Case study
$12,000/mo on a single OpenAI account. After routing: $4,800/mo. 60% reclaim, 3-week payback, $86K year-1 savings.
Numbers anonymized; everything else verbatim from the audit deliverable. This is exactly the shape your report will take — populated with your numbers.
§1
Customer A is spending ~$12,000/mo on LLM inference, concentrated on OpenAI (100% of spend). This audit identifies $7,200/mo of recoverable spend (60%) from routing call types to lower-cost providers with parity-or-better quality, plus a fallback chain that holds availability above 99.5%.
Implementation is one engineer-week (or $15K turnkey via the implementation engagement). ROI: payback in 3 weeks; year-1 savings ~$86,400.
| Metric | Today | After routing |
|---|---|---|
| Monthly LLM spend | $12,000 | $4,800 |
| Single-provider exposure | 100% (OpenAI) | < 30% per provider |
| p99 inference latency | 2,400 ms | ≤ 1,200 ms |
| Cost per active user | $2.40 | $0.96 |
§2
| Call type | Share of spend | Monthly |
|---|---|---|
| Text (classify, extract, translate) | 55% | $6,600 |
| Vision (image classify, OCR, hero rank) | 25% | $3,000 |
| Grounded search (event/studio discovery) | 8% | $960 |
| Brand-visible writing (emails, marketing) | 5% | $600 |
| Embeddings (RAG retrieval) | 7% | $840 |
Concentration risk: 100% of inference flows through one provider and one billing entity. A pricing tweak, rate-limit, or policy change at OpenAI degrades or fails every AI feature in the product.
§3
Current path: OpenAI gpt-4o for classify, extract, and translate.
Recommendation: Route through Groq (Llama 3.3 70B) as primary. Cerebras (Qwen 2.5 72B) as failover. Anthropic Sonnet 4.6 as final fallback.
Quality vetting: Llama 3.3 70B benchmarked within 2-4 points of Sonnet 4 on MMLU and within margin on Customer A's actual classify prompts (94.8% vs 95.6% accuracy across 200 sampled requests).
Cost delta: $6,600 → $1,320/mo · 80% reduction
Current path: OpenAI gpt-4o vision for image classification, OCR, and hero ranking.
Recommendation: Vertex Gemini 2.5 Flash. Among the cheapest production-grade vision models. Anthropic Sonnet vision as failover.
Quality vetting: Gemini 2.5 Flash classified Customer A's image batches at 95.4% accuracy vs gpt-4o's 96.1% across 500 sampled images. Within margin for the use case.
Cost delta: $3,000 → $900/mo · 70% reduction
Current path: OpenAI gpt-4o + a hand-rolled web-fetch wrapper.
Recommendation: Move to Vertex Gemini grounded search. It returns Google Search citations natively and the cost premium vs ungrounded is minimal. Don't roll your own with Bing API + RAG.
Cost delta: roughly flat. Major win is reliability, not cost.
Current path: OpenAI gpt-4o for customer email + marketing copy.
Recommendation: Switch to Anthropic Claude Sonnet 4.6. Tone control matters here and the cost difference is small relative to total bill. Do NOT downshift this category to a cheaper text provider — brand voice is the asset that's most expensive to repair if it drifts.
Cost delta: roughly flat. Quality upgrade, no spend change.
Current path: OpenAI text-embedding-3-small.
Recommendation: Keep on text-embedding-3-small. It's already on the cheap end and re-embedding the corpus to swap providers costs more than a year of savings. No change.
Cost delta: $0.
§4
A single function takes a task-type discriminator and routes accordingly. Drop into the codebase, feature-flag the rollout.
type TaskType = 'classify' | 'extract' | 'translate'
| 'brand_voice' | 'vision' | 'grounded' | 'embed';
const ROUTING: Record<TaskType, ProviderChain> = {
classify: ['groq:llama-3.3-70b', 'cerebras:qwen-3-32b', 'anthropic:claude-sonnet-4-6'],
extract: ['groq:llama-3.3-70b', 'cerebras:qwen-3-32b', 'anthropic:claude-sonnet-4-6'],
translate: ['groq:llama-3.3-70b', 'cerebras:qwen-3-32b', 'anthropic:claude-sonnet-4-6'],
brand_voice: ['anthropic:claude-sonnet-4-6'],
vision: ['vertex:gemini-2.5-flash', 'anthropic:claude-sonnet-4-6'],
grounded: ['vertex:gemini-2.5-pro-grounded'],
embed: ['openai:text-embedding-3-small'],
};For Customer A's traffic mix, this distribution lands at roughly: Groq 50% · Cerebras 6% (failover) · Vertex 28% · Anthropic 8% · OpenAI 8% (embeddings retained).
§6
| Implementation effort | 1 eng-week (or $15K turnkey) |
| Engineering cost (loaded) | $5,000 |
| Audit fee | $5,000 |
| Monthly savings | $7,200 |
| Payback period | ~3 weeks (audit + 1 eng-week) |
| Year-1 net savings | $76,400 |
| Year-2 net savings | $86,400 |
§7
implementation-snippets/ into the repo. Set provider env vars. Wire routeByTask() behind a feature flag at 10% traffic.§10
Routing live in prod, telemetry in place, $7,200/mo confirmed in actual savings (compared to the pre-rollout 30-day window).
A/B-tested model variants within Groq (Llama 3.3 70B vs Qwen 2.5 72B) on classify workloads, picked the winner. Embedding model audited and confirmed.
Cost-per-active-user dashboard live. Quarterly model-review process documented (provider releases happen ~monthly; the process makes them tractable without burning eng time on every announcement).
Yours uses your numbers. Same shape, same depth, same production-ready code drop.
$5,000 fixed-fee · 1 week turnaround · 100% refund within 7 days