What does Token Forge Cloud do?

Token Forge Cloud provides managed APIs for Qwen, DeepSeek, GLM, and Seedance, with additional video and speech models for teams building production AI products. Proven workloads can also move into a Neo Cloud marketplace product path.

Who is Token Forge Cloud built for?

It is built for product and enterprise teams that need reliable language, reasoning, coding, agent, and video model access without operating every provider stack themselves.

Can teams start with APIs before buying through Neo Cloud?

Yes. Teams can begin with managed API access for Qwen, DeepSeek, GLM, Seedance, and additional media models, then move predictable production traffic into a Neo Cloud marketplace product path.

Which AI workflows does Token Forge Cloud support?

Token Forge Cloud supports assistants, reasoning, coding, agents, text-to-video, image-to-video, asynchronous jobs, model routing, and speech generation.

How does Token Forge Cloud handle security and data control?

Token Forge Cloud supports role policies for who can call which models and workflows, controlled access for prompts and application data, and audit-ready request, generation, and routing telemetry.

ChatGPT Enterprise Cost: Pricing, API Rates & Budget Guide

For a buyer-specific ChatGPT Enterprise price, OpenAI directs prospective customers to contact sales and offers downloadable Enterprise materials that include pricing information. Separately, SpendHound describes Enterprise pricing as custom and negotiated, and says there is no standard price. OpenAI also publishes token-based usage rates for eligible Enterprise agreements, but those rates do not establish your seat fees or total contract cost. Request an organization-specific quote and estimate production API use separately unless your agreement says otherwise. (ChatGPT Enterprise; Enterprise materials; SpendHound guide)

What does ChatGPT Enterprise cost?

SpendHound’s third-party guide says ChatGPT Enterprise pricing is custom and negotiated directly with OpenAI; it gives no standard Enterprise price or individual quote. OpenAI’s Enterprise page directs buyers to sales, while its downloadable materials describe how pricing works. Neither a third-party description nor a token rate card is a substitute for your organization’s quote.

OpenAI’s rate card publishes usage rates for eligible token-based Enterprise agreements. It says the agreement determines applicable rates, discounts, and commercial terms; for workspaces moving from credits, those rates apply only after the billing model changes. The rate card states that, unless another unit is specified, its USD rates are per 1 million input, cached-input, and output tokens.

For example, the rate card lists GPT-6 (GPT-6 Sol) at $2.00 per million input tokens, $0.20 per million cached-input tokens, and $10.00 per million output tokens. These are usage rates for eligible agreements—not a seat-price quote or a prediction of your total bill. Confirm that the rate card applies to your agreement and ask OpenAI to specify any applicable discounts and terms. (Enterprise rate card)

Budget production API usage separately

OpenAI’s GPT-6.1 Sol model page lists $2.00 per million input tokens, $0.10 per million cached-input tokens, and $10.00 per million output tokens. It also states that API prompts with more than 272,000 input tokens are priced at 2× the input and cache rates and 1.5× the output rate for the full request. Confirm the current model, applicable processing option, and rates before using them in a forecast; the cited pricing information does not establish which processing tier applies. (GPT-6.1 Sol model details; API pricing)

A basic estimate uses mutually exclusive token categories:

(uncached input tokens ÷ 1 million × applicable input rate) + (cached-input tokens ÷ 1 million × applicable cached-input rate) + (output tokens ÷ 1 million × applicable output rate)

If your reported input-token total already includes cached tokens, calculate uncached input tokens as total input tokens minus cached-input tokens. Ask engineering to estimate monthly uncached-input, cached-input, and output tokens separately by model and category. Account for any relevant tools or storage, and distinguish application/API use from employees’ ChatGPT use. ChatGPT and the API platform have separate billing systems; check your billing setup and written agreement rather than assuming API use is included in Enterprise seats. (OpenAI billing guidance)

How to interpret the available budget benchmark

A SpendHound guide updated August 5, 2026 reports average annual OpenAI spend of $318,506 for organizations it labels Enterprise (1,000+ employees) and $44,318 for its SMB segment (50–1,000 employees). The guide attributes the figures to SpendHound’s dataset as of July 2026. These are third-party observed averages of OpenAI spend—not OpenAI prices, Enterprise-only license fees, or a forecast for your organization. The stated employee bands overlap at exactly 1,000 employees, so they are not mutually exclusive at that boundary. (SpendHound guide)

SpendHound identifies organization size, expected adoption and usage, API use and model selection, deployment requirements, security and compliance needs, contract length, commitments, and negotiated terms as possible cost drivers. Its guide gives no pricing formula or factor weights. Use these factors to frame questions, not to calculate an Enterprise quote from an average. (SpendHound guide)

Confirm requirements before requesting a quote

Separate your requirements into two groups:

Employee workspace: expected users and seat types; identity and access needs; required security, compliance, logging, retention, and network controls.
Production applications: API models and processing options, expected token volumes, relevant tools or storage, and workload limits.

OpenAI’s Enterprise admin guidance advises workspace owners to confirm the security, compliance, logging, retention, and network controls their organization requires. Ask OpenAI to confirm which required controls and limits are included in the proposed workspace and agreement; do not infer a price from a feature list. (Enterprise admin quickstart)

How to get a usable budget estimate

List expected employee seats and needs. Identify users, seat types, and required capabilities. Ask OpenAI to specify seat fees and any minimums in the quote.
Confirm the Enterprise billing model. Ask whether the agreement is credit-based or token-based, which rates and discounts apply, and what usage limits or controls are in scope.
Build an API forecast. Have engineering estimate monthly uncached-input tokens, cached-input tokens, and output tokens by model and processing option. If the input estimate includes cached tokens, subtract them to get uncached input. Include any applicable tools or storage, and confirm the applicable pricing tier.
Ask how variable usage is handled. Request the applicable commitments, thresholds, usage controls, and charges or actions when limits are reached. Check the details against the agreement.
Review the written terms. Check what the quote includes, how API usage is handled, and the applicable data, renewal, and overage terms. SpendHound recommends reviewing these areas as procurement practice; its guide does not establish that OpenAI will accept a requested change or discount. (SpendHound guide)

Bottom line

Do not use a third-party average or a published token rate as your Enterprise total. OpenAI publishes usage rates for eligible token-based Enterprise agreements, but the applicable commercial terms depend on your agreement. Document employee and production requirements, request an organization-specific quote, estimate API usage from your workload, and confirm whether the quote combines those costs.

Sources

ChatGPT Enterprise

ChatGPT Enterprise materials

ChatGPT Rate Card (Enterprise token-based pricing)

Pricing | OpenAI API

GPT-6.1 Sol Model

Managing billing for ChatGPT and the API platform

ChatGPT Enterprise admin quickstart

ChatGPT Pricing and Negotiation Guide (2026)