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.
Here are the inputs that move the result the most. Play with the sliders and check it out. The number updates live.
Vendor price vs the all-in cost of building: FTEs, ramp, and opportunity cost. Shown monthly; ramp fixed at 6 months.
💡Cheaper isn’t everything: building wins when it’s core and you can staff it; buying wins when speed and focus matter more.
Same calculator, three 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
Same calculator, different applications. Sizes above, workloads here. Pick the one that looks like yours.
Pre-loaded scenarios for the most common applications. Click a tab to see realistic numbers, then hit "Try this scenario" 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
Open the full calculator. Pick a model, enter your tokens, see per-call, daily, monthly, and annual cost.
🚀 Open the full calculator →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.
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.
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.
Cost isn't the only dimension. Click any constraint to 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 →
The gaps we just listed are real, and they are the expensive ones: your actual prompts, your switching cost, your MLOps overhead. An AICost expert spends the hour on your AI and cloud costs, not a generic playbook. You leave with a written report: the way forward, in 30 days of concrete steps.
Not sure yet? The $39 AICost Blueprint credits toward a Session, and the Session fee credits toward any plan. You never pay twice for the same ground. See all pricing →
You have seen the shape of it. Open the calculator with your model, your tokens, your volume.
🚀 Open the full calculator →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 →