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| schema: foundry-doc-v1 | schema: foundry-doc-v1 |
| title: "Economic Model — Community and SMB Customer Tiers" | title: "Economic Model — Community and SMB Customer Tiers" |
| slug: economic-model | slug: economic-model |
| category: architecture | category: architecture |
| type: topic | type: topic |
| quality: published | quality: published |
| short_description: "PointSav's two-tier commercial structure: a free Community tier that serves as an adoption funnel, and a paid SMB Customer tier targeting regulated small-to-medium businesses that hyperscalers cannot serve economically." | short_description: "PointSav's two-tier commercial structure: a free Community tier that serves as an adoption funnel, and a paid SMB Customer tier targeting regulated small-to-medium businesses that hyperscalers cannot serve economically." |
| status: active | status: active |
| bcsc_class: public-disclosure-safe | bcsc_class: public-disclosure-safe |
| last_edited: 2026-05-01 | last_edited: 2026-05-01 |
| editor: pointsav-engineering | editor: pointsav-engineering |
| cites: [] | cites: [] |
| paired_with: economic-model.es.md | paired_with: economic-model.es.md |
| --- | --- |
| PointSav's commercial structure has two tiers. There is no Enterprise tier. The decision is not a positioning choice — it is a structural one. Regulated small-to-medium businesses occupy a market segment hyperscalers cannot reach economically, and the platform is designed entirely around serving that segment. | PointSav's commercial structure has two tiers. There is no Enterprise tier. The decision is not a positioning choice — it is a structural one. Regulated small-to-medium businesses occupy a market segment hyperscalers cannot reach economically, and the platform is designed entirely around serving that segment. |
| ## The two tiers | ## The two tiers |
| **Community** is the free tier. It operates under an AGPL-3.0-or-later license. A Community deployment consists of one ToteboxOS archive and one ConsoleOS terminal, with local model inference available as an optional component. Community is the adoption funnel: it generates contributors, surfaces edge cases, and makes the substrate legible to future customers. PointSav earns no revenue from Community deployments. | **Community** is the free tier. It operates under an AGPL-3.0-or-later license. A Community deployment consists of one ToteboxOS archive and one ConsoleOS terminal, with local model inference available as an optional component. Community is the adoption funnel: it generates contributors, surfaces edge cases, and makes the substrate legible to future customers. PointSav earns no revenue from Community deployments. |
| **SMB Customer** is the revenue tier. It operates under a Functional Source License with an Apache-2.0 fallback after the Delay Open-Source Publication period, plus a commercial license where required. SMB Customer deployments include multi-archive aggregation via os-orchestration, GPU burst capability, federated LoRA marketplace participation, and priority access to updated base models as they are produced. The commercial relationship is an Order Form per customer. | **SMB Customer** is the revenue tier. It operates under a Functional Source License with an Apache-2.0 fallback after the Delay Open-Source Publication period, plus a commercial license where required. SMB Customer deployments include multi-archive aggregation via os-orchestration, GPU burst capability, federated LoRA marketplace participation, and priority access to updated base models as they are produced. The commercial relationship is an Order Form per customer. |
| ## Why no Enterprise tier | ## Why no Enterprise tier |
| The addressable customer for an Enterprise-tier AI platform typically runs annual contract values above $500,000. Hyperscalers are structured for that segment — their sales organisations, compliance certifications, and infrastructure commitments reflect it. PointSav is not equipped to compete there, and should not try. | The addressable customer for an Enterprise-tier AI platform typically runs annual contract values above $500,000. Hyperscalers are structured for that segment — their sales organisations, compliance certifications, and infrastructure commitments reflect it. PointSav is not equipped to compete there, and should not try. |
| The segment PointSav targets runs annual contract values of $5,000 to $50,000 for AI tooling. That gap — too small for an enterprise motion, too regulated for a consumer product — is structurally inaccessible to platforms built around hyperscale billing models. | The segment PointSav targets runs annual contract values of $5,000 to $50,000 for AI tooling. That gap — too small for an enterprise motion, too regulated for a consumer product — is structurally inaccessible to platforms built around hyperscale billing models. |
| The 2026 evidence is consistent: GPU pricing at neoclouds is structurally lower than at hyperscalers, a substantial fraction of hyperscaler customers report billing unpredictability, and hyperscaler capital expenditure commitments for AI infrastructure continue to rise. These trends do not help SMBs; they increase the pricing floor those customers face. | The 2026 evidence is consistent: GPU pricing at neoclouds is structurally lower than at hyperscalers, a substantial fraction of hyperscaler customers report billing unpredictability, and hyperscaler capital expenditure commitments for AI infrastructure continue to rise. These trends do not help SMBs; they increase the pricing floor those customers face. |
| ## The addressable SMB segment | ## The addressable SMB segment |
| Regulated SMBs share three properties that define the market: | Regulated SMBs share three properties that define the market: |
| 1. They are too small for an enterprise sales motion at hyperscaler minimums. | 1. They are too small for an enterprise sales motion at hyperscaler minimums. |
| 2. They are too regulated for consumer or unmanaged AI — HIPAA, PIPEDA, GDPR, FINRA, and equivalent Canadian provincial frameworks apply. | 2. They are too regulated for consumer or unmanaged AI — HIPAA, PIPEDA, GDPR, FINRA, and equivalent Canadian provincial frameworks apply. |
| 3. They require their data and their AI to remain on infrastructure they control. | 3. They require their data and their AI to remain on infrastructure they control. |
| Examples include small clinics subject to health privacy legislation, regional law firms with privilege and confidentiality obligations, mid-cap financial advisors with regulatory reporting requirements, and real-estate operators maintaining corporate document archives. These customers are not edge cases. They are the mainstream of the regulated economy below the enterprise threshold. | Examples include small clinics subject to health privacy legislation, regional law firms with privilege and confidentiality obligations, mid-cap financial advisors with regulatory reporting requirements, and real-estate operators maintaining corporate document archives. These customers are not edge cases. They are the mainstream of the regulated economy below the enterprise threshold. |
| ## Federation | ## Federation |
| Federation capabilities — the federated LoRA marketplace, the Mooncake KV pool, and base model updates as they are produced — are included in the SMB Customer license. There is no separate federation tier. Every paying customer may participate; privacy-preserving federated learning techniques are mature enough in 2026 to make this structurally sound. | Federation capabilities — the federated LoRA marketplace, the Mooncake KV pool, and base model updates as they are produced — are included in the SMB Customer license. There is no separate federation tier. Every paying customer may participate; privacy-preserving federated learning techniques are mature enough in 2026 to make this structurally sound. |
| ## Continued pretraining as a curatorial investment | ## Continued pretraining as a curatorial investment |
| PointSav's investment in continued pretraining of the base model — the production of PointSav-OLMo-N variants — is funded by SMB Customer license revenue and benefits every customer when an updated base is distributed in the next platform release. The economic structure resembles that of a Linux distribution: curation is funded by the subscription business; customers run their own installations; the improved base returns to the full subscriber base. | PointSav's investment in continued pretraining of the base model — the production of PointSav-OLMo-N variants — is funded by SMB Customer license revenue and benefits every customer when an updated base is distributed in the next platform release. The economic structure resembles that of a Linux distribution: curation is funded by the subscription business; customers run their own installations; the improved base returns to the full subscriber base. |
| ## The cost asymmetry | ## The cost asymmetry |
| The economics that protect this position are straightforward. LoRA fine-tuning of a seven-billion-parameter model costs in the range of $30 to $100 per adapter. Continued pretraining of that same model costs $30,000 to $100,000. Pretraining from scratch at that scale costs $500,000 to $2,000,000. | The economics that protect this position are straightforward. LoRA fine-tuning of a seven-billion-parameter model costs in the range of $30 to $100 per adapter. Continued pretraining of that same model costs $30,000 to $100,000. Pretraining from scratch at that scale costs $500,000 to $2,000,000. |
| SMBs can do LoRA fine-tuning on their own data. They cannot do continued pretraining. PointSav does continued pretraining. Federation pools the learning from per-customer LoRA adapters into a commons that improves the base for everyone. This asymmetry is structural: it does not depend on any particular competitive outcome. It depends on the economics of compute at scale, which are unlikely to invert. | SMBs can do LoRA fine-tuning on their own data. They cannot do continued pretraining. PointSav does continued pretraining. Federation pools the learning from per-customer LoRA adapters into a commons that improves the base for everyone. This asymmetry is structural: it does not depend on any particular competitive outcome. It depends on the economics of compute at scale, which are unlikely to invert. |
| ## Service availability by tier | ## Service availability by tier |
| Ring 1 services (boundary ingest: filesystem, people, email, and input) and Ring 2 services (knowledge and processing: content, extraction, search, and egress) are available in both tiers. Ring 3 services differ: Community includes optional local model inference; SMB Customer adds GPU burst, external API integration, multi-archive aggregation, federation, and access to updated base models. Support and integration services are community-forum only for Community and contracted for SMB Customer. | Ring 1 services (boundary ingest: filesystem, people, email, and input) and Ring 2 services (knowledge and processing: content, extraction, search, and egress) are available in both tiers. Ring 3 services differ: Community includes optional local model inference; SMB Customer adds GPU burst, external API integration, multi-archive aggregation, federation, and access to updated base models. Support and integration services are community-forum only for Community and contracted for SMB Customer. |
| ## See Also | ## See Also |
| - [[compounding-substrate]] — the five structural properties this economic model funds | - [[compounding-substrate]] — the five structural properties this economic model funds |
| - [[sovereign-ai-commons]] — the market positioning of the substrate as a commons | - [[sovereign-ai-commons]] — the market positioning of the substrate as a commons |
| - [[llm-substrate-decision]] — how the model choice maps to Community and SMB Customer capability tiers | - [[llm-substrate-decision]] — how the model choice maps to Community and SMB Customer capability tiers |
| - [[four-tier-slm-substrate]] — the four deployment tiers within the SMB Customer model | - [[four-tier-slm-substrate]] — the four deployment tiers within the SMB Customer model |
| ## References | ## References |
| 1. PointSav factory-release-engineering `LICENSE-MATRIX.md` — authoritative license assignment per tier. | 1. PointSav factory-release-engineering `LICENSE-MATRIX.md` — authoritative license assignment per tier. |
| 2. `conventions/sovereign-ai-commons.md` — structural market positioning. | 2. `conventions/sovereign-ai-commons.md` — structural market positioning. |
| 3. `conventions/economic-model.md` — source convention for this article. | 3. `conventions/economic-model.md` — source convention for this article. |
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| *Copyright © 2026 Woodfine Capital Projects Inc. Licensed under [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0/).* | *Copyright © 2026 Woodfine Capital Projects Inc. Licensed under [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0/).* |
| *Woodfine Capital Projects™, Woodfine Management Corp™, PointSav Digital Systems™, Totebox Orchestration™, and Totebox Archive™ are trademarks of Woodfine Capital Projects Inc., used in Canada, the United States, Latin America, and Europe. All other trademarks are the property of their respective owners.* | *Woodfine Capital Projects™, Woodfine Management Corp™, PointSav Digital Systems™, Totebox Orchestration™, and Totebox Archive™ are trademarks of Woodfine Capital Projects Inc., used in Canada, the United States, Latin America, and Europe. All other trademarks are the property of their respective owners.* |