Cost Optimization for AI Infrastructure

If You Remember Nothing Else

AI cost optimization compounds. Each lever is 20 to 50% savings; combined, they routinely deliver 60 to 80% cost reduction without quality loss.

The priority order matters. Right-size the model first (the largest single lever), then quantize, then cache, then capacity-mix, then continuous batching. Most teams skip the first two and start at the bottom of the list, which is why their optimization efforts plateau at 30%.

If your AI bill is growing faster than your usage, the team has skipped optimization steps. The first question to ask: "Are we using the right-sized model?" The answer is usually no. Smaller, fine-tuned, quantized models match larger frontier models on most production tasks at 50 to 100x lower cost.

Recommendations by Situation

Worked Examples

Example 1: SaaS reducing AI bill 75% (compound effect)

A SaaS company's AI bill hit $35K per month and was growing 15% per month. They wanted to reduce cost without quality loss.

The right approach (in order):

1. Right-size: replaced GPT-4 with fine-tuned Llama 3.1 8B for the 80% of queries that didn't need frontier reasoning. Saved $20K per month.
2. Quantize: deployed AWQ INT4 on the fine-tuned model. Saved another $5K per month.
3. Cache: added response caching for repeated queries. Hit rate of 35%. Saved $3K per month.
4. Capacity mix: moved from on-demand to 1-year reserved + spot for batch. Saved $1.5K per month.

Total monthly cost after optimization: $5.5K. Saving: $29.5K per month, $354K per year. Quality validation showed no measurable degradation on production tasks.

What worked: priority order. Right-sizing alone saved more than all the other optimizations combined. Starting with the biggest lever made everything else additive.

What they nearly got wrong: starting at the bottom of the list. The team initially proposed quantization first ("low risk, easy win"). Right-sizing was off the table because "we'd need to rebuild." The cost analysis forced the priority change.

What to remember: the first question is always model size. Most cost optimization plateaus at 30 to 40% because teams skip this lever. Right-sizing is the largest single lever; everything else compounds on top.

Example 2: High-volume batch workload (capacity mix)

A platform processes 500M tokens per day, 30% of which is batch-eligible (analytics, content backfill).

The right approach: hybrid capacity. Real-time workload runs on 1-year reserved A100s ($1,500 per GPU per month). Batch workload runs on spot A100s ($800 per GPU per month). Combined cost: about $25K per month. Pure on-demand would have been $80K per month. Pure reserved would have been $50K per month.

What worked: matching capacity to workload type. Reserved for predictable steady-state, spot for genuinely interruptible batch.

What they nearly got wrong: avoiding spot due to interruption risk. The batch workloads were checkpointed and retryable; spot interruptions added a few percent overhead at most. The 50% cost saving on batch workload was worth the engineering effort.

What to remember: spot is the right default for any interruptible workload. Build the retry logic once; save 50 to 70% recurring.

Example 3: Caching to reduce input token cost (high cache hit workload)

An enterprise copilot serves 50M tokens per day. The team noticed about 80% of queries used the same large system prompt (about 4,000 tokens of company context).

The right approach: prompt caching via Anthropic's caching feature. The system prompt is cached after the first request; subsequent requests pay 10% of the original input cost for the cached portion. Cache hit rate: 95%.

Result: input token cost dropped from $12K per month to $4K per month. Output token cost unchanged. Total saving: $8K per month with zero engineering work beyond enabling the feature.

What worked: recognizing the high-cache-hit pattern. Workloads with shared system prompts are exactly what prompt caching is designed for.

What they nearly got wrong: ignoring caching as "an optimization to consider later." Anthropic's prompt caching is a flag in the API call; the only investment was identifying the workload as cache-eligible.

What to remember: prompt caching is free money for workloads with shared prompt prefixes. Always check whether your workload qualifies; the engineering cost is near-zero.

Anti-Patterns to Watch For

"We'll optimize later when cost becomes a problem"

What it looks like: deferring cost optimization until a budget crisis.

Why it's wrong: by the time cost becomes a crisis, optimization is harder. Caching, capacity reserves, and batching all work better when designed in.

How to redirect: bake cost optimization into the architecture from day one. Right-size the model, enable caching, set capacity strategy. The savings start immediately.

"Quantization is risky, let's stick with FP16"

What it looks like: avoiding quantization due to perceived quality risk.

Why it's wrong: AWQ INT4 and FP8 are well-validated production techniques. Quality loss is typically under 1 point on real workloads. The cost savings (40 to 75%) dwarf the risk if you validate.

How to redirect: validate quantization on a held-out test set before shipping. The validation takes a week; the savings are recurring.

"Reserved capacity locks us in"

What it looks like: avoiding reserved capacity due to lock-in concerns.

Why it's wrong: reserved capacity is 30 to 60% cheaper than on-demand. For predictable steady-state workloads, the lock-in is fine because you'd be using the capacity anyway.

How to redirect: forecast 12-month usage. The portion that's predictably steady should be reserved. Stay on-demand for the unpredictable portion.

"Caching adds complexity"

What it looks like: avoiding caching due to implementation complexity.

Why it's wrong: prompt caching (provider-side) is a flag in the API call. Response caching is a Redis lookup. Neither is genuinely complex.

How to redirect: identify the highest-value caching opportunities (shared system prompts, repeated queries) and implement them first. Each takes days, not weeks.

"We need observability before we can optimize"

What it looks like: spending months on observability before any optimization work.

Why it's wrong: you can ship the first three levers (right-sizing, quantization, caching) without sophisticated observability. Observability matters most for ongoing optimization, not the initial wave.

How to redirect: ship the obvious savings first. Build observability in parallel for the next round of optimizations.

When Cost Optimization Isn't Worth Doing

Specific cases where you should accept the cost:

• Volume is too low to matter. At under 1M tokens per day, optimizing 50% saves a few hundred dollars per month; not worth meaningful engineering investment.
• The product is in early stages and pivoting frequently. Optimization assumes stable workloads.
• The team has zero capacity. Optimization without engineering investment isn't possible; if you can't allocate the time, accept the cost.
• Regulatory constraints prevent the optimization (e.g., specific provider required, no caching of certain queries).

In these cases, accept the cost as the cost of doing business. Revisit when scale or stability changes.

What to Ask Your Engineering Team

1. What's our current cost breakdown by feature, by user, by query type? If they can't answer, observability is the first lever.
2. What's our model right-sizing analysis? Have we tested smaller fine-tuned models against frontier APIs?
3. Are we quantizing? If not, why not? AWQ INT4 should be the default.
4. Are we using prompt caching? If we have shared system prompts, the savings are immediate.
5. What's our capacity mix? Pure on-demand at scale is wasteful.
6. What workloads are batch-eligible but currently real-time? Audit; many "real-time" workloads aren't.
7. Where's the next 20% saving going to come from? If the team can't answer, there's no optimization roadmap.

Cost & Timeline Quick Reference

Typical savings per lever, in priority order:

Combined, these deliver 60 to 80% cost reduction at minimal quality risk. Most teams have only pulled 2 or 3 of these levers.

The Bottom Line

AI cost optimization is engineering discipline, not a one-time project. The teams that spend less on AI infrastructure aren't lucky; they're rigorous about pulling all the levers.

Right-size the model first. Quantize second. Cache third. Capacity-mix fourth. Skip the priority order and your optimization plateaus at 30%; follow it and you reach 60 to 80%.

If your AI bill is growing faster than your usage, the team has skipped optimization steps. Start with model right-sizing and work down the list. The savings compound.