Pricing rates and the ≈$35,520 annual forecast are planning inputs for modeling; on-demand inference is consumption-billed and variable, so the total is an envelope, not a contract. Confirm OpenAI model availability and per-region pricing in the live AWS console for ca-central-1 before procurement.
1. Summary
AWS Bedrock now hosts OpenAI's GPT-5.6 models alongside Anthropic's Claude. This means the identical private architecture described in the Technical Implementation Blueprint — ca-central-1 pinning, AWS PrivateLink, customer-managed AWS KMS encryption, the AWS Organizations SCP region lock, and per-user IAM Identity Center sign-in — can back an OpenAI-model deployment with no change to the security perimeter. Data stays inside our AWS account in Canada, never reaches OpenAI's cloud, and is not used to train OpenAI's models.
The differences are concentrated in three areas: the desktop client / user experience, the application and governance ecosystem, and token economics. On pure model-consumption pricing, OpenAI is modeled at ≈ $35,520/year for 100 users — roughly 18% lower than the ≈ $43,320 Claude forecast. That saving must be weighed against the absence of a native, centrally-managed OpenAI desktop application for this deployment.
2. Side-by-side comparison
| Dimension | Claude 3.5 Sonnet on AWS Bedrock | OpenAI GPT-5.6 on AWS Bedrock |
|---|---|---|
| Security perimeter | ca-central-1, PrivateLink, KMS, SCP, Identity Center | Identical — same account and controls |
| Data residency / vendor isolation | Data never reaches Anthropic; stays in Canada | Data never reaches OpenAI; stays in Canada |
| Desktop UI / client | Native Claude desktop app in third-party mode; first-class managed configuration, MDM export, feature lockdown | No native OpenAI desktop app for this deployment; consumed via a client or internal gateway that speaks the Bedrock OpenAI API — desktop UX must be sourced or built separately |
| App / agent ecosystem | Deep native surface (chat, agentic workflows, code, MCP, plugins/skills) governable through the same managed keys | Model-only on Bedrock; the desktop/agentic ecosystem and its governance are the organization's responsibility to assemble |
| Centralized lockdown (MDM) | Built-in: registry/Managed-Preferences policy, read-only managed config, per-feature disable keys | Must be implemented around whatever client is chosen |
| Input token rate | $3.00 / 1M | $2.50 / 1M |
| Output token rate | $15.00 / 1M | $10.00 / 1M |
| Blended rate @ ~5:1 read:generate | ≈ $5.00 / 1M | ≈ $3.75 / 1M |
| Fixed platform overhead (100 users) | ≈ $1,320 / yr | ≈ $1,320 / yr (same stack) |
| 12-month forecast, 100 users | ≈ $43,320 | ≈ $35,520 |
3. How this architecture handles the OpenAI-specific gaps
Desktop user interface. The Claude deployment's biggest hidden asset is the native desktop application: it ships the managed-configuration channel, MDM export (.mobileconfig / registry .reg), read-only lockdown, per-feature disable keys, and audit integration described in the technical blueprint — out of the box. OpenAI on Bedrock is delivered as a model endpoint, not a managed desktop product. To match the Claude user experience and governance, the organization would need to either (a) adopt a third-party desktop client that supports the Bedrock OpenAI API and layer its own MDM controls around it, or (b) build an internal gateway/desktop front-end. Either path adds engineering effort, a governance-tooling gap to close, and schedule risk that does not appear in the token line.
App-ecosystem variations. With Claude, plugins, skills, agentic workflows, and MCP connectors are governed centrally through the same managed keys that lock down the client. With OpenAI-on-Bedrock, there is no equivalent managed client, so any comparable ecosystem — and the controls over it — must be assembled and governed by the organization. This increases both capability-build effort and the audit surface the security team must own.
Token cost dynamics. OpenAI's lower per-token rates ($2.50/$10.00 vs $3.00/$15.00) drive the ≈18% lower model-consumption forecast. Two caveats temper it: (1) prompt-caching efficiency differs between providers, and the effective input volume actually billed depends on how well each caches the stable prompt prefix — so the realized delta may be smaller or larger than list-price arithmetic suggests; and (2) the token saving is a partial, not total, cost picture — it excludes the client and governance build-out above. The forecasts here should be read as vendor-reference envelopes, validated by a scored pilot on real workloads.
4. Recommendation
Keep OpenAI-on-Bedrock as a credible, same-perimeter alternative, not a default. The ≈18% inference saving (≈$7,800/year at 100 users) is real and the residency posture is identical, but it comes without the native, centrally-managed desktop client that makes the Claude deployment fast to lock down and govern. Decide between them with a scored pilot that measures model fit for the target workflows, the true realized token cost after caching, and the engineering cost of reaching an equivalent locked-down desktop experience with OpenAI — rather than on list price alone.