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AtomEons / Learn / calc / tools / stack-recommender

::calculator · Heuristic stack-selection for solo builders and small teams, using June 2026 list pricing

AI Stack Recommender

This is a transparent heuristic, not a benchmark. It scores three frontier providers — Anthropic (Claude 3.5 Sonnet), OpenAI (GPT-4o), and Google (Gemini 1.5 Pro) — against your five inputs, then recommends a primary model, a fallback, and a rough monthly cost estimate. The scoring formula is visible, the weights are stated, and the assumptions are listed at the bottom so you can disagree with us and rerun the math yourself. Stack choice in mid-2026 is not a settled question. Anthropic leads on writing register, long-context reasoning, and tool-use reliability. OpenAI leads on raw coding throughput and ecosystem breadth. Google leads on cost-per-token and the longest context window in the frontier tier. None of the three is universally best; the right pick depends on what you're actually doing, how much regulated data you touch, and how price-sensitive your monthly burn is. We weight five inputs: use case (which provider scores best at writing vs. coding vs. research vs. analysis vs. support), team size (proxy for governance complexity), monthly budget (which forces fallback to cheaper tiers above certain spend ceilings), data sensitivity (regulated workloads bias toward Anthropic's enterprise posture), and volume tier (high-throughput shops get pushed toward Gemini's cheaper input pricing). The output is a ranked recommendation with a fallback for redundancy, and a cost estimate based on an assumed token mix. What this tool will not do: it will not tell you which model is "smartest." That changes monthly, varies by eval, and frontier-model gaps in 2026 are smaller than vendor marketing implies. It will not account for fine-tuning costs, dedicated capacity discounts, enterprise agreement pricing, or your existing infrastructure lock-in. It will not replace a real proof-of-concept against your actual workload. What it will do: give you a defensible starting point in under thirty seconds, so you can stop deliberating and start building. If the recommendation feels wrong, the assumptions are listed — adjust your priors and re-run. Honest heuristic, no theater.

::inputs

Primary use case

What this stack will mostly be doing day-to-day.

Team size

Number of humans who will hit the API regularly.

Monthly budget (USD)$

Hard ceiling on API spend per month, excluding fine-tuning.

Data sensitivity

Regulatory and confidentiality posture of the data you'll send.

Volume tier

Approximate API call volume per month across the team.

::result

Top provider score

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Estimated monthly cost (Claude Sonnet pricing)

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Budget fit %

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::how this calculates

Each of the three providers (Anthropic, OpenAI, Google) gets a heuristic score from 0-100 based on the five inputs. Use case contributes the most weight (40 points), then data sensitivity (25), volume/cost fit (20), and team-size governance fit (15). The provider with the highest score becomes the recommended primary; the second-highest becomes the fallback for redundancy. Monthly cost estimate assumes an average call uses 1,500 input tokens and 500 output tokens, multiplied by volume tier (50K, 500K, 5M, or 25M calls/mo) and the primary provider's blended price.

::worked examples

Solo writer, low volume, internal data

primaryUseCase: writingteamSize: 1monthlyBudget: 200dataSensitivity: internalvolumeTier: low

Anthropic wins on writing register and low-volume cost is trivial ($375/mo at Sonnet pricing). Fallback to OpenAI GPT-4o for redundancy. Budget fit is comfortable.

10-person engineering team, mid-volume, confidential

primaryUseCase: codingteamSize: 10monthlyBudget: 5000dataSensitivity: confidentialvolumeTier: medium

OpenAI GPT-4o leads on coding, with Anthropic as fallback for governance posture. Monthly burn at ~$3,750 fits budget. Both providers have enterprise data-handling agreements.

Research team, long-context, regulated data

primaryUseCase: researchteamSize: 8monthlyBudget: 3000dataSensitivity: regulatedvolumeTier: medium

Anthropic scores highest — long-context Claude is strong here and the regulated-data weighting bumps it further. Gemini 1.5 Pro is fallback for 2M-token context overflow cases. Cost runs near the budget ceiling.

High-volume customer support automation

primaryUseCase: customer-supportteamSize: 25monthlyBudget: 20000dataSensitivity: confidentialvolumeTier: high

OpenAI leads on support templates and tool-use throughput. Gemini fallback for cost-per-token relief on routine queries. At 5M calls/mo on Sonnet pricing the bill hits ~$37,500 — over budget, so consider routing routine queries to Gemini Flash or GPT-4o-mini.

::what this does NOT capture

○Pricing snapshot is June 2026 list-price: Claude 3.5 Sonnet $3/M input + $15/M output, GPT-4o $2.50/M + $10/M, Gemini 1.5 Pro $1.25-$5/M input + $5-$15/M output. List prices change frequently; verify before committing.
○Cost estimate uses Claude 3.5 Sonnet pricing as the anchor, not the actual recommended provider. Real cost will vary ±30% depending on which provider you pick and your token mix.
○Average call assumed to be 1,500 input tokens + 500 output tokens. Long-context research workloads will be 5-10x higher; short support replies will be lower.
○Use-case scoring reflects mid-2026 frontier-model rankings on public benchmarks and operator-reported real-world quality, not a single source of truth. Gaps between top models are smaller than vendor marketing implies.
○Data sensitivity weighting assumes Anthropic and OpenAI both have SOC 2 / HIPAA BAA options for enterprise tiers; verify your specific contract terms.
○Volume tier buckets are coarse (50K, 500K, 5M, 25M calls/mo). Real traffic distributions have heavy tails that this heuristic does not capture.
○Fallback logic does not account for cross-provider context-window or tool-use compatibility. Switching providers mid-workflow usually requires prompt rework.
○Fine-tuning, dedicated capacity, prompt caching discounts, batch-API discounts, and enterprise agreement pricing are not modeled. Real spend at scale will be 20-50% lower than list.

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