Ai Spend Auditv1.0.0

A 50-employee SaaS company spends $30K monthly on AI tools, exceeding 5% of revenue without clear ROI. Using the AI Spend Audit, they inventory all line items, score tools, and identify $8K monthly waste from unused licenses and over-provisioned infrastructure, aiming to reduce spend to 3% of revenue.

A 100-employee consulting firm faces AI tool subscription renewals. They apply the framework to assess usage and ROI, focusing on billable hour impact. They discover 40% waste from overlapping content tools and downgrade model tiers, targeting $5 in labor savings per $1 of AI spend.

An ecommerce company with 200 employees has AI spend of $60K monthly, growing faster than revenue. They use the audit to implement model cost optimization and vendor consolidation, reducing spend by 25% through caching and consolidating three vector databases into one, lowering AI cost per order.

A manufacturing firm with 300 employees considers building custom AI capabilities versus buying SaaS. The audit helps map spending categories, score existing tools, and identify 30% waste from duplicate development efforts, guiding a decision to standardize on one vendor for defect reduction AI.

A healthcare organization with 500 employees audits AI spend, factoring in 25% compliance overhead. They use industry adjustments to legitimately account for these costs while identifying waste from GPU instances running 24/7 and unused SaaS features, recovering $50K monthly.

Companies pay recurring fees for AI tools like OpenAI or HubSpot AI. The audit helps optimize by right-sizing tiers, eliminating unused licenses, and consolidating vendors, typically recovering 25-40% of spend through waste identification and model downgrades.

Firms invest in internal ML teams or fine-tuning for bespoke AI solutions. The audit identifies duplicate efforts and over-engineering, with 25-45% typical waste, guiding decisions to standardize pipelines and reduce unnecessary one-time costs recurring as ongoing expenses.

Businesses provision GPU instances and vector databases for AI workloads. The audit targets over-provisioned and always-on dev instances, with 35-55% waste, by implementing caching, batch processing, and consolidation to cut costs by up to 40%.