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Build when the problem is narrow (one provider, one team, simple attribution) and the tooling is strategically yours to own. Buy when AI spend spans providers, arrives untagged, and needs unit costs finance will trust, because that build is a multi-quarter platform project with a permanent maintenance tail. Price both paths in engineer-years before deciding. CloudZero sells the “buy” side.

Perplexity pricing runs $0 for Free, $20 a month for Pro, and $200 a month for Max, with enterprise seats listed from $40 per user. Pro fits most people who search for work daily. Max exists for heavy automation. The prices are verified against Perplexity's live plans page as of September 2026. When Perplexity published a customer quote on its own pricing page, it chose an unusual one.

Kling AI pricing runs from a free tier of 66 daily credits to a reported $180 per month, with annual billing about 34% cheaper. Kling 3.0 bills per second: 6 to 12 credits for standard resolutions and 30 for native 4K. The API sells separate prepaid packages from $9.80 to $7,560.

ElevenLabs pricing runs from a free plan to $990 per month across six published tiers, with custom Enterprise above that. Plans meter usage in credits, where one credit roughly equals one character of speech. Voice agents cost $0.08 per minute on every tier, plus separate LLM and telephony charges. The AI agency PxlPeak published its own ElevenLabs invoice: $303 for January 2026, covering voice content for six clients, IVR systems for two, and live voice agents for three.

Token-based pricing charges for AI by the volume of text a model processes, metered separately for input tokens (what you send) and output tokens (what the model returns). As of 2026, OpenAI, Anthropic, and Google all bill their APIs this way, and the model is spreading into enterprise chat products. Bills scale with usage rather than seats.

Worldwide AI spending will reach $2.59 trillion in 2026, up 47% from 2025, according to Gartner. Yet only 37% of organizations report any earnings impact from AI, McKinsey finds. These AI statistics cover what companies spend, what AI costs to run, and the ROI they're actually getting. That gap between the two headline numbers is the story of AI in 2026.

CloudZero’s own AI Economics Pulse for September found the 75th percentile of its 430-company customer panel crossed 10% of its cloud bill on AI for the first time in August. Gartner’s latest survey found only 22% of organizations have scaled AI successfully and 11% don’t know what their own function spent on it last year. CJ Gustafson showed what that gap looks like on an actual invoice this week.

Picture two teams running the same million input tokens through the same model. One team’s tokens are cache hits, queued through the batch API. The other team’s are fresh, sent live. On a current-generation OpenAI model, cached input runs about a tenth the price of a fresh token, and batch processing cuts whatever’s left in half. Stack the two: at a list rate of $2 per million tokens, one team’s bill comes to 10 cents, the other’s to two dollars.

An LLM gateway is a proxy that sits between your applications and model providers, handling routing, failover, caching, and cost controls through one API. The strongest picks in 2026: LiteLLM for self-hosted control, OpenRouter for instant multi-model access, Portkey for managed governance, and Bifrost for production-scale throughput. Enterprises spent $37 billion on generative AI in 2025, a 3.2x jump in one year, per Menlo Ventures.

model-right-sizer-schema is a Claude Code skill that designs the typed contract between one agent and the controller that dispatches it. Point it at an agent plus its controller and it returns a JSON prescription with typed in/out fields, an exclusion list that keeps raw logs out of the reply, a before/after size delta, then writes the contract into the agent's own file. It picks from nine portable output-shape families, or your repo's own.