Why are my LLM costs so high?

The usual culprits: every request goes to a premium model regardless of difficulty, prompts carry redundant context that inflates input tokens, repeated questions are re-answered instead of cached, and no budgets or rate limits cap runaway usage. Most teams suffer all four at once because costs were never attributed per feature.

What is the fastest way to reduce LLM costs?

Model routing. Classify requests by difficulty and send only the hard ones to frontier models, routing simple classification, extraction, and formatting tasks to models that cost a fraction as much. Teams typically find a large share of traffic doesn't need the premium model.

What is semantic caching?

Semantic caching recognizes when a new request means the same thing as a previous one — even with different wording — and serves the cached answer instead of calling the model. For workloads with repetitive queries, like support bots and internal knowledge search, it eliminates the cost of that repeated traffic and returns answers instantly.

How do I attribute AI costs to teams and features?

Route all AI traffic through a gateway that tags each request with its team, application, feature, and customer, and meters token usage per tag. This converts one opaque provider invoice into a cost breakdown you can act on. Net3 Gateway does this tagging and metering automatically.

Do smaller models hurt quality?

Not when routed correctly. The skill is matching task difficulty to model capability: frontier models for complex reasoning and generation, small models for classification, extraction, and reformatting. Measure quality per task with evals before and after routing changes — most teams find no measurable difference on the simple tier.

LLM Cost Optimization: 9 Proven Strategies to Cut AI Spend

LLM costs are unusual among infrastructure costs: they scale directly with success. More users, more features, more agents — more tokens. Teams that treat cost as an afterthought routinely watch inference spend grow into one of their largest line items. The good news: AI spend responds to engineering, and the same handful of strategies work almost everywhere.

Here are nine, roughly in order of impact.

1. Route by task difficulty

The single biggest lever. Frontier models are priced for hard problems, yet most production traffic is easy: classification, extraction, summarization of short texts, reformatting. Send each request to the cheapest model that meets its quality bar.

Task type	Model tier	Relative cost
Complex reasoning, generation, agents	Frontier	High
Summarization, drafting, RAG answers	Mid-tier	Fraction of frontier
Classification, extraction, routing	Small	Cents on the dollar

Multi-model routing through a gateway like Net3 Gateway makes this a configuration change instead of an engineering project.

2. Cache semantically

Users ask the same questions in different words. Semantic caching detects that "How do I reset my password?" and "password reset steps?" are the same query and serves the cached answer — zero tokens, near-zero latency. Support and knowledge-search workloads see a meaningful share of traffic served from cache.

3. Put prompts on a diet

Input tokens are money. Common bloat: entire documents pasted where excerpts suffice, verbose system prompts repeated on every call, unbounded chat history dragged along forever. Trim system prompts, window or summarize conversation history, and retrieve only the passages a query needs.

4. Cap output length

Output tokens usually cost more than input tokens. Set explicit maximum lengths per use case and instruct models to be concise where verbosity adds nothing. Structured-output tasks (JSON, labels) should have tight caps — there's no reason a classification response runs to paragraphs.

5. Set budgets and rate limits

Every key, team, and customer should have a quota. Runaway loops, abusive users, and test scripts pointed at production have generated legendary invoices. Hard budgets convert "unbounded downside" into "capped and alerted." This is table stakes in Net3 Gateway's cost controls.

6. Attribute everything

You cannot optimize what you cannot see. Tag every request with team, feature, and customer, and meter tokens per tag. The first attributed cost report almost always surprises: one feature or one customer dominating spend nobody knew about. Net3 Monitor turns this attribution into dashboards and alerts.

7. Batch and debounce

Many workloads don't need one call per event. Batch classification jobs, debounce rapid-fire user input, and consolidate multiple small calls into one structured request where quality allows. Fewer round-trips also means fewer input-token repetitions of the same system prompt.

8. Use provider price asymmetries

Providers price aggressively in different places — batch APIs, cached-input discounts, off-peak capacity, smaller model families. A gateway that can route across providers lets you arbitrage those differences without code changes, and shift when pricing shifts.

9. Evaluate before and after

Cost optimization without quality measurement is just degradation with extra steps. Maintain small eval sets per task, run them before and after routing or prompt changes, and let data — not vibes — decide whether the cheaper path holds quality.

The pattern behind all nine

Every strategy above depends on one architectural fact: all AI traffic flows through a single layer you control. That's where routing happens, caching lives, budgets are enforced, and attribution is captured. Scattered direct-to-provider calls can't be optimized; unified traffic can. It's the same control plane that makes security and observability tractable — cost is simply the third thing the choke point buys you.

The bottom line

AI spend is not weather — it's a system you can engineer. Route by difficulty, cache aggressively, trim tokens, cap and attribute everything, and verify quality with evals. Teams that do this treat cost as a product metric and routinely support multiples of their original traffic on the same budget.