10 questions before choosing a cloud optimizer

Cloud promised agility and lower costs, but rising bills have created new challenges. Enterprises now face pressure to make spending accountable and efficient, and the issue lies not just in technology but in how optimisation is prioritised, tracked, and embedded into daily work.

A structured evaluation of cost optimisers is key. The right tool must fit real-world processes, culture, and governance. For clarity, this guide is divided into three parts: Optimisation Focus, Organisational Fit, and Governance & Validation.

Optimization Focus

A cost optimiser should do more than list idle resources. It must highlight the tasks with the best savings-to-effort ratio so engineering time is spent where it matters most.

Clarity on effort versus reward turns optimisation from guesswork into measurable ROI. With that in mind, the first question to ask is how a tool prioritises and allocates opportunities.

Q1. Where are we wasting cloud spend?

Cloud waste shows up in many forms, such as idle servers running overnight, storage that no one uses, or oversized databases. The hardest part is that some leaks are obvious, but many are hidden across large, multi-cloud environments. Opslyft helps by combining billing data, usage metrics, and metadata into one view so nothing slips through the cracks.

• Idle VMs, unused storage, and misconfigured databases are common drains
• Small inefficiencies across thousands of resources quickly add up
• Multi-source data integration uncovers the long tail of waste

Q2. How does the tool prioritize and allocate optimization opportunities?

Every optimisation is not equal. Some fixes save a lot for little effort, while others eat up engineering time for marginal results. That’s why the right tool must rank tasks by both savings and effort. Opslyft’s prioritisation feed does exactly this, surfacing the “highest savings with lowest effort” opportunities first.

• Savings-to-effort ranking ensures engineers focus on high ROI.
• Custom policies tailor priorities to business goals or risk appetite.
• Quick wins get done first, while bigger projects can be planned later.

Q3. Do teams have sufficient data for cloud spend optimization?

A bill alone can’t explain where waste is happening. Real insight comes from pairing costs with resource tags, usage patterns, and application context. Opslyft connects billing with utilisation metrics, APM tools, and business data so optimisation decisions are grounded in reality, not guesswork.

• Tags and metadata show who owns which resources.
• Usage data reveals over-provisioned or idle capacity.
• Business context aligns savings with product and customer outcomes.

Q4. Does it provide clear effort-vs-reward visibility?

Optimisation only makes sense when the payoff is worth the time. A $500 monthly saving that takes 20 hours to fix may not be worth it, but a 10-minute change saving $100 each month is a clear win. Opslyft calculates both the effort and the reward so trade-offs are transparent.

• ROI calculations help balance the cost of work against feature delivery.
• Jira integration creates tasks with estimated effort included.
• Payback period visibility makes priorities clear at a glance.

Organizational Fit

A FinOps tool only works if it fits into existing workflows. Forcing new systems or ticket-heavy processes often creates pushback, while embedding into Jira, Slack, or APIs makes optimisation seamless.

At enterprise scale, ad hoc conversations don’t work. A feed-style approach keeps engineers engaged without overload. With that in mind, the next step is asking the right questions to see how well a tool supports this fit.

Q5. Do we need to hire (or can automation replace manual FinOps work)?

Managing cloud costs manually is resource-heavy and often requires large FinOps teams. Automation reduces that burden by detecting anomalies, surfacing optimisation opportunities, and generating reports without constant human effort. Opslyft’s AI-driven automation enables leaner teams to manage millions in spend effectively.

• Automation cuts down repetitive, manual analysis
• Lean teams achieve enterprise-scale cost control
• Alerts and insights arrive in real time with fewer false positives

Q6. How will it integrate with our workflows and culture?

Adoption fails if a tool forces engineers to change how they work. A cost optimiser must fit into existing systems like Jira, Slack, or internal dashboards. Opslyft’s API-first design and integrations embed recommendations directly into workflows, making optimisation part of daily routines rather than extra work.

• API-first design connects with existing dashboards and tools
• Jira and Slack integration ensures visibility without disruption
• Feed-style UX avoids overwhelming engineers with trivial tasks

Q7. Is it time to go serverless (and how will the optimiser handle it)?

Serverless adoption brings agility but also new visibility challenges, as costs are tied to execution rather than provisioned resources. An effective optimiser must track serverless usage patterns, model unit costs, and flag anomalies. Opslyft captures serverless data alongside traditional resources for a complete optimisation picture.

• Tracks cost drivers in serverless and containerised workloads
• Provides anomaly detection on variable consumption patterns
• Aligns serverless spend with business KPIs like requests or sessions

Q8. Do we know our unit cost (and can the tool help map spend to outcomes)?

Knowing the true cost per customer, transaction, or product is key to aligning cloud spend with business goals. A mature optimiser links raw spend with tags, cost centres, and usage data to surface unit economics. Opslyft maps costs to outcomes, making cloud spend accountable at every level.

• Cost allocation ties resources to teams, apps, or products
• Unit economics reveal the cost per customer or transaction
• Business-aligned reporting bridges finance and engineering

Governance & Validation

Optimisation must respect contracts, SLAs, and business obligations, so flexibility in applying rules is essential. At the same time, tracking and validating actual savings turns assumptions into proven outcomes and builds confidence across teams.

With these governance needs in mind, the following questions help assess whether a tool can deliver both control and accountability.

Q9. How does it handle governance, SLAs, and tracking?

Cost optimisation must respect contractual obligations and service-level agreements, while also proving that savings are real. A mature optimiser allows custom rules, approval workflows, and detailed tracking so changes stay compliant and measurable. Opslyft’s staged workflow ensures every recommendation is reviewed, approved, executed, and validated.

• Custom rules exclude SLA-bound or contractual resources
• Approval workflows maintain control before execution
• Tracking and audit trails confirm realised savings

Q10. What pricing model does the optimiser use?

Pricing matters as much as features. Many tools charge a percentage of the cloud bill, which becomes expensive at scale. Opslyft takes a usage-based approach, tying cost to the data processed and value delivered, making it far more predictable and efficient for large enterprises.

• Percentage-of-spend models grow costly with cloud scale
• Usage-based pricing aligns cost with actual platform value
• More sustainable for enterprises managing millions in spend

What next

These are some of the common pain points that every enterprise faces as cloud usage scales: unclear priorities, hidden waste, lack of ROI visibility, workflow friction, and governance challenges. The right optimiser is not just about technology, but about how well it fits into teams, processes, and business goals.

Opslyft addresses these challenges with intelligent prioritisation, seamless workflow integration, usage-based pricing, and a flexible, data-driven approach. The result is not only reduced cloud waste, but also a cost-conscious culture that enables innovation and financial discipline to grow together.