Your LLM bill is a cost center hiding in plain sight
AI API spend grows quietly until it's a real line item — then keeps growing. Nexargate's token optimization practice cuts LLM costs 40–70% through prompt engineering, caching, model routing, and context management. Same output quality. Audited before and after.
Where the tokens leak
Every AI-powered product and internal stack we audit shows the same leaks. None require re-architecture to fix.
- closeFlagship models running tasks a model one-third the price handles identically.
- closeFull documents and chat histories re-sent on every call — paying repeatedly for the same unchanged context.
- closeZero per-feature cost attribution: the bill is one number, so nobody owns reducing it.
What you get
Token Audit
Two-week measurement of spend by feature, endpoint, and prompt: where every dollar goes, and the ranked list of what to fix first.
Prompt Engineering
Prompts rewritten for token efficiency — compressed instructions, tightened outputs, trimmed few-shot examples — validated against quality baselines.
Model Routing & Caching
Right-sized model per task with automatic escalation, plus prompt caching for repeated context — the two biggest levers, typically 30–50% alone.
Guardrails & Monitoring
Per-feature cost dashboards, budget alerts, and regression checks so savings survive the next deploy instead of eroding by Q3.
How it works
- 1
Baseline (Weeks 1–2)
Instrument current usage. Cost per feature, per call, per token — plus output quality benchmarks so 'same quality' is provable, not claimed.
- 2
Quick wins (Weeks 2–4)
Caching, obvious model downgrades, prompt compression. Most engagements recoup the fee inside this phase.
- 3
Structural (Weeks 4–8)
Routing logic, context management, batch processing, output-length controls — the durable 40–70% architecture.
- 4
Lock in (Week 8+)
Monitoring, alerts, and a cost-review cadence handed to your team. Optionally: quarterly re-audits as models and prices shift.
Frequently asked questions
Will output quality drop?add
No — that's the audited constraint. Every change ships behind a quality benchmark built in week one; anything that degrades outputs gets reverted. Savings that break the product aren't savings.
Which providers do you optimize?add
Anthropic (Claude), OpenAI, Google (Gemini), and open-source deployments. The levers — caching, routing, context discipline, prompt efficiency — are provider-agnostic; the implementations are provider-specific.
How much do teams actually save?add
Typical range: 40–70% of monthly LLM spend, driven mostly by routing and caching. The week-two audit gives you a projected number for your stack before you commit to the full engagement.
We're spending under $2K/month — worth it?add
Probably not yet as a paid engagement — the audit checklist in our free playbook covers the basics. It becomes worth it around $5K+/month, or earlier if AI costs scale directly with your user growth.
Related services
Find out what you're overspending
Free 30-minute review of your AI stack. We'll estimate your savings range before you commit to anything.