Default Premium Routing
High-cost models run by default even when simpler tasks need lower-tier inference.
XOLOS GUIDE
AI Cost Optimization
Reduce model and inference spend with practical controls for routing, token efficiency, and continuous AI governance.
Book a cost reviewDaily spend reduction in less than 1 week
Why AI bills spike
Prompt Bloat
Context payloads grow over time and token usage increases faster than product value.
No Cache Guardrails
Deterministic responses are repeatedly recomputed instead of being reused.
No Unit Cost Ownership
Teams track total spend but miss cost per feature and workflow economics.
A sharper AI optimization lens
Winning teams optimize AI spend as an execution loop, not a one-time model swap.
Routing
Reserve premium models for high-complexity tasks only.
Prompting
Compress prompts and constrain output length by default.
Caching
Cache deterministic steps and repeated context patterns.
Governance
Review AI unit economics weekly with engineering ownership.
Unconventional but practical truths
Most AI waste is workflow design debt, not model pricing alone
Model quality and cost both improve when routing discipline is enforced
If cost per feature is invisible, AI spend grows faster than product value
How XOLOS helps
XOLOS identifies the highest-impact AI cost levers and helps teams execute routing, prompt, and policy updates with measurable savings.
What Happens Next
See results on daily spend within 1 week
- Workflow-level AI cost opportunity map
- Automation-first routing and prompt plan
- Expected monthly savings range and owners
AI cost optimization FAQ
What usually drives AI costs the most?
The largest AI cost drivers are excessive token usage, overpowered model choices for simple tasks, and repeated calls that could be cached.
Can we reduce AI spend without lowering quality?
Yes. Teams can often improve quality and reduce cost by using model routing, prompt compression, and workflow-level evaluation.
When should we route to smaller models?
Route to smaller models for classification, extraction, and simple Q and A, then escalate only complex tasks to premium models.
How should we monitor AI unit economics?
Track token and inference cost per feature, per user workflow, and per successful business outcome to guide optimization decisions.