LLM & AI Cost Optimization
I cut LLM API bills by routing easy requests to cheaper models, caching repeated calls, and trimming wasted tokens, all behind a gateway. Most teams I see are paying frontier prices for work a smaller model handles fine.
Sound familiar?
- ✕The LLM API bill grows every month and nobody can explain it
- ✕You send everything to the most expensive model out of habit
- ✕The same prompts get answered over and over with no caching
- ✕Prompts are bloated with context the model doesn't need
- ✕You're wondering whether self-hosting would be cheaper
- ✕You have no per-feature or per-user view of where spend goes
What you get
- ✓A clear breakdown of where your token spend actually goes
- ✓Cheaper requests routed to smaller models with quality held steady
- ✓Caching that kills repeated and near-duplicate calls
- ✓An LLM gateway for routing, caching, limits, and cost tracking
- ✓An honest self-hosting break-even based on your real usage
Most LLM bills are paying frontier prices for everyday work
The same waste that hides in cloud bills hides in LLM bills, just newer. Every request gets sent to the most expensive model out of habit, identical prompts get answered again and again with no cache, and context windows get stuffed with text the model never reads. I spend most days running Kubernetes and shipping AI infra, including the gateways that sit in front of LLM traffic, so I’ve seen where the money actually leaks. The fix is measurement first, then routing, caching, and token discipline.
What I help with
- Cost visibility. A breakdown of token spend by feature, model, and user, so you know what to optimize before you touch anything.
- Model routing. Easy requests to a smaller, cheaper model, hard ones to the frontier model, with quality measured so it holds.
- Caching. Exact and semantic caching to stop paying for the same answer twice.
- LLM gateways. A proxy for routing, caching, rate limits, fallbacks, and cost tracking across providers and self-hosted models.
- Token efficiency. Trimming bloated prompts and using provider prompt caching, so you stop paying for context the model ignores.
- Self-hosting analysis. An honest break-even on running open models yourself, based on your real volume rather than a hunch.
Why me for this
LLM gateways and model serving are my day job at TrueFoundry. Routing, caching, and cost controls at production scale are exactly the work. You get someone who has built the layer that does this, not someone reading about it for the first time on your bill.
How an engagement works
- Measure. Pull usage data and find where the token spend actually concentrates.
- Quick wins. Ship the safe, high-impact cuts (caching, routing the obvious cases) so savings hit the next bill.
- Structural fixes. Put a gateway out front, tune routing against quality evals, and decide the self-hosting question with real numbers.
- Lock it in. Dashboards and limits so the bill doesn’t quietly climb back up.
Frequently asked questions
How can I reduce my OpenAI API costs without hurting quality?+
Start by measuring where tokens go, because the bill is usually concentrated in a few features. Then route the easy requests to a smaller, cheaper model and keep the frontier model for the hard ones. Cache repeated calls. Trim prompts that carry context the model never uses, and use prompt caching where the provider supports it. Most teams cut 30 to 60 percent this way with no drop in answer quality, because they were overpaying for capability they didn't need.
What is an LLM gateway and will it save me money?+
It's a proxy in front of your models that handles routing, caching, rate limiting, fallbacks, and cost tracking across providers. The savings come from two places. You can route by cost and capability instead of hardcoding one expensive model, and you finally get per-feature spend data so you know what to optimize. If you're spending real money on LLMs, a gateway pays for itself quickly. This is squarely my day-job domain.
Is self-hosting an open model cheaper than paying for an API?+
Sometimes, and only above a certain volume. APIs win on time to market and frontier quality, with zero ops burden. Self-hosting wins on cost at high, steady volume, plus data control, but you take on GPU capacity, autoscaling, and uptime. I model your actual token volume and traffic shape and give you the real break-even number rather than a dogmatic answer either way.
How much can I realistically cut my LLM bill?+
For teams that haven't done a focused pass, 30 to 60 percent is common, and sometimes more. The biggest levers are routing cheaper requests to smaller models, caching, and cutting wasted tokens. I go after the largest line items first, so the savings show up fast rather than after a long project.
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