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Generative AI API Integration: Multi-Model Prompts, Routing, and Embeddings Across Providers

February 12, 2026

Generative AI systems sit at the point where your product turns text into outputs your users rely on. Providers change model names, feature flags differ, and teams end up maintaining multiple SDKs, model lists, and request shapes just to keep inference working across OpenAI, Anthropic, Gemini, and others. Generative AI API integration exists to reduce that integration surface area.

In this guide, we'll explain what a Generative AI API covers, which objects matter in practice, how requests behave in production, and how Unified's GenAI API fits alongside Storage, KMS, Repository, Messaging, and MCP in the broader platform.

Introduction to Generative AI API Integrations

Generative AI providers such as OpenAI, Anthropic Claude, Google Gemini, Cohere, MistralAI, Groq, Hugging Face, Azure OpenAI, DeepSeek, and others each ship their own APIs, auth patterns, and request/response formats.

Products that support multiple model providers usually run into:

  • Different chat message formats and response shapes
  • Provider-specific model catalogs and capability flags (temperature, max token behavior, tool support)
  • Separate embedding endpoints with inconsistent vector formats and dimensionality options
  • Provider-specific failure modes when a model is unavailable or rate-limited
    A Generative AI API provides a category-scoped interface for model discovery, prompt execution, and embedding generation across providers.

What Is a Generative AI API?

A Generative AI API allows applications to programmatically:

  • List available models and model capabilities
  • Send prompts/messages to an LLM and receive responses
  • Generate embeddings for text inputs
    It focuses on model calls and embedding generation. It is not a chat product, a vector database, or a place to store prompt history.

Generative AI API vs Storage, KMS, Repository, Messaging, and MCP

Unified keeps clear boundaries between categories:

  • GenAI handles Model, Prompt, and Embedding objects across LLM providers.
  • Storage handles files and folders (download URLs, hierarchical traversal, metadata).
  • KMS handles spaces, pages, and comments in knowledge platforms (Confluence, Notion, Guru, etc.).
  • Repository handles orgs, repos, branches, commits, and pull requests in version control systems.
  • Messaging handles channels, messages, and events in chat platforms.
  • MCP is the tool execution layer. It makes Unified integrations callable as tools inside LLM workflows.
    GenAI can be used alongside these categories (for example, embedding KMS pages or summarizing storage files), but those objects remain in their own APIs.

Real-Time Behavior and Request State

Live requests to model providers

GenAI requests are executed against the selected provider in real time. Unified does not run sync jobs or maintain stored copies of prompt inputs, outputs, or embeddings.

Stateless execution for Prompt and Embedding

Prompts and embeddings are one-shot operations:

  • There is no prompt ID.
  • There are no list or retrieve endpoints for prompts or embeddings.
  • If you need history, you include it yourself in the messages array or store it in your system.
    Models are different: they can be listed and retrieved as catalog metadata.

Core GenAI Data Models

Unified normalizes three objects in the GenAI category.

Model

Models represent provider model metadata.

Key fields include:

  • id, name, description
  • max_tokens
  • has_temperature
  • web_url
    Models are read-only. Your application lists models to discover what's available and to understand capability flags (for example, whether temperature is supported).

Prompt

Prompts represent a single inference request and its response.

Key request fields include:

  • model_id
  • messages
  • temperature (when supported by the model)
  • max_tokens
    Key response fields include:
  • responses
  • tokens_used
    MCP-related fields exist on the Prompt object:
  • mcp_url (optional)
  • mcp_deferred_tools (optional)
    These support workflows where a model call needs to reference MCP tooling, but GenAI itself does not create conversation threads or retain state between calls.

Embedding

Embeddings represent a single embedding request and its returned vector.

Key fields include:

  • model_id
  • content
  • encoding_format
  • dimension
  • type
  • embeddings
  • tokens_used
    Embeddings are returned to your application. Unified does not provide a vector index or vector search layer. If you need retrieval, you store vectors in your own database and handle similarity search there.

Identity and Cross-Category Isolation

GenAI objects do not reference Storage files, KMS pages, Repository objects, CRM entities, or Ticketing objects.

  • Model identifiers map to LLM providers' model naming.
  • Prompts reference a model_id and carry messages and responses for that request only.
  • Embeddings reference a model_id and return vectors for that request only.
    If you want to connect GenAI outputs to other systems, you do it explicitly in your application (for example, store an embedding alongside the KMS page ID you generated it from).

Updates, Events, and Polling

GenAI does not behave like event-driven SaaS categories (Ticketing, Shipping, CRM) because prompts and embeddings are terminal operations.

  • No webhooks are emitted for Prompt or Embedding objects.
  • There is nothing to poll for prompts or embeddings because there are no list endpoints.
  • Models can be polled via the models list endpoint (including updated_gte support) to detect catalog changes over time.

Common GenAI API Integration Use Cases

Multi-model inference

Route prompt execution across multiple providers without rewriting request formats per vendor. This is useful for provider fallback, latency-sensitive workloads, and controlled rollout across models.

Model comparison

Send the same prompt to multiple model IDs and compare responses, token usage, and output differences. Useful for evaluation workflows and regression testing.

Embedding generation for retrieval

Generate embeddings using the provider you want, store vectors in your own system, and build retrieval on top of your stored vectors.

Prompt testing and management

Standardize how your team defines prompt payloads and model selection logic so prompt changes don't require per-provider rewrites.

Security and Data Handling

GenAI requests and responses can include sensitive content. Unified's architecture is designed to avoid creating a data store of prompt content.

  • Requests are executed in real time.
  • Prompt payloads, responses, and embedding vectors are returned to your application.
  • If you need persistence, you store the data in your own infrastructure.
    Provider retention policies still apply at the model provider layer. Your compliance posture depends on both your own retention decisions and the provider configuration you choose.

Build vs Maintain Multi-Provider GenAI Integrations

Building in-house

  • Multiple provider SDKs and auth patterns
  • Different request/response shapes per provider
  • Separate model catalogs and capability flags
  • Extra work to implement routing and fallback consistently

Using Unified's GenAI API

  • A category-scoped API surface for models, prompts, and embeddings
  • Unified request shapes across providers
  • Clear separation from content categories (Storage, KMS, Repository) and execution tooling (MCP)
  • Direct control over what your application stores

Best Practices for GenAI Integrations

  • Treat Prompt and Embedding as execution calls, not stored objects.
  • Persist only what you need (and decide retention explicitly).
  • Use Model metadata to drive feature flags (temperature support, token limits).
  • Store embeddings in your own vector store, keep model_id and dimension alongside them.
  • Keep category boundaries clean: retrieve content via Storage/KMS/Repository, generate via GenAI, execute tools via MCP.

Build GenAI integrations that stay portable

If your product supports multiple model providers, you need consistent request shapes, predictable model discovery, and a clean separation between model execution and the data your application stores.

Unified's GenAI API gives you normalized Model, Prompt, and Embedding objects across providers, while keeping execution stateless and category boundaries intact.

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FAQ

What does the GenAI API cover?

Models, prompt execution, and embedding generation across multiple LLM providers.

Does Unified store prompt history or embeddings?

No. Prompts and embeddings are returned in the response. Persistence is handled by your application.

Is there a vector database included?

No. The API returns embedding vectors only. Vector storage and retrieval are handled externally.

How does this relate to MCP?

GenAI handles model calls and embeddings. MCP is the tool execution layer that makes Unified integrations callable inside LLM workflows.

Which providers are supported?

Unified's GenAI category includes providers such as OpenAI, Anthropic, Google Gemini, Cohere, MistralAI, Groq, Hugging Face, Azure OpenAI, DeepSeek, AnyScale, and X.ai Grok.

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