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Cost Management for AI Features

5 min read

Platform

You own infra costs. AI is another line item. Meter it, alert on it.

Data Eng

Embeddings and LLM calls scale with volume. Back-of-envelope before you ship.

Eng Manager

AI features need cost KPIs. Per-user, per-request, or per-outcome.

Cost Management for AI Features

TL;DR

  • AI costs scale with usage. One viral feature = 10x traffic = 10x bill. Model it before launch.
  • Levers: model choice, prompt length, caching, rate limits, and fallbacks to cheaper paths.
  • Meter everything. Set alerts. Have a kill switch.

Where the Money Goes

ComponentCost Driver
LLM completionInput tokens + output tokens. GPT-4 Turbo ≈ $0.01/1K input, $0.03/1K output
Embeddings~$0.0001/1K tokens. Cheap per call, huge at scale
RAG retrievalVector DB costs. Usually smaller than LLM
Fine-tuningUpfront. Rare for most apps

Rough math: 1M input + 500K output tokens/day on GPT-4 Turbo ≈ $25/day ≈ $750/month. One feature. One model.

Budget Before You Build

  1. Estimate usage. How many requests/day? Requests × avg tokens in × price in + avg tokens out × price out.
  2. Pick a ceiling. "This feature should never exceed $X/month."
  3. Set alerts. At 50%, 80%, 100% of budget. Slack, PagerDuty, email — whatever you use for cost alerts.

Optimization Levers

Model choice. GPT-4 is expensive. GPT-4o-mini, Claude Haiku, or smaller models are 5–10x cheaper for many tasks. Test quality; often it's good enough.

Prompt length. Shorter prompts = fewer input tokens. Trim context. Don't stuff the whole doc if a summary works.

Caching. Same prompt + same context = same output. Cache by hash of (prompt, context). Cache TTL depends on how often context changes. Big wins for high-volume, low-variance use cases.

Rate limiting. Per user, per IP, or per org. Prevents abuse and runaways.

Fallbacks. Simple queries → cheap model or rule-based. Complex → expensive model. Route by intent or confidence.

Metering

Log every call:

  • Model
  • Input tokens, output tokens
  • Latency
  • User/org/session (for attribution)

Aggregate: daily, by feature, by model. Dashboard it. Alert on anomaly (e.g., 2x yesterday's spend).

Kill Switch

When costs explode: ability to turn off the feature or route to a cheaper path. Feature flag, config, or emergency endpoint. Better to degrade gracefully than to wake up to a $50K bill.

// Log every LLM call. Aggregate by feature, model, org.
interface LLMCallLog {
model: string;
inputTokens: number;
outputTokens: number;
latencyMs: number;
feature: string;
userId?: string;
}
// Aggregate: daily spend by feature. Alert on 2x yesterday.
// Kill switch: feature flag routes to cheaper model or disables feature.

Quick Check

Your AI feature goes viral. Traffic 10x. What should you have in place?

Do This Next

  1. Calculate — Pick one AI feature. Estimate monthly cost at 1x, 5x, 10x current traffic. What's the number?
  2. Add metering — Log tokens per request. Aggregate. See real numbers.
  3. Set one alert — When daily spend exceeds $X, notify. Adjust X based on your budget.