costs down.

It is important to review access patterns first. One of our teams came across a project that stored a lot of data in the cold tier, and yet were facing increasing storage costs. The project team did not realize that the data they put in the cold tier were frequently accessed, leading to the cost increase.

Consolidating duplicative tools: While enumerating the cost drivers in terms of service providers, the cost optimization team may realize the company is paying for multiple tools within the same category (e.g. observability), or even wonder if any team is really using a particular tool. Eliminating unused resources/tools and consolidating duplicative tools in a category is certainly another cost-saving lever.

Depending on the volume of usage after consolidation, there may be additional savings to be gained by qualifying for a better pricing tier, or even taking advantage of increased negotiation leverage.

Prioritize by effort and impact

Any potential cost-saving opportunity has two important characteristics: its potential impact (size of potential savings), and the level of effort needed to realize them.

If the company needs to save costs quickly, saving 10% out of a category that costs $50,000 naturally beats saving 10% out of a category that costs $5,000.

However, different cost-saving opportunities require different levels of effort to realize them. Some opportunities require changes in code or architecture which take more effort than configuration changes such as rightsizing or utilizing commitment-based pricing. To get a good understanding of the required effort, the cost optimization team will need to get input from relevant teams.

Figure 2: Example output from a prioritization exercise for a client (the same exercise done for a different company could yield different results)

At the end of this exercise, the cost optimization team should have a list of opportunities, with potential cost savings, the effort to realize them, and the cost of delay (low/high) associated with the lead time to implementation. For more complex opportunities, a proper financial analysis needs to be specified as covered later. The cost optimization team would then review with leaders sponsoring the initiative, prioritize which to act upon, and make any resource requests required for execution.

The cost optimization team should ideally work with the impacted product and platform teams for execution, after giving them enough context on the action needed and reasoning (potential impact and priority). However, the cost optimization team can help provide capacity or guidance if needed. As execution progresses, the team should re-prioritize based on learnings from realized vs projected savings and business priorities.

Federate accountability for cost management

For the reduce phase, we recommended putting together a cost optimization team that works with relevant teams to execute on prioritized cost-saving opportunities. To sustain the optimized cost levels, accountability over cost management needs to shift from that cost optimization team to the respective teams.

Most startups we have worked with have adopted the product team model (including platform engineering product teams), which means many of their teams have a good idea of what systems they own. A product team owns the custom software it needs to run its product, and any unique third-party systems that are required for their functionality. A platform team owns the systems related to the capability they provide. This might be technical capabilities such as the CI/CD or observability, or it might be a reusable business capability, such as payments.

However, even in well-organized start-ups, some systems become orphaned over time, sometimes silently, through organization restructuring and organic attrition.

To validate how much clarity teams have over their ownership boundary, the startup can review the systems they have and existing ownership assignments. In many organizations, this information would already be maintained as a durable asset, such as a service catalog, which should be reviewed with teams to validate its currency.

Organizations without such a service catalog could start by taking a snapshot of their technology landscape (listing out or visualizing systems, for example with the C4 model) and working with teams to tag each component with its respective owner. In the longer term, it would be best to move this towards a living artifact like a service catalog (even a lightweight one – a document instead of a full-blown portal) as well.

Ideally, through that process, any identified orphan systems will have been assigned an owner. In reality, it is reasonable to prioritize based on the business criticality, cost impact, or other dimensions.

Team leaders (engineering manager, tech lead, product lead) will then need to be held accountable over costs of the systems their teams own. This could initially be an expectation that they review costs at a reasonable cadence - which can be weaved into their existing rituals (monthly sync-ups, quarterly review, etc.). Eventually, cost measures should be embedded into the KPIs of these teams.

Putting cost front and center builds organizational muscle and helps to send the message that everybody should think about the cost implications of their decisions, and that managers are accountable for it, the same way we think about security and quality.

However, just like with security and quality, the company needs to be aware of the second order effects of cost considerations. Teams should not be frugal to a fault; they can consult relevant company leaders to help them make decisions on cost. Often, at a startup it makes sense for teams to take on tech debt that incur additional costs if it means faster time to market, especially for experimental features. The trick is to have the discipline to spot when costs are trending up and course correct.

While it is critical to federate accountability to team level, some cost management actions, such as negotiating bulk purchase commitments, and optimizing overall technology portfolio, can only be done optimally by taking into account broader organization context. Therefore, cost accountability also needs to be assumed across teams, at product group level.

Make cost visible

To support the desired level of federated accountability, the cost of running systems need to be reported and attributed to the owning organization levels (teams, product groups). ​​The way to achieve this depends on the type of service. For example, cloud infrastructure or services can be attributed to their owners with a tag hierarchy, whereas workloads running on shared Kubernetes clusters can be attributed back with labels, namespaces, or service names (Kubernetes-specific cost monitoring tools allow breaking down cost of shared Kubernetes clusters to teams based on labels, namespaces, and service names).

While perfect attribution of every service or infrastructure component to the product or team is hard to achieve, it is critical to keep the reports discoverable, understandable, and actionable, allowing engineers to understand the cost implications of their technical decisions and leaders to see if any course-correction is needed.

The level of detail in these reports is important. When the audience is closer to the team level, it's helpful to have finer-grained data and to have the reports updated more frequently. Teams make decisions that affect costs during their day-to-day development work, so having reports on a bi-weekly or monthly basis gives them a faster indication to change direction compared to quarterly reports.

For leaders above team level, the focus is on detecting larger trends and understanding if any changes in contract structures or technology strategy is needed. The reports may aggregate costs across different dimensions such as the ones covered in Understand primary cost drivers, but the report generally does not need to be as frequently-updated as team-level reports.

Make it easier for teams to do the right thing

An often overlooked part of cost management is making it easier for people to do what is expected of them, which is to be accountable for cost for their part of the tech landscape. Rather than create a heavy handed governance process, seek to enable desired behavior and outcomes. If the easiest way is the most cost efficient, technologists will naturally follow best practices.

To create an environment where it is easy to achieve the desired outcomes, we can use nudges. Richard Thaler discusses this topic in detail in his book Nudge. One example he uses that is good analogy to limitless self-service compute is: “Trayless cafeterias. Cafeteria managers have been taking a keen interest in reducing food waste. Seeing how easy it is to load up a tray with extra food that often goes uneaten and extra napkins that go unused, curious managers and students at Alfred University in New York tested a trayless policy over two days. When trays weren’t offered, food and beverage waste dropped between 30 and 50 percent!”

The Backstage developer portal is a good example of a tool that nudges people to do the right thing. The use of templates nudges engineers to the Golden Path (Spotify’s version of a paved road). It also has scorecards on documentation to nudge engineers to add attributes that make them more useful and readable.

Here are examples on applying nudges to improve cost efficiency:

Review and govern technology portfolio

An organization’s technology portfolio consists of its in-house systems and third-party tools, languages, and frameworks. The more items in the portfolio, the greater the scope of responsibility for the organization, resulting in higher costs both in terms of financial investment and cognitive burden. While each single item in the technology portfolio was initially introduced for a specific reason, these reasons can become invalid or irrelevant over time. Governing the technology portfolio in a way that reduces fragmentation therefore helps reduce costs.

The first step to governing a startup’s technology portfolio is to take an inventory of the systems it has and the capabilities of each. In doing this, it becomes easier to identify overlapping functionality and identify redundant systems or tools. For instance, multiple systems within the portfolio may have their own implementations of a CMS, or the startup may discover that across teams, there are multiple tools in use to provide CI/CD capabilities.

Once the latest state of the portfolio is well-understood, our teams frequently rely on a couple of tools to help with portfolio optimization.

A Wardley map enables engineering leaders to visualize technology capabilities supporting the business, how visible they are to users/customers, and the stage of evolution they are in (whether they are experimental, or fairly mature but custom/specific to the business, or a commodity). This opens up conversations about how differentiating these capabilities are to the business and whether any shifts in investment is needed. The Wardley map has helped teams in reviewing their portfolio and identifying capabilities that were developed and still maintained in-house, even though a lower-cost “buy” option has become more compelling over time.

Another tool we like is a technology radar for languages and third-party tools and platforms.

Technology Radar itself is a Thoughtworks industry publication, but the model has been used by multiple organizations such as Zalando to communicate a consolidated landscape of languages and tools. By communicating to engineers within the organization what the safe language/tool choices are, what’s not recommended for use, and what technologies are in various stages of experimentation, it nudges engineers along the desired path, discourages fragmentation, and in turn helps reduce costs. There are multiple ways to get going with a custom technology radar, such as through the Build Your Own Radar toolkit or the Backstage Tech Radar Plugin.

When considering changes to the technology portfolio or any major architectural changes, it is important to conduct a financial analysis on the potential return on investment (ROI) with your financial partner. The main considerations in the financial analysis are the cost of labor of implementing the change, the cost of the tool itself if being replaced, and the potential efficiency gains or losses because of the new approach, and the cost of maintaining the status quo. This analysis has to span the organization in terms of labor and impact as it isn’t just the team that is implementing the change that could be impacted.

A client of ours considered moving to a new observability tool as the cost variability of the current tool was worrisome and the tool was cumbersome to manage. After careful consideration, which included a quote from a new vendor, estimate of expected labor costs, and analysis of cost reduction in the timeframe they chose, the client decided to keep with the current tool as the ROI was negative.

Optimize rates

Cloud and SaaS service providers reward their customers for commitment (e.g. reserved instance or savings plan purchases) and scale. However, bulk purchasing plans can become liabilities if actual usage doesn’t match estimates. Longer term commitments represent increased risk, since a company’s system, users, and market opportunities evolve and change faster than its contract terms. One client of ours had signed up for a three year commitment with AWS (compute savings plan) but then a year later decided to decommission one part of their system, which resulted in the committed plan being underutilized.

The reverse is also true: if the company always under-commits, it is always paying a higher price for the resources. This requires a careful balance and continued conversation with business on how they want to manage this risk.

Most SaaS products assign companies an account manager who should be able to tell them what kind of contract structures are available to them to get the best price. It never hurts to ask for a discount. Some SaaS providers give discounts in return for intangible benefits, such as being a referenceable client or providing feedback on beta versions of their product. We recommend to regularly meet with the account manager as they keep track of the company’s usage and will notify them if there are better options.

Consolidating responsibility over rate optimizations for the organization (or department) enables a more efficient purchasing strategy than federating responsibility across multiple teams. The scale of the organization may justify investment in a FinOps team to manage the pricing models which best enable economies of scale across the company.