
Token Budgeting: How To Think About AI Cost Control
Learn how to budget AI token spend across teams and developers by using cost allocation, unit costs, and efficiency metrics instead of raw usage alone.
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Learn how to budget AI token spend across teams and developers by using cost allocation, unit costs, and efficiency metrics instead of raw usage alone.

AI coding tool spend is usage-based, variable, hard to predict, and skewed by power users. It's cloud infrastructure economics all over again, and it needs the same FinOps treatment.

Per-developer AI spend data creates an instinct to rein in the top spenders. But raw cost without a denominator is meaningless - here's how to measure what agentic coding actually delivers.

Per-token pricing gets all the visibility, but agentic sessions have a completely different cost structure. Input tokens, context accumulation, and session length are what actually drive the bill.

A recap of our recent webinar on applying FinOps practices to AI token costs - from provider data gaps to developer-level attribution and model switching tradeoffs.

Cursor adoption scales fast. Visibility into what's driving the bill usually doesn't. Here's how to understand what you're spending and how to get granular cost tracking set up.

Cursor just shipped a model that beats Opus 4.6 on coding benchmarks at a tenth of the per-token price. Here's what Composer 2's pricing means for your team's AI coding spend.

Instance sizing and reservations get all the attention, but how your workloads actually execute is often the bigger line item. Here's how to find and fix workflow waste.

Compare Anthropic and OpenAI direct API pricing side by side, from per-token costs to caching discounts and billing models.

How AWS edge locations reduce latency through caching, what CloudFront delivery costs at scale, and when edge caching helps or hurts your cloud bill.