Zylver Meter
AI spend, broken down to the call
Per-call, per-tenant, per-model attribution. Budgets and hard caps. Auto-routing that picks the best provider by cost and quality. The distillation engine that converts repeat LLM calls into deterministic functions.
The problem
"$18,400 on LLM calls. Which features? Which customers?"
Provider dashboards show the total bill. They don't tell you which feature burned the tokens, which customer used it, or which model was overkill for the job. You can't optimize what you can't attribute.
Meter instruments every call so you know exactly where the money goes and what to do about it.
The dashboard
Every call. Every dollar. Every model.
tenant: acme-corp | period: last 7d
feature:content.generate $1,847.12 (62% of tenant spend)
└─ model: gpt-4o 12,804 calls $1,640.30
└─ model: claude-haiku 4,120 calls $206.82
feature:docs.extract $812.44 (27% of tenant spend)
└─ router: distillation hit rate 68% (saved $1,723)
alert: content.generate approaching 80% of monthly cap ($2,400) _
What Meter does
Attribution. Control. Savings.
Per-call attribution
Every request tagged with tenant, feature, user, and session. Pivot spend by any dimension in seconds.
Budgets and caps
Hard caps per tenant, feature, or experiment. Soft alerts before limits. Automatic throttling when thresholds hit.
Auto-routing
Pick the cheapest model that meets quality thresholds. Fall back automatically when a provider degrades.
Distillation engine
Pattern match across your calls. Convert repeat operations into near-zero-cost deterministic functions. The pattern behind 60-90% cost reduction.
Quality sentinel
Drift detection, output scoring, anomaly alerts. Cost savings never come at the price of quality regressions.
Provider coverage
All major hosted-model providers and self-hosted endpoints. One instrumentation surface.
See the bill before it ships.
Meter is in staged early access. Waitlist members get the first cohort invite and locked-in pricing.