Multiple providersYour own keysA familiar workspace
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AI
Choose the providers and models that power assistant features across your instance.
Model providers
Leera checks enabled providers in priority order for a matching model and usable credentials.
Team AIopenai
https://api.openai.com/v1
primary-model, fast-model
Priority 20 · API key configured
Team fallbackanthropic
https://api.anthropic.com/v1
fallback-model
Priority 10 · API key configured
Interactive example · Sample providers, model IDs, and connection results.
Good tools leave room for your decisions
The work stays connected. The intelligence is your choice.
Provider choice is a data decision as well as a model decision. Review where prompts and project context will go, which terms apply, and what the model can do before making it part of your team’s work.
Choose what powers the work.
Use a supported provider or test a compatible local endpoint. Model IDs and request destinations stay configurable.
Bring the relationship you have.
Connect with your own credentials. Review the provider’s usage costs, retention terms, and agreements for your data.
Keep room to change your mind.
Add another provider, test its connection, and keep a working configuration available as your needs change.
One workspace. More possibility.
A different model. The same place for work.
Bring a model into the context your team already shares: issues, plans, documents, and conversations. Evaluate it against the work you actually do.
Configure OpenAI, Anthropic, Gemini, or Vertex AI in instance settings. Use a compatible local endpoint when model processing needs to stay on your network, and verify its capabilities with your workflow.
01
OpenAI
+ compatible endpoints
Use OpenAI, or point the base URL to a service that supports the same API format.
02
Anthropic
Native Messages API
Connect Claude models through Leera’s native Anthropic provider configuration.
03
Google Gemini
API key connection
Connect using a Gemini API key from Google AI Studio, with editable model IDs.
04
Google Vertex AI
Service account access
Use the Google service account configured on your instance to connect through Vertex AI.
Need model processing on your network?
Configure a compatible local service through the OpenAI provider path with its base URL and model IDs. Verify the network path, tool use, structured output, and response behavior with a representative task.
A connection that answers is the starting point. A model that handles your team’s work well is the goal.
01
Make the connection yours.
As an instance administrator, choose a provider, name the connection, and set its base URL. Add your API key or use the configured Vertex service account.
Provider · Endpoint · Credentials
02
Give it something to answer.
Add the model IDs you want to use, then run the connection test. Inspect the reply and response time before moving into a real workflow.
Models · Test reply · Latency
03
Try it with your actual work.
Ask a project question or run a representative task. Check the answer, tool behavior, and provider usage. Keep a working provider available as a fallback.
Quality · Reliability · Usage
A little flexibility goes a long way
Give your setup another way forward.
Configure more than one provider. Leera checks enabled providers in priority order to find a model that fits the request, with usable credentials.
Test each connection, enable the ones you need, and disable the ones you’re still evaluating.
Self-hosting controls the application. Provider configuration controls the model destination. Review both decisions before sending project context to AI.
Where Leera runs.
Self-hosting puts the application on infrastructure you choose. Instance settings apply across the workspaces on that deployment.
Where the request goes.
AI requests go to your configured endpoint. Choose an external provider or a compatible local service, then verify the request path.
How credentials are handled.
Provider API keys are stored encrypted and are not returned to the interface. Review the chosen provider’s data and retention terms.
Ownership is a feature.
Know whose key.Know where usage lands.
Your infrastructure. Your brand. Your AI account.With self-hosted Leera, those choices stay with you.
Self-hosted & white-label
Run it on your terms
Your self-hosted instance
Make the whole setup yours.
Run Leera on infrastructure you choose. Make it your own with your brand and domain. Connect the AI providers you want, using your own credentials.
Infrastructure handled for youConceptual illustration
Prefer a managed workspace?
Leera Cloud
Let Leera host the workspace. AI access and usage follow your Cloud plan and its credit arrangements. Choose the plan that fits the way your team works.
Instance settings include OpenAI and OpenAI-compatible endpoints, Anthropic, Google Gemini with an API key, and Google Vertex AI with a configured service account. Model names and base URLs are editable. Available model features depend on the selected provider and endpoint.
Who can change the provider configuration?+
The AI provider controls shown here are in the self-hosted edition’s Instance Settings. An instance administrator manages them, and the configuration applies across the workspaces on that instance. Review the AI arrangements for your plan if you use Leera Cloud.
Do we have to use the preset model names?+
No. Presets are starting points. Enter the model IDs available to your provider account, add at least one model, and test the connection. Model capabilities determine whether the workflow’s tools and structured output work as expected.
Is every OpenAI-compatible endpoint interchangeable?+
No. Compatibility with an API format does not guarantee the same model features, tool behavior, or response quality. Test the endpoint against the specific Leera workflow you intend to use.
Does self-hosting keep every AI request on our server?+
Self-hosting controls where the application runs. Model requests go to the provider endpoint you configure, which may be external. A compatible local endpoint can keep model processing on your network, but you must verify the request path, model capabilities, and your deployment’s other services. Review provider retention and data terms before sending project context.
Is choosing a model also a data-handling decision?+
Yes. The selected provider receives the prompts and project context needed by the AI workflow. Decide which data may be sent, where the endpoint runs, and which retention and contractual terms apply. Configure and test the setup for your deployment; provider choice alone is not a compliance certification.
How are our provider keys handled?+
Provider API keys are stored encrypted and are not returned to the interface after saving. Vertex AI uses the Google service account configured on the deployment. The interactive example on this page uses a fixed demo credential and never contacts a provider.
Can we keep more than one provider configured?+
Yes. Configure multiple providers and enable the ones you want to use. Leera checks them in priority order for a model that matches the request and has usable credentials. A newly added provider receives the highest priority. Test each connection with the models you intend to use.
Where is AI usage billed?+
When your self-hosted instance uses your own provider keys, model usage follows the pricing and account arrangements with that provider. Leera Cloud has its own plan and AI credit arrangements. Review both the deployment plan and the provider account when deciding on your setup.
Bring Your Own Model
Your work. Your models. A setup you can explain.
Choose the infrastructure, model endpoint, and data terms behind the work.
