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Enterprise AI is usually sold as a mix of seat access, credits or token consumption, annual commitments, and negotiated support and service terms. The final bill depends as much on usage controls, minimum spend, data handling, integration work, and service-level commitments as on a per-seat figure. A sound purchase process therefore does two things in parallel: it models fixed and variable cost, and it verifies the exact security and compliance scope of the product your organization will use.
What counts as an enterprise AI service?
An enterprise AI service is a hosted application, API, or managed platform used across an organization rather than by a small personal team. It may provide conversational assistants, document analysis, coding help, retrieval over internal data, workflow automation, or model access through an API. Enterprise packaging typically adds centralized administration, identity integration, auditability, contractual privacy terms, support, and service-level commitments.
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The label “enterprise” does not guarantee a particular architecture or compliance level. One provider may offer a hosted application with a fixed number of user seats; another may charge for model calls through an API; a third may combine both. Treat the service, region, model family, and contract as the unit you are evaluating.
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There is no universal enterprise price. Expect a combination of fixed access charges and variable consumption, followed by negotiated commercial terms. Ask the provider to quote the complete cost model, including implementation and support, rather than accepting a single seat or token number.
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Common charging methods
- Seat or subscription fees: a recurring charge for named or provisioned users and administrative features.
- Token or usage metering: charges based on input and output tokens, requests, compute time, storage, or other measured consumption.
- Credits: prepaid or committed units that are drawn down as models and features are used.
- Seat plus usage: a platform fee provides access while every model call is billed separately.
- Negotiated commitments: annual terms, minimum spend, volume discounts, support tiers, and service-level credits set in the contract.
Published examples and their limits
| Offering or model | Fixed charge | Variable charge | Qualification |
|---|---|---|---|
| OpenAI Business standard | $20 per seat per month when billed annually; $25 per seat per month when billed monthly | Separate usage may apply depending on the product and agreement | Public Business pricing is not an Enterprise quote. |
| OpenAI Business premium | $100 per seat per month when billed annually; $125 per seat per month when billed monthly | Separate usage may apply depending on the product and agreement | Public Business pricing is not an Enterprise quote. |
| OpenAI Enterprise | Custom pricing | Credit-based or token-based options; token charges are separate from seat fees and based on actual consumption | Terms must be confirmed with sales and in the order form. |
| Anthropic Enterprise | Seat fee; amount not stated publicly in the cited material | Every token used in Claude, Claude Code, or Cowork is billed separately at standard API rates | There is no included token allowance in the described model. |
These figures illustrate structures, not a benchmark for your project. A public Business rate cannot be used to infer an Enterprise quote. Conversely, a low token rate can still produce a high total when prompts are long, retrieval returns many documents, agents retry calls, or workflows run continuously.
Terms to negotiate before signing
- Annual commitment, minimum spend, and the date on which the commitment starts.
- Volume discounts and whether discounts apply equally to every model and region.
- Overage rates, hard spend caps, alerts, and who can raise a limit.
- Prepaid-credit expiry, refunds, transferability, and treatment of unused balances.
- How and when model rates, capabilities, or token definitions may change.
- Support response targets, escalation paths, uptime commitments, and service-level credits.
- Implementation, connector, migration, and premium-support fees that sit outside the headline subscription.
How should you model the total cost?
Build a usage model before comparing quotes. Separate costs that remain stable from those that scale with activity, then test realistic and worst-case scenarios.
Fixed-cost worksheet
- Number and type of seats, including administrators, developers, reviewers, and occasional users.
- Base platform or workspace fee.
- Support tier and any dedicated account or solution-architect service.
- Private networking, customer-managed keys, regional hosting, or other architecture add-ons.
- Implementation, data preparation, connector development, and security-assessment work.
Variable-cost worksheet
- Expected requests per user or workflow per day.
- Average input and output tokens, including system prompts and retrieved context.
- Model mix: routine requests may use a smaller model while complex tasks use a more expensive one.
- Retries, parallel agent calls, batch jobs, embeddings, indexing, storage, and file processing.
- Seasonal peaks and automated workloads that can run outside office hours.
Ask for a sample invoice showing each meter. Require dashboards that attribute spend by user, team, application, model, and environment. Set alerts below the contractual cap and test what happens when the cap is reached: blocking, throttling, fallback models, or an unapproved overage are materially different outcomes.
What belongs in an enterprise AI security review?
A security review is an evidence exercise, not a badge-collection exercise. Request current documents, verify their scope, and record the contractual promise that applies to your chosen product and region.
Assurance reports and certification scope
- Obtain the current SOC 2 report and a bridge letter if the examination period has ended. Check which Trust Services Criteria were tested and which services, environments, and locations are in scope.
- Request ISO/IEC 27001 certificates and scope statements. Where relevant, check 27017 for cloud controls, 27018 for protection of personal data in public clouds, 27701 for privacy information management, and 42001 for AI management systems.
- Confirm that the certificate covers the exact API, hosted application, support systems, and region you will purchase. A parent-company certificate or a badge on a marketing page is not proof that your workload is covered.
- Ask for product-level compliance mappings and the date of the latest penetration test or independent security assessment.
OpenAI states that its API and ChatGPT business products underwent an independent SOC 2 Type 2 examination and maintain ISO/IEC 27001:2022 and ISO/IEC 27701:2019 certifications. Its Trust Portal also lists ISO/IEC 27017:2015, ISO/IEC 27018:2019, ISO/IEC 42001:2023, PCI DSS, GDPR, and other materials. Those statements are useful starting points; procurement must still validate the scope and the contract for the selected service.
Data use, training, and monitoring
Ask explicitly whether prompts, files, outputs, feedback, and telemetry are used to train or improve models by default. Document any opt-in path, the administrator who can enable it, and whether the setting applies to every endpoint and model. Clarify how abuse monitoring, automated classifiers, human review, and support access process business data.
“We don’t train our models on your organization’s data by default.” — OpenAI, Security and Privacy page.
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Do not treat a default as a permanent prohibition. Put the chosen setting, permitted uses, retention period, and deletion obligations in the data-processing agreement or order form.
Encryption and key control
Record encryption in transit and at rest, key ownership, rotation, backup protection, and tenant separation. Ask whether customer-managed keys are available, which services they cover, and what failure mode occurs if a key is disabled.
“OpenAI encrypts all data at rest (AES-256) and in transit between our customers and us and between us and our service providers (TLS 1.2+).” — OpenAI, Enterprise Privacy page.
Identity and administrative access
- SAML 2.0 single sign-on, multifactor authentication, and verified-domain controls.
- SCIM 2.0 provisioning and deprovisioning, with joiner, mover, and leaver automation.
- Role-based access control, least privilege, separate administrator roles, and service-account restrictions.
- Exportable audit logs covering sign-ins, configuration changes, data access, model use, and administrative actions.
- Controls for API keys, secrets, session duration, device access, and emergency revocation.
Retention, deletion, and incident response
Get written defaults and configurable settings for prompts, outputs, files, caches, logs, backups, and abuse-monitoring records. Ask how legal holds interact with deletion, how backup copies are removed, and what evidence you receive when deletion is complete. Review the subprocessor list, hosting regions, breach-notification deadline, incident-response process, and customer communication channel.
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Regulated workloads
Verify HIPAA and a business associate agreement, GDPR and a data-processing agreement, CCPA obligations, FERPA, PCI DSS, FedRAMP, or sector-specific controls only when the provider’s published scope covers the exact service. A provider may support one regulated product while excluding another API, model, region, or support channel.
Which integration requirements should go into an RFP?
Write integration requirements before selecting a vendor. This prevents a polished demonstration from masking manual provisioning, unsupported data paths, or an expensive exit.
Identity and lifecycle
- SAML SSO, SCIM provisioning, MFA, RBAC, group mapping, domain verification, and automated joiner/mover/leaver workflows.
- Separate permissions for builders, reviewers, operators, auditors, and billing administrators.
- Support for multiple environments and a documented process to revoke credentials immediately.
Data and application connectivity
- Supported connectors and APIs for CRM, ERP, document stores, data warehouses, HR systems, IoT or SCADA sources, and internal applications.
- API and SDK languages, batch and streaming ingestion, metadata and lineage handling, and vector or search integration.
- Data classification, field-level filtering, regional processing, and controls that prevent restricted records from entering a prompt.
Enterprise AI data integration may span ERP, CRM, SCADA, HR, MRP, IoT, and external data. C3.ai’s FY2025 filing describes these integration needs alongside authentication, authorization, encryption, privacy, GDPR, NERC-CIP, and SOC 2 requirements. Use that breadth as a reminder to inventory every source and destination, not as evidence that a particular connector is included in a quote.
Operations, reliability, and cost controls
- Published rate limits, quotas, concurrency limits, batch limits, and a process for requesting increases.
- Usage analytics, cost dashboards, per-team budgets, spend caps, alerts, and exportable billing data.
- Audit events, model-version pinning, deprecation notices, rollback, evaluation records, and incident escalation.
- Service-level targets for availability, latency, support response, and credits when targets are missed.
Network and security architecture
- Private networking or approved egress paths, firewall requirements, regional endpoints, and proxy compatibility.
- Secrets management, customer-managed keys, tenant isolation, vulnerability disclosure, and independent testing.
- Clear boundaries between your environment, the provider’s hosted application, subprocessors, and support tooling.
Governance and human oversight
- Human approval for high-impact actions such as financial changes, employment decisions, healthcare outputs, or production deployments.
- Prompt and model change control, evaluation evidence, red-team procedures, content filtering, and records suitable for regulatory audits.
- Ownership of prompts, outputs, evaluation sets, indexes, and configurations when teams or vendors change.
Exit and portability
- Export of prompts, outputs, evaluations, indexes, logs, permissions, and configuration in documented formats.
- Migration assistance, replacement of proprietary connectors, and a timetable for transition.
- Deletion certification covering live systems, caches, backups, and subprocessors after termination.
How do you run the procurement process?
- Inventory workloads: list users, data classes, regions, systems, expected volume, latency needs, and high-impact decisions.
- Define acceptance tests: specify accuracy, retrieval quality, latency, availability, audit-log fields, and failure behavior before demonstrations.
- Issue a structured RFP: require responses to pricing meters, security evidence, integration interfaces, residency, support, and exit terms.
- Validate evidence: inspect reports and certificates, confirm product and regional scope, and involve legal, privacy, security, identity, and finance reviewers.
- Pilot with controls: use representative but properly classified data, enforce spend limits, test SSO and SCIM, exercise logging, and simulate an incident.
- Contract the decisions: place data-use settings, retention, notification windows, rate limits, service levels, credits, and deletion obligations in binding documents.
- Operate continuously: review usage, model changes, subprocessors, certificates, vulnerabilities, and access rights at a defined cadence.
Common mistakes and how to correct them
Choosing on seat price alone
A cheap seat can hide token overages, retrieval volume, implementation work, or a costly support tier. Correct the comparison with a three-scenario model: expected, peak, and automated-workflow usage.
Accepting a compliance logo without checking scope
Ask for the report, certificate, service description, region, and audit dates. If the exact API or hosted application is absent, treat the control as unverified for that workload.
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Leaving identity and deletion until after purchase
Require SSO, SCIM, RBAC, audit export, retention, and deletion tests in the pilot. Retrofitting them can force architecture changes or manual administration.
Ignoring retries and autonomous workflows
Agents can multiply calls without a user clicking each time. Set per-workflow budgets, retry limits, model fallbacks, and alerts, then test the behavior at the cap.
Assuming integration means data governance
A connector that can read a system does not establish field filtering, classification, residency, or authorization. Map each data flow and require an owner for every permission boundary.
What should the final decision record contain?
Keep a concise record that names the selected product and region, pricing meters and committed amounts, approved data uses, retention and deletion settings, encryption and key design, identity controls, connected systems, regulatory scope, support and SLA terms, and the exit plan. Include the evidence date for every assurance report and the person responsible for rechecking it. This record turns a one-time purchase into an accountable operating arrangement.
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