Guides → Playground & Guide → Buy vs Build - When to Use a Vendor SaaS vs Build Your Own AI
Meet Aditi Sharma. VP Engineering deciding on AI sales coaching tooling. "Cresta wants $400K/year for sales call coaching. Could we build it ourselves with Claude for $50K/year + 1 engineer?"
🔥 Vendor pitch claims '6 months to build yourself'. Engineer says '6 weeks'. Both are wrong.
Buy-vs-build for AI is a 4-axis decision. (1) Total cost (vendor fee vs API + headcount + opportunity cost). (2) Time-to-value (vendor 4 weeks vs in-house 4-6 months). (3) Differentiation (does the AI feature need to be unique?). (4) Long-term flexibility (vendor lock-in vs full control).
Aditi's situation: Cresta $400K/year for sales coaching. In-house equivalent: $50K LLM + 1.5 FTE × $250K = $425K/year, similar cost. Plus 4-6 months to build. Plus eval pipeline maintenance. Plus the opportunity cost of those engineers not doing differentiating work. The honest math often favors buying - until the feature becomes core differentiation.
Three buy-vs-build patterns. (1) Buy commodity AI (transcription, OCR, generic chatbot). (2) Build differentiating AI (your unique workflow, your customer's unique data). (3) Hybrid (vendor for the LLM, in-house for the wrapping). Most teams should default to buying commodity, building differentiating, and using vendor-LLM-with-in-house-wrapping for the rest.
Should you buy a vertical AI SaaS (Cresta, Glean, Harvey) or build your own with OpenAI/Anthropic APIs? Real cost math + non-cost factors + decision framework.
Below: live sliders. Move them to see numbers change in real time.
Each input shapes your cost. Move the slider — see the impact.
Open the full calculator — pick a model, enter your tokens, see per-call, daily, monthly, and annual cost.
🚀 Open the full calculator →Vendor cost is fixed; in-house cost is loaded. Vendor: $400K/year. In-house: API ($50K) + FTEs ($375K) × opportunity multiplier (1.3) = $553K/year first year, then $487K/year ongoing.
Time-to-value is the bigger axis. Vendor: 4 weeks. In-house: 4-6 months. If the feature drives revenue, those 5 missed months cost more than the vendor fee.
Differentiation flips the math. If your AI feature IS the product (or its biggest moat), in-house is mandatory regardless of cost. Don't outsource your moat.
Hybrid is the under-used answer. Use vendor LLM (Anthropic, OpenAI), build the in-house wrapping (your workflow, your data integration). Get cost benefit of API, differentiation benefit of custom code.
Same calculator, three different team sizes. Click a tab to see how the numbers shift.
Document OCR for legal team. Vendor: $60K/year. In-house: $20K API + $250K FTE × 1.3 = $345K. Buy saves money AND time.
Healthy range: Buy wins by $260K + 4 months
Cost roughly equal. Decision pivots on: is sales coaching CORE differentiation or just operational? If core → build. If not → buy and use those engineers on differentiation work.
Healthy range: In-house wins year 2+ if AI is core; buy if not
AI feature IS the product moat (e.g., proprietary algorithm + customer data integration). Don't outsource the moat. Math is secondary.
Healthy range: Build mandatory regardless of math
Cost isn't the only dimension. Click any constraint — see how recommendations change.
Vendor cost is predictable, in-house cost is optimizable. At small scale, vendor wins on predictability. At large scale, in-house wins on optimization. The crossover is usually around 5-10× current vendor fee.
Vendor SaaS includes eval/monitoring you'd otherwise build. Discount the in-house FTE estimate accordingly.
Vendor compliance is a feature you'd otherwise build. Self-build means you own the audit, the BAA negotiations, the retention policies.
In-house has a real privacy advantage. Customer data + AI processing both stay in your VPC. Vendor SaaS sends prompts to their infra.
Vendor SaaS adds a network hop. In-house can co-locate API calls with your application server. Matters for real-time UX.
Vendor lock-in for AI SaaS is severe - your workflow, your data, your evals all live in their platform. Migration = full re-implementation.
MLOps for AI features is real ongoing cost. Vendor includes it; in-house means a 0.5-1 FTE/year line item that's easy to forget.
Tradeoff analysis is where most AI projects go sideways. Talk to a CFO-grade AI cost analyst →
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.
Standard call transcription. Deepgram, AssemblyAI, others all cheap. Building yourself = wasted engineering.
Healthy range: Buy clearly - commodity
Don't buy Intercom Fin or full chatbot platform. Use Anthropic API + custom integration to your support stack. Cheaper + better fit + zero lock-in.
Healthy range: Hybrid: vendor LLM + custom integration
Vertical AI SaaS for legal/finance research. Vendor: $1.2M/year. In-house: $400K API + 6 FTE × $300K × 1.5 = $3.1M year 1. Buy now, plan year-2 in-house IF usage justifies.
Healthy range: Buy for fast value, build year 2 if scale justifies
Honest limitations — every model is wrong; some are useful. Where this one falls short:
For these, use: Cost Calculator for in-house API math. Scale Projection for stress-test.
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.
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 →
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.
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 →