Managed AI platform. Gateway. Workspace.
Controlled AI for teams, without provider sprawl.
2342.ai is the German platform layer for teams that want to use modern AI productively and under control: workspace, API access, model routing, roles, budgets, files, RAG and usage transparency. One invoice, one team access point, one budget. Not 2,342 separate accounts and bills.
We do not run our own foundation model and we do not sell provider accounts. We run the layer companies need: access, policy, routing, budgets, logs and clear data paths.
One workspace, not a tool zoo
AI gets expensive when every team builds its own little shelf of tools.
A team does not need private logins, scattered prompts and invoices that accounting has to decode later. It needs one workspace, one invoice, clear budgets and an API that behaves like the OpenAI workflows already in place.
What matters day to day
If AI is supposed to stay inside the company, the gateway has to be controllable.
Not with strategy slides. With daily operating questions: which teams may use which model configurations, which access is approved, which providers are allowed for sensitive data, which API keys are active, and what usage sits on top of the team access fee.
SOTA models without tool sprawl
The privacy story is central access, roles, budgets and traceable usage. For the team it stays simple: one workspace, one API, clear control.
Good prompts do not belong in private notes
When a workflow works, it belongs to the team. Otherwise everyone copies half a template from some chat history and calls it process.
The task decides, not provider folklore
Some work needs a cheap model. Some needs research. Some needs image generation. Users should not have to study provider politics first.
One invoice beats a pile of model bills
Charts after month end are accounting, not control. What matters are limits, owners and one budget that does not fall apart across ten tool accounts.
What you get
A platform that makes sense to purchasing, IT and the teams doing the work.
One governed gateway instead of a provider zoo
2342.ai can route workloads to supported third-party model providers according to workspace policy, availability, cost and compliance settings. One login. One budget. One place where rules apply.
Platform fee and usage stay separate
The plan pays for the shared access layer. Model usage stays visible as its own budget, so nobody confuses a seat fee with the real cost of the work.
Platform operation in Germany
The platform runs on our own infrastructure in Nuremberg. That does not solve every model-provider question, but it removes the application layer from the usual cloud patchwork.
OpenAI-style and Anthropic-style API for easier migration
Existing OpenAI-style or Anthropic-style integrations can connect to 2342.ai with minimal changes. Requests still pass through tenant policy, model routing, budget controls, usage tracking and provider-specific compliance rules.
Chat, research, RAG and files in one place
The workday should not jump between ten tabs. Projects, threads, storage, vector data and research belong on the same workbench.
Team access without seat penalty
When ten people work with AI, the same base fee should not be due ten times. The team grows. Fixed costs do not have to run behind it.
Standards instead of gut feeling
Good individual tricks only become a process when the team can reuse them.
A useful prompt hidden in a private chat helps exactly one person. A shared team prompt helps the whole operation. Add budgets, roles and traceable models, and AI becomes a tool. Not a foggy side project.
How rollout starts
Put order in place first. Then let usage grow.
Set the budget frame
Start with a clear monthly frame instead of five individual provider subscriptions someone has to reconcile later.
Make the team operational
Access, prompts, models and rules move into one place. No internal tool bazaar.
Watch usage
Chat, research, images and API run together. Costs stay visible before they become a month-end problem.
Pricing
Do not charge by chair when usage writes the bill.
Seat pricing looks clean while only three people are involved. Once AI spreads across departments, you suddenly pay for attendance instead of work. Our logic: a fixed platform fee for team access, a shared usage budget for model consumption, no penalty for adding another person.
Starter
25 EUR / monthFor individual users and small internal tests.
- Managed platform access and API
- Curated model catalog with routing rules
- Usage budget visible separately from access
Professional
99 EUR / monthFor teams that use AI daily, not just as an experiment.
- Team access without per-member pricing
- Shared usage budget for model consumption
- Prompt library and team functions
- Usage and budget view for owners
Enterprise
199 EUR / monthFor companies with more usage, more rules and API integration.
- Higher limits and prioritized support
- Own operating and approval processes
- Technical help for rollout, API usage and usage budgets
Questions before rollout
The hard objections belong on the table before anyone buys.
Why no seat prices?
Because AI costs are not cleanly attached to office chairs. Usage, model choice and control matter. Seat models punish growth in the wrong place.
Does 2342.ai run its own models?
No. We run the platform, API, budget logic and team controls. The models come from several providers. That is exactly why the layer in between matters.
Is this only an API or also a workspace?
Both. Developers can connect existing tools through the API. Teams can work directly with chat, research, images, files and reusable prompts.
How is this different from individual provider subscriptions?
You bundle access, costs, rules and teamwork in one place. That saves money, but also coordination, shadow processes and privacy headaches.
How does this fit GDPR and compliance work?
The platform runs on our own infrastructure in Nuremberg. The privacy lever is central access, roles, budgets and traceable usage, instead of scattered individual tool accounts.
How fast can a team start productively?
As soon as login, budget and first standards are in place. That is why we prioritize clear defaults, reusable prompts and traceable models over feature theater.
Next step