Toolkit

Foundation Model Choice, Cost & Tradeoffs Toolkit

Practical calculators, planners, and decision inputs to compare foundation model options by cost-per-inference, latency/SLA implications, adaptation tradeoffs (adapter vs fine-tune), and vendor/governance risks. Save your inputs for team review.

Interactive Tool

Foundation Model Choice, Cost & Tradeoffs Toolkit

Why this toolkit helps

Choosing a foundation model isn't only about benchmark scores — it's about the economics, latency, adaptability, and governance fit for your product or workflow. This toolkit helps you capture realistic inputs, run simple cost and capacity estimates, surface adaptation tradeoffs, and record vendor and governance constraints so your team can compare options and make evidence-based choices.

The form collects the key inputs a practitioner needs. Form submissions are saved so you can iterate, compare model families, and share results with stakeholders.

How to use

  1. Enter realistic averages (tokens, requests, instance costs, constraints).
  2. Use the guidance below to calculate cost-per-request and hourly cost estimates.
  3. Record adaptation strategy preferences and governance constraints to surface hidden costs and risks.

Quick formulas (examples)

Use these formulas with values you enter below. Example numbers are illustrative; replace with vendor pricing.

  • Total tokens per request = Average prompt tokens + Average completion tokens
  • Cost per request = (Total tokens per request / 1000) * Price per 1k tokens
  • Hourly inference cost = Requests per second * 3600 * Cost per request
  • Estimated instances needed = ceil(Requests per second / Concurrency per instance)
  • Instance hourly cost = Estimated instances needed * Instance hourly cost
Typical user prompt length in tokens. Example: 50
Expected model output tokens per response. Example: 150
Enter vendor price for prompt+completion combined, per 1,000 tokens. If vendor provides separate prompt & completion rates, add them and convert to per-1k. Example: 0.03
Peak steady RPS to plan capacity. Example: 10
User-facing latency target. Examples: 150 ms for chat, 500 ms for assistant tasks.
Estimate based on model size and vendor guidance. Example: 4
Vendor price per hour for an inference instance (GPU/CPU). If using serverless pricing, convert to effective hourly. Example: 3.50
Adapters typically reduce compute and data needs and are faster to iterate; fine-tuning usually yields stronger task performance but increases training and maintenance costs.
Size of labeled/unlabeled data you plan to use for adapters or fine-tuning. Helps estimate training costs and storage needs.
Choose constraints that affect whether you can use hosted vendors, on-prem inference, or particular model families.
Rate 1 (low) to 5 (high) based on availability, export controls, or vendor stability.
Rate 1 (low) to 5 (high) based on certifications, SLA clarity, and contractual controls.
Capture qualitative judgments: e.g., 'Need sub-200ms latency; prefer open models for data residency; consider 13B model vs hosted large model.'
Paste computed values (cost per request, hourly inference cost, estimated instances). Use the formulas in the introduction. This field stores your calculations for future comparison.
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