Cloud and AI cost optimization for SaaS companies exists because AI and infrastructure spend is quietly becoming one of the largest line items on a SaaS company's cost sheet, and most teams have no real benchmark for whether their number is normal or bloated. A generic "reduce your cloud bill" audit doesn't answer that question. It just finds unused instances.
Why this needs a SaaS-specific benchmark, not a generic audit
A SaaS product's cost structure is different from a typical enterprise workload: usage scales with customer growth, AI/model calls scale with feature adoption, and margin compression shows up fastest in the percentage of revenue infrastructure consumes. Generic cloud cost consulting optimizes instance sizing; it rarely has a real answer for whether your AI feature's per-call cost is reasonable at your scale.
The benchmark this audit is built against
We publish our own production numbers rather than asking you to trust an invented industry average: our Kavya WhatsApp assistant teardown documents real production AI agent costs running $500-15,000/month, scaling to $3,200-13,000/month at production scale. The audit places your current spend against bands like that: evidence you can check yourself, not a vendor's unverifiable claim.
What the audit actually covers
A review of your cloud infrastructure spend and your AI/model spend together, since for a SaaS product the two are increasingly the same budget conversation. The output is specific optimization targets scoped to your actual usage pattern (right-sizing, caching, model-selection tradeoffs), not a generic checklist applied regardless of what your product actually does.
Where this fits with a broader infrastructure conversation
If cost optimization surfaces a deeper infrastructure or CI/CD gap, that's a related but separate conversation. See our DevOps page for SaaS for what that looks like. And if you're earlier in evaluating what an AI feature should cost to build in the first place, our breakdown of custom software costs in 2026 covers that ground.
Common questions
What should we actually be paying for AI at our scale?
It depends heavily on usage pattern and model choice, which is why a benchmark comparison is more useful than a single number. Our own published production AI agent runs $500-15,000/month depending on scale, and the audit places your spend against a band like that rather than an arbitrary target.
How does this compare to the numbers in your Kavya post?
Directly. The Kavya WhatsApp assistant teardown publishes real production cost figures ($3,200-13,000/month at production scale) that this audit uses as one reference point among others, not a coincidence but a deliberate cross-link to evidence we've already made public.
What does the audit actually deliver?
A breakdown of where your current cloud and AI/model spend sits against comparable production workloads, plus specific, scoped optimization targets, not a generic "reduce your cloud bill" checklist.