Canonical URL: https://elacelerador.es/en/cases-and-research/the-agile-monkeys-ai-bill-privacy-deployment/ --- description: An AIFindr Privacy case compares two deployments performing the same function. The fall in estimated cost shows why a technical demonstration still needs a commercial calculation. title: The Agile Monkeys: the AI bill is decided outside the model image: https://elacelerador.es/images/social/v1/agile-monkeys-ai-bill-decided-outside-model-2e1d778392d73ded54c1701d1ec72382.jpg --- 1. [Home](https://elacelerador.es/en/) 2. [Cases and research](https://elacelerador.es/en/cases-and-research/) # The Agile Monkeys: the AI bill is decided outside the model An AIFindr Privacy case compares two deployments performing the same function. The fall in estimated cost shows why a technical demonstration still needs a commercial calculation. By [Marian Vasile](https://elacelerador.es/en/author/marian-vasile/) 13 October 2026 at 08:50 CEST 4 min read [ Add as preferred source ](https://www.google.com/preferences/source?q=elacelerador.es&utm%5Fsource=elacelerador.es&utm%5Fcampaign=elacelerador.es) Share [ ](https://www.linkedin.com/sharing/share-offsite/?url=https%3A%2F%2Felacelerador.es%2Fen%2Fcases-and-research%2Fthe-agile-monkeys-ai-bill-privacy-deployment%2F%3Futm%5Fsource%3Dlinkedin%26utm%5Fmedium%3Dsocial&utm%5Fsource=elacelerador.es&utm%5Fcampaign=elacelerador.es "Share on LinkedIn. 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Preview requests: 0") [ ](https://www.facebook.com/sharer/sharer.php?u=https%3A%2F%2Felacelerador.es%2Fen%2Fcases-and-research%2Fthe-agile-monkeys-ai-bill-privacy-deployment%2F%3Futm%5Fsource%3Dfacebook%26utm%5Fmedium%3Dsocial&utm%5Fsource=elacelerador.es&utm%5Fcampaign=elacelerador.es "Share on Facebook. Preview requests: 0") ![Illuminated server racks containing cabling and computing equipment.](https://elacelerador.es/images/variants/v1/agile-monkeys-ai-bill-decided-outside-model-illuminated-server-racks-containing-cabling-w1280.webp) Server racks at NOIRLab in Tucson, Arizona. Archive context photograph of infrastructure; it does not show an AIFindr deployment. Framing adapted. Photo: [NOIRLab/NSF/AURA/T. Slovinský · CC BY 4.0](https://commons.wikimedia.org/wiki/File:NOIRLab%5FHQ%5FServer%5FRacks%5F%286V6A0375-CC%29.jpg?utm%5Fsource=elacelerador.es&utm%5Fcampaign=elacelerador.es) **In this report** 1. [The full deployment changes the price of the promise](#article-section-the-full-deployment-changes-the-price-of-the-promise) 2. [The cheaper provider depends on volume and requirements](#article-section-the-cheaper-provider-depends-on-volume-and-requirements) 3. [The sale needs a defensible range of use](#article-section-the-sale-needs-a-defensible-range-of-use) 2 figures * [AI security assessment differs by organization size](#article-figure-1) * [Buying or building does not determine where data is processed](#article-figure-2) 1 tables * [The full deployment changes the price of the promise](#article-table-1) [Download data (CSV)](https://elacelerador.es/en/download.csv?article=%2Fen%2Fcases-and-research%2Fthe-agile-monkeys-ai-bill-privacy-deployment%2F) Chart values, table cells, source references and the report revision date. An AIFindr privacy layer moved from a design estimated at €1,343.76 per month to one estimated at €119.09\. The Agile Monkeys published the comparison in its August 20, 2026 case . The amounts include development, staging and production for that component. The difference changes what an AI provider can sell to a business. A model that performs the task can require a deployment too expensive for the volume it handles. Keeping it available creates a cost before the next customer arrives. ## The full deployment changes the price of the promise The case describes a layer removing personal data before subsequent product steps. AI Findr presents itself as conversational search for customers . Waiting for that layer adds to the time before a useful answer appears. The initial design kept four GPU machines running across its environments. The later design uses CPUs and reduces that infrastructure. The reported 91.1% saving concerns the privacy component, rather than the cost of all AI Findr or its customers’ spending. Scroll left or right to see all columns __The full deployment changes the price of the promise__ | Compared design | Estimated monthly component cost | | ---------------------- | -------------------------------- | | Initial GPU deployment | €1,343.76 | | Later CPU deployment | €119.09 | Expand notesCollapse notes These are the developer’s published estimates for that architecture. The comparison exposes the weight of the full deployment without turning a provider’s own case into a universal AI price. ## The cheaper provider depends on volume and requirements The document retains a decisive commercial qualification: at the case’s traffic level, the external API remained cheaper in direct inference spending. The private infrastructure also met a requirement to keep data inside the system, which a price per call cannot address. Security assessment is another part of that decision. In the World Economic Forum’s global 2024 survey, 31% of participants at organizations classified as small reported having a process to assess AI tools before deployment, compared with 59% at large ones. Having the process does not guarantee security; it shows why defining privacy conditions needs to precede comparing an API with private infrastructure. The data behind the story ### AI security assessment differs by organization size Reported assessment process before deployment, global 2024 survey. YesNo Tap or hover for details. Select a series above to show or hide it. Expand notesCollapse notes **Sources:** Statista; Original source World Economic Forum and Accenture: 321 qualified business and cyber leaders from 57 countries, September 2–October 11, 2024\. Size categories follow the report. The survey does not measure model risk or The Agile Monkeys’ customers. Explore the data table Scroll left or right to see all columns __AI security assessment differs by organization size (%)__ | Category | Yes | No | | -------- | --- | --- | | Small | 31% | 69% | | Medium | 46% | 54% | | Large | 59% | 41% | That tension prevents selling the same saving to every buyer. A business with little traffic and no such condition faces a different decision from one requiring private processing. The provider needs to explain the problem each cost pays to solve. The comparison between usage pricing and available capacity can change as traffic grows. It requires separating the fixed cost of keeping a service running from spending that increases with every query. An average lacking volume and architecture can make a proposition appear economical while it still consumes more resources than its alternative. Buying a solution does not require external inference. Menlo Ventures estimates that enterprise AI use cases purchased from vendors moved from 53% in 2024 to 76% in 2025 in its US studies. A private integration can be part of that purchase. For The Agile Monkeys, the commercial choice still requires explaining the component’s operating cost and responsibility for its data. The data behind the story ### Buying or building does not determine where data is processed Enterprise AI use cases bought or built internally, Menlo studies in the United States. PurchasedBuilt internally Tap or hover for details. Select a series above to show or hide it. Expand notesCollapse notes **Sources:** Statista; Original source Values and labels checked against Menlo: Statista reverses the series names in its table. Separate surveys: 600 IT decision-makers at enterprises with at least 50 employees in 2024; 495 decision-makers at companies already using AI in 2025\. These compare use cases, not company percentages or a longitudinal panel. Explore the data table Scroll left or right to see all columns __Buying or building does not determine where data is processed (%)__ | Category | Purchased | Built internally | | -------- | --------- | ---------------- | | 2024 | 53% | 47% | | 2025 | 76% | 24% | ## The sale needs a defensible range of use For The Agile Monkeys, the case’s commercial value is turning engineering experience into a specific conversation with the buyer. The data to protect, acceptable delay and expected traffic define the assignment. These are purchase conditions, rather than details to disclose after the project. The next proposal needs to include the operating component and the work required to maintain it. A quick demonstration can open the door; the operating calculation determines whether a customer can keep using it when it stops being a demonstration. The case is stronger because it does not present CPUs as the cheapest option in every circumstance. It shows an architecture reducing a fixed cost while meeting a specific requirement. That is a promise capable of sustaining commercial growth: a solution the customer can pay for and operate at its actual volume. [Back to top ↑](#article-top) ## In this article ### Sources and documents * [The Agile Monkeys: AIFindr Privacy case, August 20, 2026](https://www.theagilemonkeys.com/labs/posts/aifindr-privacy-private-fast-affordable/index.html?utm%5Fsource=elacelerador.es&utm%5Fcampaign=elacelerador.es) · www.theagilemonkeys.com * [AI Findr: conversational search and customer insights](https://www.aifindr.ai/?utm%5Fsource=elacelerador.es&utm%5Fcampaign=elacelerador.es) · www.aifindr.ai * Statista * [Original methodology and data: World Economic Forum / Accenture / Statista](https://reports.weforum.org/docs/WEF%5FGlobal%5FCybersecurity%5FOutlook%5F2025.pdf?utm%5Fsource=elacelerador.es&utm%5Fcampaign=elacelerador.es) · World Economic Forum / Accenture / Statista * [Original methodology and data: Menlo Ventures / Statista](https://menlovc.com/perspective/2025-the-state-of-generative-ai-in-the-enterprise/?utm%5Fsource=elacelerador.es&utm%5Fcampaign=elacelerador.es) · Menlo Ventures / Statista Share [ ](https://www.linkedin.com/sharing/share-offsite/?url=https%3A%2F%2Felacelerador.es%2Fen%2Fcases-and-research%2Fthe-agile-monkeys-ai-bill-privacy-deployment%2F%3Futm%5Fsource%3Dlinkedin%26utm%5Fmedium%3Dsocial&utm%5Fsource=elacelerador.es&utm%5Fcampaign=elacelerador.es "Share on LinkedIn. 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His work spans product marketing, SEO, paid advertising and deep online tracking, including behavioral analytics and attribution. ## Related articles [Cases and research](https://elacelerador.es/en/cases-and-research/) Analysis ### [SHIELD: a coupon’s cost depends on who claims it again](https://elacelerador.es/en/cases-and-research/shield-coupon-cost-accounts-legitimate-customers/) [Marian Vasile](https://elacelerador.es/en/author/marian-vasile/) 17 October 2026 6 min [Cases and research](https://elacelerador.es/en/cases-and-research/) Analysis ### [Safeguard Global: the calculator pricing two routes to expansion](https://elacelerador.es/en/cases-and-research/safeguard-global-calculator-eor-entity-expansion-cost/) [Marian Vasile](https://elacelerador.es/en/author/marian-vasile/) 17 October 2026 6 min [Cases and research](https://elacelerador.es/en/cases-and-research/) Analysis ### [AIOS and Bolt: a three-minute promise needs to explain the review that follows](https://elacelerador.es/en/cases-and-research/aios-bolt-consultation-approval-conversion/) [Marian Vasile](https://elacelerador.es/en/author/marian-vasile/) 17 October 2026 5 min **In this report** 1. 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