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AI Subscription Picker for Builders - Anthropic, OpenAI, Google, GitHub, Microsoft

🚀 Open the calculator

Meet Selectable: Eng leader / Solo founder / FP&A / Consultant. Builder choosing AI subscriptions across vendors for a team or solo practice. "Should we add Pro/Max subscriptions, ChatGPT Team, Gemini, Copilot, M365 Copilot - or stay on raw API? Which vendor, which tier, how many seats?"

🔥 Multiple vendors, multiple tiers per vendor, team's chat needs vs automation costs vary. Without a structured comparison, the answer drifts to 'whatever someone advocated for in the last meeting'.

The story

Anthropic's June 15, 2026 Agent SDK credit launch broke the math on subscription vs API. Pro at $20/mo now covers $20 of automation AND $20 of chat usage. Max 20x at $200 covers $200 of each. But the win only works for Anthropic - OpenAI/Google/GitHub/Microsoft subscriptions don't bundle API credits.

Your team isn't pure Anthropic, though. Engineering uses ChatGPT for code review, finance uses Gemini for docs, ops uses Copilot in the IDE. The right subscription mix depends on persona, team size, API spend, and vendor allowances.

This picker takes your persona, workload, and vendor preferences and ranks all 13 plans across 5 vendors. Pricing is sourced from aicost.ai's verified-2026q2 catalog (HIGH confidence) and Anthropic's SDK credit announcement (MEDIUM, refined post-launch). It tells you the top 3 picks with side-by-side savings, plus what's wasteful (over-bought) and what's a trap (under-bought).

About this calculator: AI Subscription Picker for Builders - Anthropic, OpenAI, Google, GitHub, Microsoft

Pick the right AI subscription mix across Anthropic, OpenAI, Google, GitHub, Microsoft for your team. Persona-driven 4-step wizard accounts for API spend, team size, chat usage, and vendor preferences. Includes Anthropic Agent SDK credit math (effective June 15, 2026).

Inputs you control

Input Impact on result Range Typical
Step 1: Who's deciding? Persona sets default API spend, team size, and chat-realization assumptions. You can override any of them in Step 2. — eng_leader
Step 2: Current monthly Claude/OpenAI/Google API spend ($) Sum of all metered AI API charges in a typical month. Round to nearest $50. $0 if you don't use API today (pure chat use case). 0 – 10K 500
Step 2: Number of seats / users How many people would use the subscription. Solo personas default to 1. Eng-leader defaults to 10. Team plans suppress automatically when seats < 2. 1 – 500 10
Step 2: Interactive AI value realized (% of allowance) What fraction of the bundled chat allowance your team would actually use at retail value. 0% = pure automation, no chat. 60% = solid dev team default. 100% = chat power users. 0 – 100 60
Step 3: Vendor allowlist (optional) Restrict the comparison to specific vendors. Use 'all' for full ranking. Useful if procurement has already locked in a vendor relationship. — all

Outputs computed for you

Output How inputs affect it
Monthly cost ($) computed from inputs
Annual cost ($) monthlyUsd × 12

Below: live sliders. Move them to see numbers change in real time.

What you're looking at

Each input shapes your cost. Move the slider — see the impact.

eng_leader

Persona sets default API spend, team size, and chat-realization assumptions. You can override any of them in Step 2.

Estimated: —
500

Sum of all metered AI API charges in a typical month. Round to nearest $50. $0 if you don't use API today (pure chat use case).

Estimated: —
10

How many people would use the subscription. Solo personas default to 1. Eng-leader defaults to 10. Team plans suppress automatically when seats < 2.

Estimated: —
60

What fraction of the bundled chat allowance your team would actually use at retail value. 0% = pure automation, no chat. 60% = solid dev team default. 100% = chat power users.

Estimated: —
all

Restrict the comparison to specific vendors. Use 'all' for full ranking. Useful if procurement has already locked in a vendor relationship.

Estimated: —

Ready to run the numbers?

Open the full calculator — pick a model, enter your tokens, see per-call, daily, monthly, and annual cost.

🚀 Open the full calculator →

Reading your result

Net monthly savings = API cost displaced (Anthropic SDK credit) + chat allowance realized − subscription cost.

Anthropic wins right now if you have any API spend. Other vendors compete only on chat value, never on API offset.

The picker shows top 3 picks per persona + 'why this beats X' explanation.

Wasteful pick warning: if Max 20x ($200/mo) ranks above your actual API spend, you're paying for unused SDK credit capacity.

Trap pick warning: Team Standard, Enterprise Standard, and OpenAI Team are NOT eligible for SDK credit. They cost you predictability without the savings.

What "good" looks like:
  • Eng leader default ($500 API, 10 seats, 60% chat): Team Premium wins, saves ~$260/month, $3,120/year
  • FP&A default ($2000 API, 25 seats, 40% chat): Team Premium wins, saves ~$600/month, $7,200/year
  • Solo founder default ($50 API, 1 seat, 80% chat): Max 5x wins, saves ~$30/month, $360/year
  • Consultant default ($100 API, 1 seat, 70% chat): Max 5x wins, saves ~$70/month, $840/year

Multi-vendor picker - also surfaces vendor benchmark context

Verified 20 hours ago
  1. 1
    GPT-5 Mini
    $0.250 in · $2.00 out ·
  2. 2
    Command
    $1.00 in · $2.00 out ·
  3. 3
    devstral-2
    $0.400 in · $2.00 out ·
  4. 4
    grok-build-0-1
    $1.00 in · $2.00 out ·
  5. 5
    command
    $1.00 in · $2.00 out ·

Three real scenarios

Same calculator, three different team sizes. Click a tab to see how the numbers shift.

10 engineers at Team Premium ($60/seat × 10 = $600/mo, $600 SDK credit absorbs all $500 API, $360 chat value at 60%). Net +$260/month, $3,120/year. Beats staying on raw API by capturing the chat allowance team would use anyway.

Healthy range: Team Premium ranks first with positive net savings

See inputs used
persona
eng_leader
monthly_api_spend_usd
500
num_users
10
interactive_value_pct
60

Trade-offs

Cost isn't the only dimension. Click any constraint — see how recommendations change.

What matters most to you? Click any dimension — recommendations update.

Best fit for "cost":

  1. Match plan size to actual API spend per seat Avoid Max 20x for $200/eng - overbuy
  2. Multi-seat team plans almost always beat individual subs at 3+ users 5+ seats = Team Premium dominates
  3. Annual savings often $500-$5,000+ For typical 10-eng team with API automation

The Anthropic SDK credit reshapes builder subscription math. Match SDK credit to API spend per seat; overbuy is more wasteful than under-buy because overflow is charged at standard API rates.

Use cases

Pre-loaded scenarios for the most common applications. Click a tab to see realistic numbers — then the "Try this scenario" button to load it into the calculator above.

Consultant pays for Max 5x ($100/mo) with the $100 API offset, gets $70/mo chat value (worth ~1 billable hour/month). Net win and the chat allowance pays for itself in 1-2 saved hours.

Healthy range: Max 5x positive net savings; payback in month 1

See inputs used
persona
consultant
monthly_api_spend_usd
100
num_users
1
interactive_value_pct
70

What this calculator can't tell you

Honest limitations — every model is wrong; some are useful. Where this one falls short:

For these, use: See API vs Pro+SDK Breakeven for single-vendor Claude-only depth. Consumer subscription picker for personal-use scenarios.

Where to go next

Claude-only subscription depth →

Detailed comparison if you've already decided on Anthropic.

 Consumer subscription picker →

Personal/family use - different math, different tiers.

 Raw API cost math →

Pure API costs without subscription overlay.

 Buy vs build framing →

Subscription is one input to the broader buy/build decision.

Methodology

Source
/ai-cost-economics
Extraction
Anthropic, OpenAI, Google, GitHub, Microsoft public pricing pages cross-checked via aicost.ai source-resilience adapter (vendor_page + litellm + openrouter).
Editorial gate
8-layer defense — see aicost.ai/ai-cost-economics
Last verified
6/4/2026, 8:00:00 PM

Author: Subu Vdaygiri, Founder & CEO of CloudIntelligence.ai. 17 years Fortune 100 (Ingram Micro, Siemens). Wharton CTO program · Kellogg CPO program · 10× AWS+Azure certified.

3 years of pricing history

Why this matters: pricing for major vendors has dropped 40-90% in the last 24 months. A budget set 12 months ago is probably wrong by 30%+.

View 3-year history for →
📖 Data sources & methodology 161 text models · 9 embeddings · 24 vision · 41 audio · 8 vector DBs across 10 vendor pages · last verified 2026-06-05

Methodology

  • All prices are USD per 1 million tokens, current as of 2026-06-05.
  • Vendor-published values have no mark. Inferred/extrapolated values are marked with * and listed below.
  • Batch API discounts are 50% off standard rates across providers that offer Batch mode.
  • Prompt caching discounts vary by provider (typically 80-90% off cached input tokens).
  • Regional data-residency surcharges (Anthropic 1.1x, OpenAI 1.1x, Google regional tiers) are NOT included in base rates.
  • Long-context pricing tiers apply when input exceeds model threshold.
  • Embedding prices are input-only (no output tokens generated).

Primary sources

Last-verified date is the most recent successful daily snapshot (aicost_pricing_snapshots) or, when no snapshot exists yet, the latest successful crawler run (aicost_crawler_runs). 10 of 10 vendors are currently verified. Aggregator services (TokenCost, AI Pricing Guru, etc.) are not listed.

Anthropic
2026-06-05
https://www.anthropic.com/pricing
Daily snapshot since Sep 2023 · 578 days captured
Anthropic Docs
2026-06-05
https://platform.claude.com/docs/en/about-claude/pricing
Daily snapshot since Sep 2023 · 578 days captured
OpenAI
2026-06-05
https://openai.com/api/pricing/
Daily snapshot since Sep 2023 · 579 days captured
Google AI
2026-06-05
https://ai.google.dev/gemini-api/docs/pricing
Daily snapshot since Dec 2023 · 554 days captured
Google Vertex
2026-06-05
https://cloud.google.com/vertex-ai/generative-ai/pricing
Daily snapshot since Dec 2023 · 554 days captured
DeepSeek
2026-06-05
https://api-docs.deepseek.com/quick_start/pricing
Daily snapshot since May 2024 · 493 days captured
xAI
2026-06-05
https://x.ai/api
Daily snapshot since Nov 2024 · 411 days captured
Mistral
2026-06-05
https://mistral.ai/pricing
Daily snapshot since Dec 2023 · 552 days captured
Cohere
2026-06-05
https://cohere.com/pricing
Daily snapshot since Sep 2023 · 578 days captured
Voyage AI
2026-06-05
https://docs.voyageai.com/docs/pricing

Inferred values (marked with * in calculator tables)

Derived from industry conventions, not directly published by the vendor. Typical conventions: cached input = 10% of base (90% off), Batch API = 50% of base (50% off).

Vendor / Model Field Why it’s inferred
Anthropic — Claude Sonnet 4.6 cachedInput Derived at 10% of input rate — Anthropic publishes 90% cache-hit discount on this tier.
Anthropic — Claude Sonnet 4.5 cachedInput Derived at 10% of input rate; same 90% cache-hit convention as Sonnet 4.6.
Anthropic — Claude Sonnet 4.5 batchInput Derived at 50% of standard input — Anthropic documents uniform 50% Batch discount.
Anthropic — Claude Sonnet 4.5 batchOutput Derived at 50% of standard output — Anthropic documents uniform 50% Batch discount.
Anthropic — Claude Haiku 4.5 cachedInput Derived at 10% of input rate — Anthropic 90% cache-hit discount convention.
OpenAI — GPT-5.4 Mini cachedInput Derived at 10% of input — OpenAI documents automatic 90% discount on cache hits across GPT-5.x tier.
OpenAI — GPT-5.4 Nano cachedInput Derived at 10% of input — OpenAI 90% cache-hit convention.
OpenAI — GPT-5.4 Nano batchInput Derived at 50% of input — OpenAI Batch API uniform 50% discount.
OpenAI — GPT-5.4 Nano batchOutput Derived at 50% of output — OpenAI Batch API uniform 50% discount.
OpenAI — GPT-5.4 Pro cachedInput Derived at 10% of input — OpenAI 90% cache-hit convention.
OpenAI — GPT-5.4 Pro batchInput Derived at 50% of input — OpenAI Batch API uniform 50% discount.
OpenAI — GPT-5.4 Pro batchOutput Derived at 50% of output — OpenAI Batch API uniform 50% discount.
OpenAI — GPT-5.2 cachedInput Derived at 10% of input; no residency uplift.
OpenAI — GPT-5.2 batchInput Derived at 50% of input.
OpenAI — GPT-5.2 batchOutput Derived at 50% of output.
OpenAI — GPT-5 cachedInput Derived at 10% of input.
OpenAI — GPT-5 batchInput Derived at 50% of input.
OpenAI — GPT-5 batchOutput Derived at 50% of output.
OpenAI — GPT-5.5 Pro cachedInput Derived at 10% of input — OpenAI does not publish a cached rate for *-pro models; using the family convention.
OpenAI — GPT-5.5 Pro batchInput Derived at 50% of input.
OpenAI — GPT-5.5 Pro batchOutput Derived at 50% of output.
OpenAI — GPT-5.2 Pro cachedInput Derived at 10% of input — pro-tier convention.
OpenAI — GPT-5.2 Pro batchInput Derived at 50% of input.
OpenAI — GPT-5.2 Pro batchOutput Derived at 50% of output.
OpenAI — GPT-5.1 batchInput Derived at 50% of input.
OpenAI — GPT-5.1 batchOutput Derived at 50% of output.
OpenAI — GPT-5 Pro batchInput Derived at 50% of input.
OpenAI — GPT-5 Pro batchOutput Derived at 50% of output.
OpenAI — GPT-5 Nano cachedInput Derived at 10% of input.
OpenAI — GPT-5 Nano batchInput Derived at 50% of input.
OpenAI — GPT-5 Nano batchOutput Derived at 50% of output.
Google — Gemini 3 Flash cachedInput Derived at 10% of input — Google caching discount convention ~90%.
Google — Gemini 3.1 Flash-Lite cachedInput Derived at 10% of input — Google caching convention.
Google — Gemini 3.1 Flash-Lite batchInput Derived at 50% of input — Google Batch API uniform 50% discount.
Google — Gemini 3.1 Flash-Lite batchOutput Derived at 50% of output — Google Batch API uniform 50% discount.
Google — Gemini 2.5 Pro cachedInput Derived at 10% of input.
Google — Gemini 2.5 Flash cachedInput Derived at 10% of input.
Google — Gemini 2.5 Flash-Lite cachedInput Derived at 10% of input — Google caching convention.
Google — Gemini 2.5 Flash-Lite batchInput Derived at 50% of input — Google Batch API uniform 50% discount.
Google — Gemini 2.5 Flash-Lite batchOutput Derived at 50% of output — Google Batch API uniform 50% discount.
Google — Gemini 2.0 Flash cachedInput Derived at 25% of input per Google 2.0 family caching rates.
Google — Gemini 2.0 Flash batchInput Derived at 50% of input — Google Batch API uniform 50% discount.
Google — Gemini 2.0 Flash batchOutput Derived at 50% of output — Google Batch API uniform 50% discount.
Google — Gemini 2.0 Flash-Lite cachedInput Derived at 10% of input — Google caching convention.
Google — Gemini 2.0 Flash-Lite batchInput Derived at 50% of input — Google Batch API uniform 50% discount.
Google — Gemini 2.0 Flash-Lite batchOutput Derived at 50% of output — Google Batch API uniform 50% discount.
xAI — Grok 4 (legacy) cachedInput Extrapolated at 25% of base.

Pricing is cross-verified against the LiteLLM community registry when available. Daily snapshots are kept in aicost_pricing_snapshots; every change is logged to aicost_price_changelog with old & new values for full audit trail. Read the full methodology →