XAI Enterprise Plans: Stand Up an Enterprise-Grade AI API Gateway from ¥5,000/Month — AI Coding, High-Concurrency APIs, and Image Workflows

Enterprises do not need another pile of shared accounts, manually distributed keys, and fragile tool configs. XAI delivers an enterprise-grade AI API gateway: a dedicated system, unified access to major global and China models, primary/sub-account governance, internal API resource distribution, and a matching XAI image app — serving both internal AI coding and high-concurrency production traffic.

When an enterprise introduces AI, the hard part is usually not "can we buy an account?" The hard part is what happens afterward: who manages it, who can use it, how much each team consumes, how to add more people, and how to keep the whole thing under control.

Engineering wants Codex and Claude Code. Production systems want one AI API that can absorb real concurrency. Marketing and design want image generation and editing. Meanwhile administrators want to know who is using what, how much, whether headcount can grow, and whether quotas can be enforced. A handful of scattered accounts plus keys pasted into a group chat becomes its own management cost very quickly.

So we packaged XAI enterprise service into a clear plan lineup:

In short: what an enterprise buys is not a few accounts, but an AI API gateway it can manage itself, keep expanding, and build on for the long term.

If you are an individual or a small team who just wants to start using it, skip the enterprise process entirely: register online at m.xairouter.com and buy as you go — pay only for what you use, no deployment and no sales conversation.

The full plan lineup and a side-by-side comparison live on the Enterprise page.

Three tiers, clear boundaries

Enterprise delivery has three tiers: Base and Fully Managed differ on who prepares the machines and runs the account pools, while Enterprise Custom is scoped to scale and compliance needs.

Base is about "system delivery + resource delivery + continuous upgrades." You prepare the machines; we stand up a dedicated XAI environment, connect 1 ChatGPT 20x Pro account supplied monthly, deliver the standalone XAI image app, and hand administrator rights to you. From there we keep shipping regular version updates and upgrades.

After delivery, your team can:

• Create sub-accounts for engineering, product, operations, and data teams
• Assign different model permissions and quotas per team
• Point Codex CLI / App, Claude Code, Gemini CLI, and OpenAI-compatible tools at XAI
• Freely add any third-party AI provider and bring major global and China models under one entry point
• Cover internal design, editing, and content production through the XAI image app
• Review per-member usage and resource consumption
• Keep adding accounts as the team grows

If you would rather not deal with machines or day-to-day account-pool operations, choose Fully Managed at ¥10,000/month. We prepare the machines and related resources, set up Claude, Codex, and Grok pools in one go, and take on the daily updates, checks, management, and upgrades of every pool. You only tell us who needs access, what quotas to grant, and which tools and models to onboard.

Why buying a few accounts does not scale

Buying individual accounts works for personal trials. It does not work for an organization over time.

Once you are operating at company scale, the problems arrive fast:

• Employees hold their own accounts and keys, making offboarding painful
• Each team runs a different config, so failures are hard to diagnose
• Leadership sees a total bill but no per-department or per-person breakdown
• Dev tools, internal scripts, production systems, and image tools have no shared entry point
• Production-side high concurrency has no load balancing, failover, or rate governance
• Model resources you already bought cannot be distributed centrally
• Adding headcount means reorganizing accounts, permissions, and configs all over again

XAI consolidates all of this.

Instead of organizing usage around individual accounts, the company organizes AI capability around its own AI API gateway: the primary account holds the resource pool, sub-accounts distribute to teams and members, and Codex, major model APIs, and production requests all flow through one entry point for routing, metering, and governance. Image generation and editing are handled by the companion XAI image app.

This is the point we keep making: enterprises do not need account sharing, they need capability distribution.

A dedicated system per company

All three enterprise tiers support dedicated delivery: every customer gets a dedicated XAI deployment plus a standalone XAI image app.

That means:

• Your company has its own XAI management entry point
• Your members, sub-accounts, usage stats, and config policies exist independently
• Your account resources, third-party model resources, and later additions are governed separately
• Your image app is deployed on its own and serves internal generation/editing only
• You allocate permissions and quotas according to your own org structure
• If you later need deeper isolation and customization, move to Enterprise Custom for a continuous upgrade path

For leadership, this solves control. For technical leads, it solves rollout. For individual employees, it solves usability — nobody has to ask "which account today," "where's the key," or "how do I configure this tool." The company simply hands out XAI access.

What ¥5,000/month includes

The ¥5,000/month Base tier suits companies with basic technical management capacity and available machine resources.

We stand up the system, connect the resources, hand over a clean entry point, and keep shipping updates. You manage members and day-to-day pool operations.

Base delivery includes:

• A dedicated XAI system — deployed per company, with clear config and data boundaries
• A standalone XAI image app — paired with XAI for internal generation and editing
• 1 ChatGPT 20x Pro account supplied per month — initial Codex capacity, expandable with your headcount
• AI coding access — engineering uses Codex, Claude Code, Gemini CLI and more through one entry point
• Free onboarding of major models — add any third-party provider and bring Codex, Claude, Gemini, DeepSeek, GLM, Grok, K3, Qwen, plus Volcengine Ark, Alibaba Cloud, Baidu Qianfan, Tencent Cloud and others under unified management
• Primary/sub-account hierarchy — administrators keep creating team and member accounts
• Model and quota governance — control which models a member can use, daily limits, and which resources are reserved for specific teams
• A unified API entry point — internal tools, scripts, agents, and production systems onboard progressively; AI API is allocated like a cloud resource
• High-concurrency capability — load balancing, failover, model mapping, and rate governance for production-scale traffic
• Usage statistics — visibility by member, team, and model
• Deployment plus regular updates and upgrades — we deploy and keep the system current

Your side only needs to prepare machine resources. This tier fits companies with their own IT or engineering lead, available servers, and a preference for owning the system — adding or removing people and allocating quotas happens internally.

Not just AI coding — an enterprise AI API foundation

Codex is the easiest entry point to roll out and the fastest place to see efficiency gains, but XAI is not limited to AI coding.

An AI API gateway serves two very different workloads at once:

Internal AI coding. Engineering uses Codex, Claude Code, Gemini CLI, OpenClaw, OpenCode. Long context, interactive, allocated per person.

Production-scale, high-concurrency API traffic. Support systems, content production, data cleaning, batch jobs, agent workflows. High volume, high concurrency, sensitive to stability and cost.

Both workloads run through the same governance layer in XAI, with completely different policies:

• Engineering uses high-quality models; batch jobs fall back to cheaper ones
• Different teams and projects get different quotas and rate limits
• Multiple upstreams load-balance and fail over, so one upstream wobble does not hit production
• Model mapping lets you switch upstreams without touching business code
• Every call is attributable to a person, team, project, and model

The result: internal AI API is no longer a set of keys scattered across laptops and scripts, but a resource pool that can be allocated, audited, expanded, and pushed hard.

The XAI image app covers generation and editing

Beyond text, code, and API calls, companies have a steady stream of image needs: product visuals, marketing assets, event posters, social content, knowledge-base illustrations, restoration, and local edits.

So all three enterprise tiers can include a standalone XAI image app.

Its scope is deliberately narrow: an independent image service that does not depend on XAI's user system. Use it as an internal image workbench:

• Two core entry points — generate and edit
• Upload source images and masks for editing
• Requests hit upstream from the server side, so employees never touch upstream image keys
• Credit-based accounting and allocation, so image spend stays controlled
• Generation and editing run in the background; switching pages does not interrupt a task
• Failed requests refund pre-deducted credits, and admins maintain plans, credit quotas, and user resources

That way you are not only answering "how does engineering use AI," but also "how do content, design, and operations use AI imagery."

When Fully Managed at ¥10,000/month is the right call

If you would rather not prepare machines, and would rather not spend time on pool management, account onboarding, model mapping, and day-to-day troubleshooting, pick Fully Managed.

Fully Managed adds ¥5,000/month on top of Base, for ¥10,000/month total.

It covers:

• Machines and related resources prepared by us — no servers needed on your side
• Initial deployment plus ongoing version maintenance and upgrades
• Daily updates, checks, and management of every account pool
• Claude, Codex, and Grok pools set up in one go
• Upstream provider onboarding, model mapping, and API resource allocation
• Image app deployment, upstream image model onboarding, and credit policy configuration
• Help creating member and team sub-accounts
• Quota policy, rate limits, and team boundary configuration
• Day-to-day issue diagnosis
• Usage anomaly and consumption monitoring
• Ongoing structural optimization based on your feedback

Two kinds of customers pick this tier.

The first has leadership committed to rolling out AI but does not want internal teams bogged down in server procurement and configuration detail.

The second has an engineering team but wants XAI as purchased external infrastructure — we maintain the underlying capability, they focus on outcomes and output.

What this gives the enterprise

1. Faster AI coding rollout

No need to research accounts, interfaces, client configuration, and distribution from scratch. We deliver a running XAI system with Codex, Claude Code, and Gemini CLI capability behind one enterprise entry point.

Once engineering has its assigned access, it can start inside existing workflows.

2. A real foundation for high-concurrency production traffic

Production systems stop talking directly to a single upstream. All calls go through XAI, where load balancing, failover, model mapping, and rate limiting happen at the gateway. Business code stays stable.

As volume grows, scaling means adding upstreams, adding accounts, and tuning policy — not rewriting the integration layer.

3. API resources actually reach employees and systems

XAI supports major global and China models. Beyond enabling Codex for engineering, you can formally distribute internal AI API resources to employees, projects, and production systems.

Different upstreams, models, and quotas all consolidate into one governance layer, instead of keys spread across individuals, scripts, and departmental tools.

4. Image work has its own system

The bundled XAI image app serves as an internal image workbench for marketing, operations, product, design, and content teams.

Text, code, and API go through XAI; image generation and editing go through the image app. Two independent deployments with clear boundaries, procured and delivered as one enterprise package.

5. Administrators can actually administer

Instead of a pile of scattered accounts, administrators work through the XAI primary account:

• Who has access
• What they can use
• Daily quota
• Which teams consume the most
• Whether to keep adding accounts

All of it lives in one system.

6. A clearer cost structure

Base and Fully Managed are priced monthly; Enterprise Custom is scoped to requirements.

Start at ¥5,000/month — if you have machines and can manage internally, keep delivery lightweight. If you would rather hand over machines and pools, move up to ¥10,000/month. Account expansion is confirmed against real headcount and usage intensity.

That is far easier to budget than piecemeal purchasing, ad-hoc account top-ups, and manually coordinated permissions.

7. Room to keep expanding

As usage deepens, you can bring more global and China model resources, OpenAI-compatible APIs, Claude-compatible entry points, internal agents, and production system calls into the same XAI. On the image side, upstreams, credit rules, and usage boundaries can keep adapting.

If scale keeps growing or compliance needs deepen, choose Enterprise Custom for dedicated clusters, isolated networks, localized environments, and capacity planning.

Solve AI coding, production API traffic, and image production today; solve unified governance, cost attribution, and larger capacity planning next.

A recommended rollout

For most companies we suggest starting like this:

1. Have core members register online at m.xairouter.com and buy as they go, so real calls validate model quality and usage metering
2. Confirm the core teams and use cases — engineering, an AI project group, or a specific production system
3. Confirm whether you can provide machines, and pick Base, Fully Managed, or Enterprise Custom accordingly
4. We deploy XAI and the image app independently, with the initial 1 ChatGPT 20x Pro account supplied monthly
5. Administrators receive the primary account and create team and member sub-accounts
6. Core members validate AI coding, production API calls, and generation/editing flows
7. Based on real usage, decide whether to add accounts, onboard more models, expand image resources, or move to Fully Managed or Enterprise Custom

The advantage of this path: start online, then let real usage intensity decide whether an enterprise plan is warranted.

You do not need a complex AI strategy on day one, and you do not need a heavy custom delivery project up front. Deliver the system, get core teams stable AI coding, unified API, and image production, then layer governance and expansion on top.

Who this fits

If any of these describe you, the package is worth evaluating:

• You want Codex officially enabled for engineering instead of everyone improvising
• Several people already use AI coding, but accounts and configs are a mess
• Production systems need high-volume, high-concurrency AI API calls with cost and stability under control
• You want Codex, Claude, Gemini, DeepSeek, GLM, Grok, K3, Qwen and other major models behind one enterprise entry point
• You want AI capability distributed by department, team, and project
• You want internal AI API resources issued to employees and production systems while keeping quotas, permissions, and metering
• You want an internal generation/editing entry point for operations, design, product, and marketing
• You want your own enterprise management entry point, not just a few accounts
• You want cost control, usage visibility, and a plan for expansion
• You want a lightweight start now, with a path to Enterprise Custom or fuller AI infrastructure later

We especially recommend that engineering teams above 10 people manage AI coding systematically. The larger the team, the more obvious the cost of scattered accounts.

FAQ

Is ¥5,000/month just buying accounts?

No. ¥5,000/month delivers a dedicated XAI system, a standalone XAI image app, 1 ChatGPT 20x Pro account supplied per month, deployment, regular updates and upgrades, and enterprise AI API distribution and governance. Accounts are one part of resource supply; the real value is your own gateway and distribution model.

What actually separates Base from Fully Managed?

Two things: who prepares the machines, and who runs the account pools day to day. On Base you provide machines and manage pools yourself while we handle deployment and regular updates. On Fully Managed we prepare machines and related resources and take on daily updates, management, and upgrades of every pool.

What machines does Base need?

It depends on headcount and concurrency. Tell us expected user count, production call volume, and your existing servers, and we will come back with a concrete resource recommendation.

Which pools come with Fully Managed?

Fully Managed sets up Claude, Codex, and Grok pools in one go. Additional accounts, pool capacity, or model access are confirmed during the commercial conversation against headcount, concurrency, and real usage.

Can we add members and models ourselves?

Yes. Base is designed for self-management. Once the system and initial resources are delivered, your administrators create sub-accounts, allocate quotas, adjust member access, and freely add any third-party AI provider's model service.

What if we need more accounts?

You can keep adding them. The expansion path is confirmed against headcount, concurrency, and actual usage.

How much concurrency can the production side handle?

XAI is designed for high concurrency and low latency; real capacity depends on the size of your upstream resource pool and machine configuration. At larger volumes we run dedicated capacity planning, and where necessary recommend Enterprise Custom for dedicated deployment.

Can we onboard more models or production systems later?

Yes. XAI Router is built for multi-model, multi-account, multi-team, multi-entry governance. Start with Codex, then progressively onboard the unified AI API, internal systems, and more model resources.

Is the image app part of XAI?

No. The XAI image app is a separately delivered, standalone image service for internal generation and editing. The two are deployed independently: XAI handles LLMs, AI coding, enterprise APIs, and account governance; the image app handles generation, editing, credit deduction, and task management.

Where should we start?

Register online at m.xairouter.com and buy as you go, so real calls show you model quality, API compatibility, and usage metering — this step is entirely self-serve and needs no contact with us. Once you know it fits the team, read the full plan lineup and side-by-side comparison on the Enterprise page and pick a tier.

Closing

What an enterprise actually needs is not "a few more AI accounts." It is turning AI coding, production API traffic, and image production into an AI API gateway that can be delivered, distributed, managed, and expanded.

XAI enterprise plans keep that simple:

• Individual or small team just starting → register online at m.xairouter.com and buy as you go
• Have machines and can self-manage → ¥5,000/month Enterprise Base
• Want it handled, machines and pools included → ¥10,000/month Fully Managed
• Need more capacity → keep adding accounts
• Need more models → freely add global and China providers
• Need imagery → use the bundled XAI image app
• Need dedicated clusters, isolation, or strict compliance → choose Enterprise Custom

If you want your team on Codex, major model APIs, and internal generation/editing quickly, stably, and under control, this is a much clearer starting point than scattered accounts.

To start right now, register online and buy as you go: m.xairouter.com. Full plan lineup on the Enterprise page.

Further Reading

• Enterprise: full plan lineup and side-by-side comparison
• Enterprise AI Coding Service: Codex Distribution, Multi-Model Access, Sub-Account Governance, Private Deployment
• Understanding XAI Router
• XAI Router Codex Monthly Service: Codex / OpenClaw / OpenCode Access
• XAI Router Private Deployment