Cost optimization interviews reward a specific kind of thinking that’s easy to underestimate: knowing the levers isn’t enough, you need to know which lever actually matters for a given situation, and — just as important — what you’d give up by pulling it. Anyone can say “use reserved capacity.” Fewer people can say when that’s genuinely the wrong call.
Split into Beginner (1–17), Intermediate (18–35), and Advanced (36–50), written the way you’d actually talk it through in a room — the reasoning behind each answer, not a pricing-page summary.
One framing worth carrying through the whole list: cost optimization in Azure SQL is rarely about finding one big lever. It’s usually a handful of smaller, unglamorous decisions — right-sizing, cleaning up unused resources, matching the storage tier to actual access patterns — that compound. The strongest answers below reflect that, rather than treating cost optimization as a single silver-bullet feature.
Beginner Level
1. What are the two main purchasing models for Azure SQL Database, and how do they affect cost? DTU-based, which bundles CPU, memory, and I/O into a single blended unit at a fixed price per tier, and vCore-based, which lets you choose CPU cores and memory independently and unlocks Azure Hybrid Benefit for reusing existing SQL Server licenses. vCore is generally the more cost-flexible model for production workloads specifically because you can scale each dimension separately instead of paying for a bundle you might only need part of.
2. What is Azure Hybrid Benefit, and how does it reduce Azure SQL costs? It lets you apply existing on-prem SQL Server licenses (with active Software Assurance) toward the compute cost of an Azure SQL vCore-based database, substantially reducing what you’d otherwise pay for compute — often cited as up to roughly 40% savings, though the exact number depends on your specific licensing and tier. It’s only available on the vCore model, not DTU.
3. What is a reserved capacity (reserved instance) purchase, and why does it save money? Committing to a specific vCore amount for a 1-year or 3-year term, paid upfront or monthly, in exchange for a meaningfully lower rate than pay-as-you-go pricing — the trade-off is losing some flexibility (you’re committed to that capacity for the term) in exchange for a real discount, generally the right call for steady-state, predictable production workloads rather than anything spiky or short-lived.
4. What is the serverless compute tier, and when does it save money compared to a provisioned tier? Serverless auto-scales compute based on actual load and can auto-pause during genuine inactivity, billing per-second for what’s actually used rather than a fixed reserved amount. It saves money specifically for workloads with unpredictable, intermittent usage — dev/test databases, new low-traffic applications — where a provisioned tier would mean paying for capacity that sits idle much of the time.
5. Why might serverless NOT save money for a steady, always-on production workload? If a database is under consistent, predictable load most of the time, serverless’s per-second billing for that consistent usage often works out more expensive than a reserved or even standard provisioned tier — serverless’s cost advantage comes specifically from variability and idle time, which a steady-state workload doesn’t have much of.
6. What is an elastic pool, and how does it help control costs for many databases? A shared resource pool that multiple databases draw from instead of each having its own fixed, separately-paid-for allocation — cost-effective specifically when you have many databases with unpredictable, non-overlapping usage spikes, since you’re paying for the pool’s shared peak capacity rather than the sum of every individual database’s peak.
7. What’s the risk of leaving a database provisioned at a higher service tier than it actually needs? You’re simply paying for compute and capability the workload isn’t using — a very common and easy-to-overlook cost drain, especially on databases that were sized generously “to be safe” during initial setup and never revisited once real usage patterns became clear.
8. What is Azure Advisor, and how does it help with cost optimization specifically? Advisor includes a cost recommendations category that flags databases with sustained low utilization and suggests downsizing, or identifies workloads that would benefit from reserved capacity based on consistent historical usage — essentially surfacing savings opportunities you might not think to go looking for manually.
9. Does Azure SQL Database storage cost scale linearly with how much data you actually store, or with the maximum size you’ve configured? Generally, storage cost is based on actual data consumed, not the maximum size limit you’ve configured for the database — though it’s still worth understanding your specific tier’s billing model, since some bundled-storage scenarios (within a certain service tier’s included storage) behave slightly differently than pure consumption-based billing.
10. What is backup storage cost, and how can it add up unexpectedly? Backup storage beyond what’s included with your service tier is billed separately, and choosing geo-redundant storage (GRS) roughly doubles that footprint compared to locally redundant storage (LRS), since it’s maintaining a full copy in another region — a cost factor that’s easy to overlook since it’s not the headline compute price people usually focus on first.
11. How does Long-Term Retention (LTR) affect cost, and why does it deserve its own review? LTR backups are billed based on how much you retain and for how long, separately from standard short-term backup retention — an aggressive, unreviewed multi-year LTR policy applied broadly across many databases (rather than only where genuinely required for compliance) can become a meaningful, often-overlooked cost line over time.
12. What’s a simple first step for reviewing Azure SQL costs across a subscription? Azure Cost Management + Billing gives a breakdown of spend by resource, letting you quickly identify which specific databases are driving the largest portion of the bill — a natural starting point before diving into optimizing any individual database’s configuration.
13. Can you scale an Azure SQL Database down during off-peak hours to save money, and is that a good idea? Yes, it’s technically possible via scripting or automation (PowerShell, Azure Automation, Logic Apps) to scale down during predictable low-traffic windows and back up before peak — a legitimate cost-saving pattern for workloads with a genuinely predictable daily/weekly usage cycle, though it adds operational complexity and isn’t worth it for workloads without a clear, reliable low-usage window.
14. What’s the cost difference between General Purpose and Business Critical tiers, and what are you paying extra for? Business Critical costs meaningfully more, largely because you’re paying for additional, continuously running, fully synchronized replicas with local storage for faster HA failover — General Purpose’s cheaper price reflects its simpler compute-storage-reattachment HA model rather than maintaining that many live redundant copies.
15. Is it cheaper to run many small, separate Azure SQL databases, or consolidate them into an elastic pool? It depends entirely on their usage patterns — if they have genuinely unpredictable, non-overlapping peaks, pooling is usually cheaper since you’re sharing capacity rather than each one separately provisioning for its own worst case. If most of them are consistently near-idle or consistently near-peak at the same time, the savings from pooling are smaller or can even reverse.
16. What’s the cost implication of choosing Hyperscale for a database that doesn’t actually need very large storage? Hyperscale is generally priced competitively for what it offers, but its real value proposition is for large or fast-growing databases where its storage architecture and fast backup/restore genuinely matter — choosing it for a small, simple database without that need means you might not be getting proportional value for whatever cost difference exists compared to a simpler tier that would serve the same workload just as well.
17. What’s a simple habit that helps prevent cost creep over time on an Azure SQL environment? Periodically reviewing actual resource utilization against provisioned capacity — not just at initial setup, but on a recurring basis, since workloads change, initial sizing assumptions age, and a database that was correctly sized a year ago may no longer be, in either direction.
Intermediate Level
18. How would you quantify whether a specific database is a good candidate for downsizing, rather than just eyeballing it? I’d pull sustained CPU/DTU utilization trends over a meaningful window (weeks, not days, to capture real variability including any periodic peaks) via Azure Monitor metrics or sys.dm_db_resource_stats, and look specifically for consistently low peak utilization, not just low average — a database that averages 20% but regularly spikes to 90% during a critical nightly batch job is a very different downsizing candidate than one that never exceeds 30% even during its busiest periods.
19. What’s the difference in cost/benefit trade-off between reserved capacity and Azure Hybrid Benefit, and can you use both together? They address different cost levers and can generally be combined — reserved capacity discounts the compute rate in exchange for a term commitment, while Azure Hybrid Benefit reduces the licensing-inclusive portion of that rate by applying existing licenses you already own. Using both together on a steady-state, license-eligible production workload typically produces the deepest combined discount, since they’re not mutually exclusive savings mechanisms.
20. How would you decide between 1-year and 3-year reserved capacity terms? It comes down to how confident you are the workload (and its current sizing) will remain stable over that horizon — a 3-year term offers a deeper discount but locks in an assumption about capacity needs further into the future, which is riskier for a growing or changing application than for a mature, genuinely stable one. I’d lean toward 1-year terms for anything with real uncertainty about future sizing, and reserve 3-year commitments for workloads with a demonstrated, multi-year track record of stability.
21. What’s the cost risk of over-provisioning an elastic pool’s total eDTU/vCore capacity relative to what member databases actually need? You’re paying for the pool’s total provisioned capacity regardless of whether member databases are using it — an elastic pool sized generously “to be safe” without basing that sizing on actual aggregate peak usage data ends up being just as wasteful as an over-provisioned single database, just spread across more databases, which can make the waste less obvious at a glance.
22. How would you use Query Performance Insight or Query Store data as part of a cost optimization review, not just a performance review? A query consuming disproportionate CPU or I/O isn’t just a performance concern — it’s directly driving how much compute capacity the database needs to provision for, meaning fixing an inefficient query can be a genuine cost lever, sometimes allowing a downsize that wouldn’t have been safe with the unoptimized query still running. I’d specifically look at whether the top resource-consuming queries have obvious tuning opportunities before concluding a database’s current tier is truly necessary.
23. What’s the cost implication of enabling verbose diagnostic logging categories (like full QueryStoreRuntimeStatistics) across a large fleet, and how do you manage it? Log Analytics ingestion is billed per GB, and chatty diagnostic categories on busy databases can meaningfully add up across a large fleet — I’d be deliberate about which categories are genuinely needed at full fidelity in production versus which could run at reduced scope or only in non-prod, and consider Basic Logs tier for high-volume, rarely-queried categories rather than defaulting every database to full Analytics-tier logging without reviewing the actual ingestion cost it’s generating.
24. How would you evaluate whether serverless is actually cheaper than provisioned compute for a specific borderline workload, rather than assuming based on general guidance? I’d model both options against the database’s actual historical usage pattern — serverless’s per-second billing rate against the real hours of active use plus any auto-pause savings, compared directly against the provisioned tier’s flat rate — using Azure’s pricing calculator with real captured utilization data, rather than relying on the general “serverless is for spiky workloads” guidance alone, since the actual breakeven point depends on the specific numbers for that specific database.
25. What’s the relationship between right-sizing and read scale-out from a cost perspective? If a workload’s peak resource consumption is being driven partly by reporting/analytical queries mixed in with the primary OLTP traffic, routing that reporting traffic to a read-only secondary (already included at no extra cost on Business Critical/Premium tiers) can reduce the primary’s peak load enough to justify a smaller primary tier — meaning read scale-out can be a genuine cost lever, not just a performance one, by changing what the primary actually needs to be sized for.
26. How would you approach cost optimization for backup storage specifically, without compromising your actual recovery requirements? I’d separate genuine compliance-driven Long-Term Retention needs from default settings nobody’s revisited, right-size the LTR retention granularity (weekly vs monthly vs yearly) to what’s actually required rather than defaulting to the most generous option, and evaluate whether GRS is genuinely needed for every database’s backup storage or whether some lower-priority, non-critical databases could reasonably use LRS instead — while being careful this is a deliberate, documented trade-off against DR requirements, not just a cost cut made without considering what it gives up.
27. What’s the cost consideration around database consolidation — combining several smaller, separately-provisioned databases into fewer, larger ones? Consolidation can reduce total cost when the combined workload doesn’t actually need the sum of what each database was separately provisioned for — similar reasoning to elastic pooling, but at a schema/database-merge level rather than a shared-capacity level. The trade-off is added complexity (shared blast radius if one workload has a problem, potential noisy-neighbor effects within the same database) that needs to be weighed against the savings, not assumed to be free.
28. How would you handle cost optimization for non-production (dev/test) environments specifically, differently from production? Non-prod environments are usually the easiest, lowest-risk place to find real savings — smaller service tiers matching genuinely lighter dev/test load, serverless with auto-pause for environments that sit idle outside working hours, and shorter backup retention where compliance doesn’t require matching production’s retention policy. I’d treat non-prod cost optimization as lower-hanging fruit than production, precisely because the risk of a slightly-too-aggressive cost cut is much lower there.
29. What’s a cost-related reason to periodically review Automatic Tuning’s applied actions, beyond just performance concerns? An automatically created index that isn’t delivering proportional read benefit is adding write overhead (and, at scale, potentially additional storage cost) without a corresponding return — while Automatic Tuning is designed to self-correct for pure performance regressions, periodically reviewing the cumulative index footprint it’s built up over time is still worth doing from a cost/efficiency lens, not just a pure performance lens.
30. How would you decide whether a specific database genuinely needs Business Critical tier, given its higher cost, versus General Purpose? I’d tie the decision explicitly to the actual RTO requirement and real read-scale-out need, not general “this feels important” instinct — if the workload’s actual failover time tolerance is genuinely served by General Purpose’s HA model, and the database doesn’t need a free read-only replica for reporting offload, Business Critical’s extra cost may not be buying anything the business actually requires, and General Purpose plus a well-designed DR strategy might meet the real requirement at meaningfully lower cost.
31. What’s the cost impact of choosing geo-redundant backup storage (GRS) across an entire large fleet by default, without differentiating by database criticality? GRS roughly doubles backup storage cost compared to LRS for every database it’s applied to — applying it uniformly across a large fleet, including lower-criticality databases that don’t genuinely need cross-region DR protection, means paying that doubled cost broadly for protection that a meaningful portion of the fleet doesn’t actually require, which is exactly the kind of blanket-default cost creep worth reviewing and tiering by actual criticality instead.
32. How would you approach cost optimization when a team resists downsizing a database out of fear of a repeat of a past performance incident? I’d validate the actual data behind the downsizing recommendation directly with them — real sustained utilization trends, including the specific peak periods that might correlate with the past incident — and propose a conservative, monitored downsize with an easy rollback path (scaling back up takes minutes, not a major project) rather than asking them to simply trust the recommendation on faith; the goal is making the downsizing decision evidence-based and reversible, which tends to be a much easier sell than an all-or-nothing framing.
33. What’s the cost consideration around monitoring and diagnostic data itself — can monitoring costs become disproportionate to what they’re protecting? Yes, and it’s a genuinely common blind spot — a large Log Analytics workspace ingesting extensive diagnostic data from a modest-cost database fleet can, if left unreviewed, end up costing more than the databases it’s monitoring, especially once retention and verbose categories accumulate over time. I’d periodically compare monitoring spend against the underlying database spend it’s supporting, and treat a monitoring cost that’s grown disproportionate to the database costs as its own optimization target, not just an assumed cost of doing business.
34. How do you approach the trade-off between cost savings and the operational risk of a more complex cost-optimized architecture (like scheduled scale-down automation)? I’d weigh the actual savings against the operational complexity and failure-mode risk being introduced — an automated scale-down/scale-up schedule adds a new thing that can fail (the scale-up not happening in time before peak traffic, for instance) in exchange for savings that need to be large enough to justify that added operational risk and maintenance burden, not pursued just because the mechanism is technically available.
35. What’s a practical way to build ongoing cost awareness into a DBA team’s regular workflow, rather than treating cost optimization as a one-time project? I’d fold a lightweight utilization and cost review into an existing regular cadence (a monthly ops review, for instance) rather than making it a separate, easily-deprioritized initiative — reviewing Advisor’s cost recommendations, checking for databases that have drifted from their original sizing assumptions, and revisiting reserved capacity coverage as workloads change, treated as routine maintenance rather than a special project that only happens when someone notices the bill is high.
Advanced Level
36. Design a comprehensive, ongoing cost optimization program for a large Azure SQL fleet (500+ databases) across multiple business units. Walk through your reasoning. I’d start with visibility — tagging every database by business unit, environment, and criticality so cost attribution and prioritization are possible at all, since you can’t optimize what you can’t attribute. Then a tiered review cadence: automated weekly reports flagging clear outliers (sustained low utilization, unused elastic pool capacity, LTR policies exceeding documented compliance requirements) for quick action, alongside a quarterly deeper review covering reserved capacity coverage against actual steady-state usage, Azure Hybrid Benefit eligibility gaps, and architecture-level reconsideration (should these five small databases actually be one, does this Business Critical database genuinely still need that tier). I’d assign clear ownership — someone accountable for acting on findings, not just generating reports — since a cost optimization program that only produces recommendations nobody implements isn’t actually saving anything.
37. How would you build a data-driven business case for migrating a large batch of steady-state production databases from pay-as-you-go to 3-year reserved capacity? I’d pull at least 6–12 months of consistent utilization history per database (or database group) to genuinely validate “steady-state” rather than assuming it, calculate the actual reserved capacity discount against that real committed vCore amount versus current pay-as-you-go spend, and explicitly model the downside scenario — what happens to the ROI if a meaningful subset of those databases needs to scale down or gets decommissioned mid-term — so the business case honestly represents the commitment risk, not just the best-case discount, which is what actually earns trust for a multi-year financial commitment recommendation.
38. Explain the cost/architecture trade-off between Hyperscale and General Purpose for a rapidly growing database, considering both current and projected costs. Hyperscale’s pricing needs to be evaluated against not just current size but the growth trajectory — General Purpose’s storage cost scales in a way that becomes less favorable, and its backup/restore time becomes an increasingly real operational cost (in risk and RTO terms, not just direct billing) as the database grows large, while Hyperscale’s architecture is specifically designed to keep those operations fast regardless of size. I’d model the total cost of ownership over a realistic multi-year growth projection, not just today’s price comparison, since choosing based on current size alone can lead to a costly re-platforming migration later if the growth trajectory was foreseeable but not accounted for.
39. How would you approach cost optimization for a multi-tenant SaaS application on Azure SQL, where tenant-level cost attribution matters for the business model? I’d evaluate whether the current architecture (single large database, elastic pool, or per-tenant databases) actually supports meaningful tenant-level cost attribution at all, since that’s often the real blocker to informed optimization in a multi-tenant context — a shared elastic pool makes it hard to know which specific tenant is driving cost, while per-tenant databases make attribution trivial but can lose pooling efficiency for genuinely small tenants. I’d likely recommend a hybrid: pooling small, low-usage tenants together for pooling efficiency while isolating the largest tenants into their own appropriately-sized databases, both for cost attribution clarity and to prevent one large tenant’s usage from driving the whole pool’s sizing.
40. What’s your approach to evaluating whether Azure Hybrid Benefit is actually being fully utilized across a large fleet, and closing any gaps? I’d audit actual license entitlement (SQL Server licenses with active Software Assurance the organization already owns) against what’s currently applied across the vCore fleet, since it’s common for this to drift out of sync — new databases provisioned without the benefit applied, or licenses that could cover more vCores than they’re currently assigned to. This is often one of the highest-value, lowest-risk cost reviews precisely because it’s not asking anyone to accept less capacity or take on new risk — it’s just correctly applying an entitlement the organization already has and is likely already paying for separately.
41. How would you design a cost-aware autoscaling or scheduled-scaling automation for a workload with a genuinely predictable but non-trivial daily usage pattern, and what safeguards would you build in? I’d base the schedule on real captured historical utilization data (not assumption) to define the scale-down and scale-up windows, build in a safety margin so scale-up happens comfortably ahead of when peak load actually begins rather than exactly at the boundary, and include monitoring/alerting specifically on the automation itself — confirming scale operations actually complete successfully — so a failed scale-up isn’t discovered for the first time when the application is already struggling under load it wasn’t sized for. I’d also build in an easy manual override for unexpected off-schedule demand, rather than a rigid schedule that can’t accommodate a genuine, unplanned peak.
42. Explain how you’d quantify the “hidden” cost of technical debt in Azure SQL sizing decisions — databases that have never been revisited since initial (often overly cautious) provisioning. I’d run a fleet-wide sustained-utilization analysis specifically flagging databases whose current provisioned tier is meaningfully larger than what sustained peak usage over a representative window actually justifies, then aggregate the delta between current spend and right-sized spend across the fleet to put a real number on what “never revisited since initial setup” is actually costing, month over month. Framing it as an ongoing, compounding monthly cost — not a one-time number — tends to be far more persuasive for getting organizational buy-in to actually act on it than a single point-in-time savings estimate.
43. How would you approach the cost/performance trade-off when a proposed cost optimization (like downsizing or reducing redundancy) carries a genuine, non-trivial risk to performance or resilience? I’d make the trade-off explicit and quantified rather than framing it as a purely technical decision — here’s the monthly savings, here’s the specific, concrete risk being accepted (a slower failover time, a narrower performance headroom margin), and get that trade-off consciously approved by whoever owns the business risk, rather than a DBA unilaterally deciding the savings are worth it. Cost optimization that trades away resilience or performance silently, without that conversation happening, tends to surface as a much more expensive incident later — the goal is an informed decision, not just a smaller bill.
44. What’s your approach to evaluating cost optimization opportunities that require code or application-level changes (like a query rewrite enabling a downsize), versus purely configuration-level changes? I’d weigh the engineering effort and deployment risk of the code-level change against its cost payoff and any performance benefit it also delivers, generally prioritizing configuration-level optimizations (right-sizing, reserved capacity, retention tuning) first since they’re lower-risk and faster to realize, while tracking code-level opportunities (a genuinely wasteful query enabling a real downsize) as a longer-term backlog item coordinated with the application team, rather than expecting DBA-led cost optimization to unilaterally drive application code changes on its own timeline.
45. How would you design cost governance guardrails (via Azure Policy or similar) to prevent cost creep at the point of provisioning, rather than only catching it after the fact in review? I’d use Azure Policy to enforce tagging requirements (so every new database is attributable from day one), potentially restrict which service tiers/sizes can be provisioned without an approval step for anything above a defined threshold, and require diagnostic settings and retention policies to follow an approved default rather than being configured ad hoc per database — shifting cost discipline earlier in the lifecycle, since preventing over-provisioning at creation time is generally cheaper and less disruptive than catching and correcting it months later after a workload and its dependencies have already grown around the oversized baseline.
46. Explain the cost consideration of Automatic Tuning’s CREATE INDEX option specifically, at fleet scale, from a storage and write-overhead cost perspective, not just performance. Individually small, each automatically created index adds marginal storage cost and marginal write overhead — but at fleet scale, across hundreds of databases each accumulating tuning-driven indexes over months or years without holistic review, that marginal cost compounds into something worth actually measuring, not assuming is negligible. I’d include index count and total index storage footprint trends as part of a periodic fleet-wide cost review, specifically to catch this kind of slow, distributed cost creep that no single database’s bill would make obvious on its own.
47. How would you handle cost optimization recommendations that conflict with a compliance or security requirement (like a mandated LTR retention period that’s genuinely expensive)? I’d clearly distinguish between costs that are genuinely mandated and non-negotiable versus costs that exist by default or convention without a real mandate behind them, since only the latter category is a legitimate optimization target — for genuinely mandated compliance costs, the right move isn’t to quietly reduce them, it’s to make sure the mechanism delivering that requirement is itself efficient (right retention granularity, right storage tier for rarely-accessed archival data) rather than trying to reduce the requirement itself, which isn’t actually the DBA’s decision to make unilaterally.
48. What’s your approach to cost optimization in a scenario where a business unit strongly resists any change to their database configuration due to past incidents, even when the data clearly supports optimization? I’d lead with a low-risk, reversible pilot on a less business-critical database within their environment first, letting a real, positive track record build trust before proposing changes to their most sensitive systems, rather than trying to win the argument on data alone against an organization that’s had a bad past experience — trust rebuilt through a demonstrated safe track record tends to be far more effective than a purely analytical case, especially when the resistance is rooted in a genuine past incident, not just general reluctance to change.
49. How would you measure and report the ROI of a cost optimization program over time, in a way that holds up to scrutiny from finance or leadership? I’d track actual realized savings against a documented baseline (what the fleet would have cost without the optimization actions taken), not just the theoretical savings estimated at the time each recommendation was made, since the two can diverge — a downsized database that later needed to scale back up for legitimate reasons shouldn’t still be counted as a permanent saving in an ongoing report. I’d also report honestly on any optimization actions that had to be reversed and why, since a program that only reports successes isn’t giving leadership an accurate picture of the actual trade-offs being navigated.
50. Walk through a genuinely difficult cost optimization challenge you’ve handled or would expect to handle, end to end. Intentionally open-ended, because interviewers are evaluating judgment under real trade-offs, not a memorized script. A strong structure to demonstrate regardless of the specific scenario: (1) establish real data before proposing anything — actual utilization, actual usage patterns, not assumption or a quick glance at the portal, (2) identify the genuine trade-off being made by any proposed optimization, and make it explicit rather than hiding it inside a “just save money” framing, (3) prioritize low-risk, reversible changes first, especially when trust in the process still needs to be built, (4) get the trade-off consciously approved by whoever owns the associated risk, not decided unilaterally, and (5) measure and report actual realized results afterward, including anything that had to be walked back, rather than declaring victory the moment a change is applied. Interviewers consistently favor candidates who describe being honest about what a savings recommendation costs elsewhere — performance headroom, resilience, flexibility — over candidates who present cost optimization as a purely upside decision with no trade-off at all.
A Closing Thought
The idea running through nearly every advanced answer here: real cost optimization isn’t about finding the one clever trick — it’s disciplined, ongoing, evidence-based review that’s honest about what every saving actually costs somewhere else, and confident enough to say plainly when a “savings opportunity” isn’t worth what it would give up.
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