What FLUX saves, segment by segment

Savings are a property of your workload's shape. One global percentage would be a lie to someone; these are the four shapes, each with its mechanism, its measured band, and what would change the number. All figures simulator-measured per methodology; traceability in evidence.

Background / batch / latency-tolerant work — 57.7% net (64.3% ceiling)

Mechanism: your provider sells the same model at 50% off for work that can wait (OpenAI's flex service tier on a synchronous API; Anthropic's batches, which stack with prompt caching). FLUX applies the tier only to traffic you flag latency-tolerant, stacks it with every token lever below, and bills from the tier the provider *actually served* — bounced requests bill at full rate. The number to use is 57.7% (net of assumed 10% default-fallback + 5% retry rates); 64.3% is the zero-fallback ceiling. Quality: identical model, identical answers; 38/40 clean on the harness. What changes it: your real fallback rate (telemetry replaces the assumption), and Anthropic-side pricing once its batch rate is catalogued.

One-shot API traffic on a shared template — 45.5%, up to 72.8%

Mechanism: the most common silent waste we model: a per-request value (a timestamp, a request id) at the top of an otherwise identical prompt, which disables provider caching entirely. FLUX relocates it, the prefix caches at one-tenth price, and schema/duplicate levers stack on top. Add the tier for latency-tolerant one-shots and the band reaches 72.8%. What changes it: how big your shared template is and whether a breaker is actually present — the five-minute quote measures both from your export.

Interactive agents — 25.9–29.4% busy, 34.0% sparse

Mechanism: busy agent traffic is already heavily subsidised by the provider's cache (35–40% on its own, which we never claim), so FLUX's honest headroom is the *fresh* tokens: oversized tool results are capped at injection (headlines, errors and file paths preserved verbatim; the replay gate approved this lever at exactly zero quality delta), stale history is compacted byte-stably, schemas are slimmed. Sparse traffic adds the 1-hour cache TTL (2× write premium billed honestly, netted when negative) for 34.0%. What changes it: how oversized your tool results actually are (our fixture's are highly compressible; your yield scales with yours), and the pending per-model cache-minimum fix, which may lower the sparse figure on some models. On Claude the ceiling is different — and higher. Anthropic's prompt cache is opt-in, and agents that never place cache_control re-pay full rate for their whole conversation history every turn (our first live export measured exactly that: a five-turn agent with zero cache traffic). FLUX stamps the stable prefix with a 5-minute-TTL breakpoint. In the deterministic fixture, the cache-only comparison is 37.6% lower cost than the identical J prompt stack with caching disabled; both arms have 38/40 clean tasks and the stamp is metadata-only. The earlier 57.1% full-stack comparison used a raw uncached baseline with 40/40 clean tasks, while the candidate had 38/40 (−5 pp). That fails the agreed −2 pp quality limit, so 57.1% is diagnostic and not a qualified full-stack saving. Real-traffic cache validation remains open. If you already place your own breakpoints, FLUX defers and never claims this. This is also where we tell you our ceiling: if someone promises an interactive agent 70%, ask for their per-task evidence.

Already-optimised stacks — 0–5%: do not pay us

Mechanism: none, and that is the point. If your input already arrives largely cached, or your gateway's response cache already answers a big share of requests, FLUX's triage says so and the quote's verdict is doNotPayUs. On our fixture of such a stack the addressable saving was 2.4%. A percentage-of-savings vendor that charges you for savings you already had is buying a dispute; we decline up front.