Cost Optimisation Strategies for AI in Production

The first version of an AI feature often feels cheap. A few test users, a neat demo, a small bill. Then the real world arrives. Support teams paste long threads into the chat box, customers ask follow-up after follow-up, and your “tiny” helper starts eating tokens like crisps at a pub.

Cost optimisation strategies for running AI in production are different from trimming a normal web app bill. You’re paying for compute time, tokens, storage, vector search, data transfer, logging, and the most expensive part of all, human time spent fixing odd edge cases and runaway workflows.

This guide gives a practical plan to cut cost without wrecking quality, latency, or safety.

Know what you’re paying for, and measure cost per outcome

Before you optimise anything, you need a clean picture of what’s actually driving spend. Production AI has more “hidden metres” than people expect, and they run even when users aren’t aware.

Here are the common cost drivers:

• Inference compute: CPU or GPU time to generate outputs.
• Tokens: input (prompt, context, tools) and output (model reply).
• Model hosting: managed endpoints, container clusters, warm replicas.
• Vector search: embedding generation, indexing, queries, storage.
• Data transfer: cross-zone or cross-region traffic, egress fees.
• Logging and tracing: storing text, prompts, tool outputs, metrics.
• Retries and timeouts: “just try again” quickly becomes “pay twice”.

The goal isn’t the smallest bill. It’s the best value per user result. If a feature saves a support agent ten minutes, a slightly higher per-request cost can still be a bargain. If it produces a nice paragraph that no one uses, it’s pure waste.

A simple habit that works is tracking “unit costs” weekly, not just monthly spend. Use metrics that match outcomes, not infrastructure.

If you want a broader FinOps framing for AI spend allocation and unit metrics, this overview is a useful reference: AI cost optimisation strategies for AI-first organisations.

Set a cost budget per feature, not just a monthly cloud budget

Monthly budgets are blunt. Production AI needs a smaller, sharper tool: a budget per feature.

Think in features users recognise:

• Chat assistant for customers
• Summarise call notes
• Classify inbound tickets
• Extract fields from documents

For each one, set a target cost per unit (per chat, per summary, per document). Then add guardrails that stop one power user or a runaway integration from swallowing the month.

Practical guardrails that don’t annoy most users:

Per-user daily limits: generous enough for normal use, strict enough to stop binge usage.

Per-workspace limits: one team shouldn’t burn the whole budget because they discovered a new workflow.

Safe defaults when limits hit: don’t just fail. Switch mode.

• Use a cheaper model for the remainder of the day.
• Force a shorter output (for example, bullet summary only).
• Delay and batch non-urgent tasks (run overnight).

This approach turns cost control into product design. Users feel a boundary, not a broken feature.

Watch for silent spend leaks: retries, timeouts, and over-logging

Some of the worst AI bills don’t come from “too many users”. They come from waste you don’t notice in dashboards.

Common leaks:

Retries on flaky upstreams: if your tool call fails and you retry three times, you’ve paid for three prompts and maybe three partial outputs.

Long timeouts: a 60-second timeout invites slow, expensive calls. Tighten timeouts, and design a fast “sorry, try again” path.

Repeated embedding jobs: teams re-embed the same documents because dedupe was skipped. Hash text and cache embeddings.

Storing full prompts and responses forever: this can become a privacy risk and a storage bill. It also increases the chance sensitive text sits around too long.

Verbose debug logs in production: logs that copy full user text, tool outputs, and model responses can grow faster than your database.

Better logging habits:

• Sample logs for high-volume endpoints (for example, 1 percent).
• Redact sensitive text early, before it reaches long-term storage.
• Store summaries and metadata (token counts, latency, model, route decision), not raw text.
• Move older logs to cheap storage tiers, with retention rules.

Treat logs like receipts in a wallet. Keep what you need for audits and fixes, not every scrap of paper since launch.

Cut inference and token costs without harming user experience

If production AI spend is a leaky bucket, tokens and inference are often the biggest hole. The good news is you can cut them without making the product feel worse. Users don’t care how many tokens you used. They care about speed, accuracy, and whether the answer helps.

This section focuses on reducing cost per request while keeping the experience solid. For cloud provider viewpoints that align with this approach, see Optimizing AI costs: Three proven strategies and Generative AI cost optimisation strategies.

Use the smallest model that meets the job, and route requests by difficulty

A common mistake is using one big model for everything. It’s like sending a lorry to deliver a sandwich.

A tiered setup usually wins:

• Small, cheap model: classification, extraction, routing, simple replies.
• Mid model: normal Q&A, summarisation, rewrite tasks.
• Large model: complex reasoning, multi-step analysis, hard edge cases.

The key is routing. Start with the cheapest route that can work, then escalate only when needed.

A simple fallback pattern:

1. Run the small model with a tight prompt and structured output.
2. Check confidence signals (rule checks, validation, or model self-rating with care).
3. Escalate only if the output fails validation, looks uncertain, or the user asks for refinement.

This keeps the “average” request cheap, while still protecting quality on the hard ones.

If you have stable tasks, a smaller fine-tuned model can also beat a general model on both cost and consistency. It’s not always the right move, but for repetitive work (like invoice field extraction) it can pay off quickly.

Shrink token use: shorter prompts, tighter outputs, and fewer round trips

Tokens are like taxi metres. You don’t feel them ticking, then you look down and wince.

Long context windows and long answers cost more. They also slow responses, which annoys users.

Ways to shrink token use without breaking the product:

Keep system prompts lean: remove repeated instructions, remove long examples, and avoid copy-pasting policy text into every request. Put stable rules into a short policy summary.

Summarise history: in chat, don’t send the full conversation every time. Summarise older turns into a compact “memory” and keep only the last few messages verbatim.

Use retrieval with small snippets: fetch only the passages you need. Cap the number of chunks and the total characters sent to the model.

Cap output length: set max output tokens by feature. A ticket tagger doesn’t need an essay.

Prefer structured outputs: JSON or key-value pairs are often shorter and easier to validate. They also reduce the chance the model rambles.

A concrete example: instead of asking “Write a full analysis and recommendations”, ask for:

• decision: approve or reject
• reason: one sentence
• next_step: one action

Users often prefer this. It reads like a good note from a colleague, not a blog post.

Cache and batch: stop paying twice for the same work

If your system answers the same question 1,000 times a day, paying 1,000 times is optional.

Caching saves money when requests repeat, or when the “expensive bit” is stable.

Good caching targets:

Response caching: FAQs, policy questions, product docs, help centre prompts.

Embedding caching: dedupe identical documents and paragraphs, then reuse embeddings.

Tool result memoisation: if an agent calls the same internal tool (for example, “fetch account status”) during one session, store the result and reuse it.

Batching helps when you have work that can wait, even a little:

• Nightly summaries
• Bulk tagging
• Backfills after a schema change
• Periodic “clean up” jobs

When you batch, you get better hardware use and fewer cold starts. You can also schedule batch jobs for cheaper compute windows.

When not to cache:

• Personal data that varies per user
• Time-sensitive answers (stock, availability, live incidents)
• Anything that changes often unless you use short cache lifetimes

Short cache lifetimes are a good middle ground. Cache for minutes or hours, not forever.

Put hard limits on agents and tool calling to prevent cost blow-ups

Agents are powerful, and also stubborn. If you give an agent unlimited steps, it can loop. Each loop can mean more tool calls, more tokens, and more latency.

Hard limits are your seatbelt.

Useful caps for production:

Max steps per request: stop after a fixed number of reasoning cycles.

Max tool calls: limit how many times it can hit search, databases, or APIs.

Max tokens per step: prevent a single step from producing a long internal chain.

Stop and ask the user: if the agent is unsure after two tries, ask a clarifying question instead of guessing and spending more.

Also consider an allow-list for tools. If a tool is expensive or risky, it shouldn’t be callable by default. This is cost control and safety control at the same time.

Optimise infrastructure spend: right hardware, right scaling, right pricing

Even with perfect prompts, infrastructure can waste money quietly. Production AI adds special wrinkles: GPU nodes, model warm-up, vector databases, and spiky traffic patterns where you pay for idle time.

For a broader overview of infrastructure cost tactics in AI workloads, this is a practical read: Cost optimisation strategies for AI workloads.

Rightsize compute and autoscale properly so GPUs don’t sit idle

The most painful sight in cost reports is an always-on GPU doing nothing. It’s like leaving every light on in the house, all month, because you might need the hallway at 3am.

Common waste patterns:

• Always-on GPU nodes for low-traffic endpoints
• Too many replicas “just in case”
• Oversized instances because the first benchmark was rushed

Better practice:

Autoscale with clear triggers: choose signals tied to real demand, such as queue depth, tokens per second, or p95 latency. Avoid triggers that lag too much.

Scale-to-zero where it’s safe: dev and staging should scale to zero by default. Low-traffic production features can too, if you can handle a cold start.

Separate real-time from batch: real-time endpoints need steady latency; batch workers can be slower and cheaper. If they share a pool, the batch jobs can keep expensive nodes hot for no good reason.

Rightsizing is not a one-off task. Models change, prompts change, traffic changes. Put a calendar reminder in place and treat it like any other reliability work.

Pick the best pricing model for each workload: on-demand, reserved, spot, and hybrid

Pricing choice is part of architecture. It changes your unit costs as much as model choice does.

A simple rule of thumb:

Steady traffic: reserved or committed use is often cheaper. You’re paying for predictability, so commit when you have it.

Bursty traffic: on-demand plus autoscaling is safer, because you can’t predict peaks.

Interruptible workloads (batch inference, training runs, backfills): spot or preemptible instances can cut cost, as long as your job can pause and resume.

Hybrid patterns work well in practice:

• Keep baseline load on committed capacity.
• Burst to on-demand when demand spikes.
• Push safe batch work onto spot capacity.

The last piece is discipline. If your “baseline” grows, re-check the commitment level. If your peaks get wilder, tighten feature budgets and rate limits so the business controls spend, not random user behaviour.

Conclusion: a practical checklist for this week

Production AI costs don’t explode in one dramatic moment. They creep up, one extra retry, one longer prompt, one agent loop at a time. The fix is steady attention, not a panic clean-up.

This week, do five things:

• Pick two unit metrics (like cost per 1,000 requests and cost per outcome).
• Set feature budgets with safe defaults when users hit limits.
• Route by difficulty, start small, escalate only when needed.
• Cap tokens, add caching, and stop paying twice for repeated work.
• Fix autoscaling so idle GPUs don’t burn cash.

Start with your biggest cost line item and make one focused change that can halve it. Cost control is a product habit, and the best time to build that habit is now.