Context Window Creep
Prompts keep expanding and token volume grows faster than feature value.
XOLOS GUIDE
LLM Token Cost Optimization
Reduce OpenAI and Anthropic token spend through prompt efficiency, model routing, and guardrails across every AI workflow.
Book a cost reviewDaily spend reduction in less than 1 week
Where token waste happens
Unbounded Outputs
Responses are not constrained, so token burn increases without improving outcomes.
No Deterministic Caching
Repeated inference workloads are recomputed instead of reused.
Default Premium Model Use
High-cost models serve requests that lower-tier models can satisfy.
A sharper token optimization lens
Token savings become durable when teams optimize routing, prompts, and caching as one system.
Measure
Track token cost by feature and user workflow owner.
Compress
Reduce prompt overhead and enforce output constraints.
Cache
Reuse deterministic steps to avoid redundant token spend.
Route
Escalate to expensive models only when complexity demands it.
Unconventional but practical truths
Most token savings comes from architecture discipline, not vendor negotiation
Prompt quality and cost efficiency improve together when teams enforce constraints
If cost per workflow is unknown, token spend becomes a hidden tax on growth
How XOLOS helps
XOLOS helps teams identify expensive token patterns, validate lower-cost alternatives, and roll out guardrails without breaking user-facing quality.
What Happens Next
See results on daily spend within 1 week
- Token waste opportunity map by workflow
- Routing and prompt optimization plan
- Expected monthly savings range and owners
LLM token optimization FAQ
Why is our token usage so high?
High token usage is often driven by verbose prompts, unnecessary context windows, and long outputs that exceed task requirements.
How much can prompt optimization save?
Many teams reduce token spend by 20 to 50 percent with prompt compression, context control, and better response constraints.
When should we switch to smaller models?
Use smaller models for deterministic and low-complexity tasks, then escalate to larger models only when quality requirements demand it.
How should we track token cost by workflow?
Measure cost per prompt chain, feature, and user action, then compare spend against completion quality and business outcomes.