From AI Experimentation to Enterprise Infrastructure in 14 Days.
Cut 30–50% off model spend via intelligent gateway caching, deploy dedicated senior AI Dev Pods in 48 hours, and ship production-ready workflows with Zero Data Retention.
Shared savings: You keep 70% of every dollar saved; our fee is the other 30%. When a prompt genuinely needs the top model, it routes directly with zero fee. Zero upfront cost, full EU AI Act & GDPR compliance.
The Three Pillars of Your AI Infrastructure
Bridging FinOps cost reduction, agile engineering capacity, and enterprise sovereign compliance.
1. FinOps & Gateway Layer
Cut 30–50% off monthly inference bills via semantic caching and intelligent routing without changing a single line of backend code. Automatic 20ms failover across global cloud endpoints.
- Shared savings model: $0 upfront fee
- Automatic semantic caching & routing
- Real-time token cost & latency telemetri
2. Agile AI Dev Pods
Skip 3–6 month hiring delays. Onboard a dedicated, senior remote AI Dev Pod in 48 hours to design, deploy, and scale custom agentic workflows in 14-day production sprints.
- Senior AI/LLM engineers ready in 48 hours
- 100% intellectual property ownership
- Flexible month-to-month commitment
3. Sovereign EU Compliance
Engineered for strict European privacy and security standards. In-memory request processing with Zero Data Retention (ZDR) and full EU AI Act & GDPR compliance.
- Zero Data Retention (ZDR) guarantee
- Model-agnostic enterprise cloud integration
- Audit-ready logging and security controls
Calculate Your Estimated Net Savings
Slide to your organization's monthly token volume and model complexity to preview your 70/30 split.
Retail token prices are sourced from Artificial Analysis and shown in USD per 1M tokens.
Available models and prices are loaded from the same catalogue used for billing. Estimates assume a 3:1 input/output mix. Routing can change the model; set allow_cascade to false in API requests to keep your selected model.
The slider starts at this model's estimated net saving for routed calls, including the fee once. Lower it to model a mix of simple and complex prompts. These are price comparisons, not quality benchmarks or measured customer results.
Token Price Index (OTPI)
Compare public retail prices with illustrative optimisation scenarios. Actual cost and cache telemetry are recorded only after a provider-backed request completes.
Provider retail pricing is sourced from Artificial Analysis. Estimated optimised rates are calculated from the selected model estimate, not quoted provider prices.
| Model / Inference Engine | Provider Retail Rate (USD / 1M) | Estimated Optimized Rate | Estimated Cost Optimisation | Arbitrage Method |
|---|---|---|---|---|
| Loading verified prices… | — | — | Same-family cascade to Luna on simple turns | |
| Loading verified prices… | — | — | Same-family cascade to Luna on simple turns | |
| Loading verified prices… | — | — | Already the economy tier — no cascade | |
| Loading verified prices… | — | — | Same-family cascade to GPT-5.4 mini on simple turns | |
| Loading verified prices… | — | — | Same-family cascade to GPT-5.4 mini on simple turns | |
| Loading verified prices… | — | — | Already the economy tier — no cascade | |
| Loading verified prices… | — | — | Same-family cascade to Grok 4.3 on simple turns | |
| Loading verified prices… | — | — | Already the economy tier — no cascade | |
| Loading verified prices… | — | — | Same retail rate as 3.6 Flash — no cascade | |
| Loading verified prices… | — | — | Same retail rate as 3.8 Flash — no cascade | |
| Loading verified prices… | — | — | Same-family cascade to V4-Flash on simple turns | |
| Loading verified prices… | — | — | Already the economy tier — no cascade | |
| Loading verified prices… | — | — | Already the economy tier — no cascade |
How The Saving Is Produced
Three mechanisms, each visible in the response of every call you make.
Dynamic Model Cascading
Long prompts and prompts that ask for reasoning go to the model you requested. Short, simple turns are routed to a cheaper model in the same family. The routing decision is made from the prompt itself before the call, and the model that actually answered is named in every response.
- Routes only inside the family you asked for
- Reasoning requests are never downgraded
- Requested and served model in every response
Provider Cache Accounting
Where the provider reports that part of your prompt was served from its cache, those tokens are billed at the cache-read rate instead of the full input rate. The gateway records what the provider reported; it does not add a cache of its own.
- No code changes required
- Cache-read tokens billed at the lower rate
- Counts come from the provider, not an estimate
Dual-Rate Telemetry
Every response carries the retail price of the model you asked for, the price of the model that answered, the difference, and your share of it. The same figures accumulate in your ledger, so the invoice can be recomputed from the calls.
- Per-call retail price, paid price and difference
- Running totals in your own ledger
- $0 saved = $0 paid
Walkthrough of the three mechanisms above. The dual-rate example uses a routed OpenAI call on the public 3:1 estimate. It is not a guaranteed saving.
High AI inference costs? Review your model architecture in 15 minutes.
Book a direct, technical 15-minute Google Meet with Steffen Kirkegaard, CEO of ExecuteAI. We analyze your live prompt routing, cache hit ratios, and identify exact net savings before you write a single line of code.
Zero Data Retention (ZDR) &
EU AI Act Ready
Engineered for enterprise tech teams with strict compliance and customer confidentiality requirements across the Nordics, DACH, and Benelux.