Provider Integrations
Quantlix integrates with major cloud model APIs—plus a built-in demo model for first success without keys. Providers are org-scoped; credentials are encrypted at rest.
What is supported today
- OpenAI and Anthropic — typical chat workloads; sync models after saving your API key.
- Azure OpenAI, AWS Bedrock, Groq, Together AI — connect per your cloud setup; capabilities depend on the model you enable.
- Voyage AI — primarily embeddings for knowledge bases and document pipelines.
- qx-example / orchestrator — built-in or self-hosted path for demos and tests when you do not want an external provider yet—not a substitute for a full vendor SLA.
Portal
Dashboard → Providers — Add Provider, choose type (Voyage AI, OpenAI, Anthropic, etc.), set credential (API key), Sync Models. Then bind a deployment to a provider model via the deployment detail page (Inference target).
Setup
- Create an org (if needed)
- Add Provider → choose type (e.g. Voyage AI for embeddings)
- Set Credential → paste API key
- Sync Models → pick models (chat, embeddings, etc.)
- Create a deployment, then bind it to a provider model in the deployment detail
Provider notes
Anthropic
Commonly used for Claude chat workflows and DPA-safe ticket analysis demos.
OpenAI
Commonly used for chat and general model inference workloads.
Azure OpenAI
Useful when your organization already governs OpenAI access through Azure.
AWS Bedrock
Use when your model access and compliance controls are centered in AWS.
Groq / Together
OpenAI-compatible chat providers. Native tool calling depends on the selected model.
Voyage AI
Primarily used for embeddings in knowledge and retrieval pipelines.
Provider-backed deployments
Workflow model and agent nodes run through provider-backed inference targets bound to deployments.
Use cases
- Chat inference — Bind deployment to OpenAI/Anthropic for inference
- Embeddings — Voyage AI for embeddings (powers knowledge bases and document lookup)
- Document Q&A — Embedding provider + knowledge base for retrieval (often called RAG)
- Multi-model workflows — Bind different model nodes to different provider-backed deployments in one workflow. Read the multi-model workflow guide →
- Native agents — Use provider-backed chat models with native tool calls. Quantlix normalizes tool calls across Anthropic and OpenAI-compatible providers, but actual support still depends on the provider and model you select.
Using provider models with MCP data
If your workflow fetches context from an MCP server and then calls a provider model (for example Anthropic), use a stable result_field and route redaction plus model prompting through the text field that exists after redaction. Quantlix normalizes MCP tool output so custom payload shapes still work.
Credentials and data flow
Provider credentials are org-scoped and encrypted at rest. Quantlix uses them server-side to call the selected provider model through an inference target. What the provider receives depends on the deployment, workflow, policies, and redaction steps configured before the model call.
For audit proof, inspect the model node output and the Analyze page's “What reached Claude” section. It shows provider/model metadata and the payload sent to the provider.