Self-Hosting LibreChat with Ace Data Cloud: A Practical Configuration Guide

Connecting a self-hosted chat interface to a model provider should be a configuration task, not a weekend spent debugging URL paths. This guide shows how to connect LibreChat to Ace Data Cloud through its OpenAI-compatible interface, verify the connection independently, and avoid the small mistakes that usually cause empty model lists or 404 responses.

What you are building

LibreChat supports custom OpenAI-compatible endpoints through librechat.yaml. The useful part of this approach is that LibreChat remains the interface—conversation history, model selection, and deployment stay under your control—while requests are routed to the configured API.

For this setup, the important pieces are:

• Base URL: https://api.acedata.cloud/v1
• Model discovery: GET https://api.acedata.cloud/v1/models
• Chat endpoint: POST https://api.acedata.cloud/v1/chat/completions
• Authentication: Authorization: Bearer {token}
• Request fields: model and messages

The distinction between a base URL and a final request URL matters. LibreChat appends the chat path itself. If the base is https://api.acedata.cloud/v1, the resulting request is https://api.acedata.cloud/v1/chat/completions, which is the intended route.

Configure the custom endpoint

Start from a working LibreChat Docker Compose deployment. Create a librechat.yaml file beside your Compose file and add a custom endpoint:

version: 1.0.5
cache: true

endpoints:
  custom:
    - name: "Ace Data Cloud"
      apiKey: "{token}"
      baseURL: "https://api.acedata.cloud/v1"
      models:
        default:
          - "MODEL_ID"
        fetch: true
      titleConvo: true
      titleModel: "MODEL_ID"
      modelDisplayLabel: "Ace Data Cloud"

Replace {token} with your API token. Replace MODEL_ID with an identifier returned by the models endpoint rather than guessing a name. With fetch: true, LibreChat can load the available model list automatically. Keeping a model under default is still useful as an explicit fallback.

Next, mount the configuration into the api service in docker-compose.yml:

volumes:
  - type: bind
    source: ./librechat.yaml
    target: /app/librechat.yaml

Then apply the change:

docker compose up -d

If the container was already running and a later YAML edit appears to do nothing, restart the API service:

docker compose restart api

One practical security note: avoid committing a real token to a public repository. LibreChat also supports apiKey: "user_provided" when each user should supply their own credential instead of sharing one server-side key.

Verify the API before debugging the UI

When an integration fails, testing the API directly separates provider, token, and network problems from LibreChat configuration problems. First inspect the current model list:

curl 'https://api.acedata.cloud/v1/models' \
  -H 'Authorization: Bearer {token}'

Choose a returned model ID, then send a minimal chat request:

curl -X POST 'https://api.acedata.cloud/v1/chat/completions' \
  -H 'Authorization: Bearer {token}' \
  -H 'Content-Type: application/json' \
  -d '{
    "model": "MODEL_ID",
    "messages": [
      {"role": "user", "content": "ping"}
    ]
  }'

An OpenAI-compatible chat.completion response tells you that the endpoint and token are working. At that point, a failure inside LibreChat is more likely to be YAML indentation, a missing bind mount, or stale container configuration.

This two-step check is the debugging habit I recommend most: prove the smallest request first, then add the client. It saves time because you are never troubleshooting the UI and the API simultaneously.

Avoid the base URL trap

Most 404 errors in this setup come from a nearly correct URL. These forms do not behave the same way:

Configured base URL	Result
https://api.acedata.cloud/v1	Correct; LibreChat calls /v1/chat/completions
https://api.acedata.cloud/openai	Also available; calls /openai/chat/completions
https://api.acedata.cloud/openai/v1	Incorrect; /openai does not add /v1
https://api.acedata.cloud	Incorrect; the request is missing /v1

If the model dropdown is empty, confirm that GET /v1/models succeeds with the same token. If automatic discovery still causes trouble, set fetch: false and list verified model IDs under models.default manually.

For APIs with model-specific parameter differences, LibreChat's dropParams setting can remove unsupported parameters before requests are sent. I would only add that after the minimal configuration works; fewer moving parts make the first successful request easier to understand.

A small integration that stays maintainable

The durable version of this setup is intentionally boring: one mounted YAML file, one exact base URL, model IDs obtained from the live model endpoint, and a curl request you can rerun whenever something changes. That gives you a reliable boundary between your self-hosted chat client and the model layer behind it.

If you want the source configuration and troubleshooting notes beside you while setting it up, keep the Ace Data Cloud LibreChat guide open as a reference. My founder-to-builder advice is simple: make the smallest request observable first, then let the UI make it convenient.