Deploy and configure AI agents for coding on a virtual server: GPT/KIMI K3/CLAUDE

Budget: 650 USD Deadline: 7 days

We can help, but 70 USD is not a budget for a sustainable multiplayer setup. A realistic first engineering phase is 650 USD and 7 days. This can include a basic server environment, isolated sessions for several developers, connection to Claude, GPT, and KIMI K3, key storage, spending limits, action logs, and a short guide for the team =/

I would start with access architecture and use cases, then deploy the working environment, check the parallel operation of 4-5 models, user rights, and recovery from failures. Look, there’s a nuance here - if you do it without design, it will be expensive to catch errors in access and limits later. It seems better to calculate the risks properly once than to fix the server like a shaman with a keyboard later.

> Please clarify, is the server already chosen or do you need a configuration recommendation?
> Do you only need terminal agents for code or also a web panel for developers?

Similar cases from Ingello
> https://business.ingello.com/fractal - agent architecture and repeatable workflows
> https://business.ingello.com/vorfahr - AI automation around complex product logic
> https://systems-fl.ingello.com - main landing page of Ingello for projects with architecture, AI, and automation

Then, after access to the server, a list of models, number of users, and repository requirements, a precise estimate for the full version can be provided. One important point - if a level with roles, auditing, backup, monitoring, and security policies is needed, this will be a separate assessment after the first phase. Expand

Budget: 300 USD Deadline: 2 days

set up a server for 4-5 models for the team — the topic is familiar. I have already done a similar setup and worked with open webui. here, the main thing is to configure session isolation and restrict access to keys. from my experience, I know that if clients are directly connected to the models, developers will interfere with each other in the history or accidentally leak limits. it’s best to wrap everything through docker and set up litellm as a proxy.
services:
litellm:
image: website
ports:
- 4000:4000
the plan is simple: prepare docker-compose with litellm and postgresql, configure session isolation in open webui, run tests, and transfer access. message me if you have questions.

Budget: 300 USD Deadline: 10 days

I implement through Docker containers for users: isolation of the file system, code, and environment variables (API keys). Orchestration — Docker Compose / Swarm with CPU/RAM limits. Access — via code-server or SSH bastion with Traefik/Nginx for HTTPS and authorization. Session state — in mounted volumes or Redis.

Experience: deployed multi-tenant asynchronous environments on VPS (Docker, Celery, Redis) with task isolation and bypassing limits of external APIs.

Budget: 8000 USD Deadline: 7 days

I often deploy backend systems to work with multiple AI models simultaneously, including session isolation and managing parallel requests — this is my specialty. Let's discuss the architecture of your setup, how many users you plan to have, and what specific coding tasks the agents should perform, then I can provide an accurate estimate of timelines and costs right here.

Budget: 350 USD Deadline: 4 days

We already have a nearly ready solution for server-side work with AI agents for development; it can be quickly adapted to your server and discussed here now - I'm available ))

Regarding timelines - 4 days for a basic working version with users, isolated sessions, GPT, Claude, Kimi K3 setup, and instructions for the team.

As for the budget - 350 USD for the first stage. 70 USD will likely be enough for a short consultation or review of the ready scheme, but not for a full-fledged multi-user setup.

There’s a nuance here... the key point is !!multi-user isolation!!, so that agents from different developers do not interfere with each other, do not mix keys, and do not break environments.

AI agents can be deployed through terminal agents, session managers, separate Linux users, limits, logs, and secure storage of model keys.

What you need to provide is access to the server through the exchange, OS and VPS specifications, a list of developers, API keys for GPT, Claude, and Kimi K3, as well as the desired access rules for developers.

I would like to clarify two things - is the server already chosen and is it Ubuntu, or do we need to select a configuration based on the load?

Should the models work only through the API, or do you want to run part locally on the GPU?

Similar cases - https://business.ingello.com/fractal - agent architecture and workflow automation.

Similar cases - https://business.ingello.com/vorfahr - AI and automation in product logic.

Ingello profile for FLH - https://systems-fl.ingello.com Expand