Vantage RuntimeAI · Forecast

Cost Forecast

Three monthly numbers for approval: evaluation on RuntimeAI Cloud, monitoring when models drift, and production inference at your provider. Teams can spin up unlimited custom scenarios in minutes — or start from the library below.

Plan your evaluation

Pick scenarios first, run models against them, then choose which scenarios to deploy for monitoring.

Custom scenarios in minutes. Use our library or define your own agent workflows — as many scenarios as you need, without waiting on us.

1. Evaluation scenarios

All library scenarios are included by default — remove any you do not need. Use the family tabs to browse, then click Choose models → on a scenario to load its scorecards.

Filter library

Scenario	Active	Models	Remove

2. Batch planner

Agent turns per eval run How many back-and-forth steps the agent may take in one eval run. One turn = one agent reply (and usually a stakeholder response). The run ends at scenario closure or when this limit is reached (1–24). Used for model costs and scorecards on the active scenario.

Plan Replications How many times each selected model runs the scenario in one batch — more reps tighten scorecard averages. Batches / month How many full model bake-offs you run per month while choosing a production model (e.g. weekly during selection).

Recommended from scorecards

Models & scorecards

Select up to 5 models for your active scenario.

Costs scale with evaluation scenarios × models × replications.

Speed shows sec/turn with color vs peers in the table (blue = fastest).

What is EFF?

Efficiency index (% of leader) — quality × speed × cost from benchmark scorecards on this scenario. Higher % is better value.

raw = sim score ÷ (latency × cost factor)

Sim score — automated scorecard mean on 0–10 (same as scoreboards).
Speed — mean seconds per agent turn from scored runs.
Cost factor — Low ×1, Medium ×2, High ×3.5 (catalog tier).

EFF = raw ÷ best raw in this scenario (100% = leader). Same metric as scoreboards and Rankings.

View full model costs & scorecards on Models →

	Model	Est. inference / run	Scorecard	Speed	EFF

Compare models

Monthly totals for your selection above — each row assumes that model alone for eval, monitoring, and production.

Check one or more models in the table above (up to 5).

Model	Scorecard	Speed	Eval / batch	Eval / mo	Monitoring / mo	Production / mo	Total / mo

Monitoring cadence — deploy scenarios

Choose which evaluation scenarios to ship for post-deploy regression. Only checked scenarios count toward monitoring cost. CI/CD or Cloud API.

Default production model Default model for deployed scenarios and production sizing below Deploy events / month Prompt or model changes that re-trigger CI Full sweeps / month Scheduled regression — e.g. weekly drift check = 4 Replications Per scenario × model when applying cadence

Scenario	Deploy	Model	Runs / mo

Cadence estimate per row: (deploys + sweeps) × replications = — runs/mo. Edit any row directly.

Which plan fits?

After you pick scenarios, models, and monitoring cadence above, compare how RuntimeAI plans scale with your workload.

1 Set phase length
2 See costs & fit

1. Set phase length

Runs per month come from your evaluation set, batch planner, and monitoring deploy choices above.

Evaluation scenarios — Matches your evaluation set — remove scenarios above to reduce Model selection months — Initial bake-off window — — runs/mo from batch planner Monitoring & optimization months — Post-ship steady state — — runs/mo from deploy + sweep cadence

Selection is often run-heavier (many models × batches), but only for a short window. Monitoring is usually fewer runs per month on one ship model — yet ongoing months are not always cheaper: a fixed platform fee can dominate when run volume drops, and frequent full sweeps or optimization cycles can push monitoring back above selection.

2. See costs & plan fit

How monthly cost scales with eval volume

Vertical markers show each phase’s runs/month. Lines show how total RuntimeAI cost scales if that run volume continued all month.

Production inference (your provider)

Unit economics at production volume for the production model above — billed to your provider, not RuntimeAI.

Scale tier Interactions / biz day Override tier default

Package for approval

Export evaluation, monitoring, and production economics together.

Company / project Prepared by Notes (optional)

Preview approval summary

For search engines and LLMs

RuntimeAI Cost Forecast: eval forecast (platform subscription, eval runs, check-ride inference); monitoring cadence (deploy-triggered and scheduled regression eval runs post-ship via vantage-core CI); production inference unit economics at customer provider not billed by Vantage. Export approval package.