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all systems normal · status.trellisagents.example v2.3.0 · tokens pass through at $0 margin

trellis Agents

configurator

Price your agent stack. Check our math.

Four choices, one receipt. Everything below is computed from the published rate card with the assumptions printed next to the answer — the same formula that generates the worked examples on the pricing page.

Estimated total at 100k runs/mo $384.00 · $0.00384/run

1 · What will the agents do?
2 · Model tier (tokens pass through at cost)
3 · Guardrails ($0.08 per 1,000 checks)
4 · Observability depth

Your stack, priced

Customer support · Economy · Standard guardrails · Sampled traces

Metered per run

Model tokens (10,800 in + 2,100 out)
$0.00288
Guardrail checks (2 per run)
$0.00016
Observability
$0.00000

+ orchestration fee, volume-tiered below

Monthly, at three volumes

Runs / mo Models Guard Obs Orchestration Total
10,000 $28.80 $1.60 $0.00 $8.00 $38.40
100,000 $288.00 $16.00 $0.00 $80.00 $384.00
1,000,000 $2,880.00 $160.00 $0.00 $530.00 $3,570.00

Effective cost per run at 100k: $0.00384

Assumptions — check our math
  • Customer support: 6 model calls per run, averaging 1,800 input + 350 output tokens per call.
  • Economy tokens at $0.15/M input, $0.60/M output — illustrative list prices, passed through at cost.
  • Guardrail checks run on our hosted screening model at $0.08 per 1,000 checks.
  • Orchestration fee: $0.0008/run for the first 100k, $0.0005 for the next 900k, $0.0003 beyond.
  • Worst case shown: provider prompt caching typically serves 60–90% of input tokens on multi-step runs at ~90% off — your model line will usually come in under this.

Your selections save locally in this browser. This is a demo — nothing is sent anywhere.

reading the estimate

Three things the totals are telling you

Tokens dominate — until they don't

On frontier models the model line is ~95% of the bill and every prompt byte matters. On economy fleets at volume, guardrails and observability can cost more than inference — that inversion is normal, and the controls are the point.

The worst case is printed

We assume zero cache hits. Real multi-step agents re-send accumulated context, and providers serve 60–90% of it from prompt cache at ~90% off — your model line usually lands under the estimate, never over it from our side.

If the number scares you, good

A frontier research fleet at scale is genuinely expensive. Before you commit, read when not to use an agent — a pipeline might do the job for 6× less.

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