11 Generative AI APIs to Integrate With: OpenAI, Anthropic, Gemini and Unified AI Infrastructure
March 25, 2026
Generative AI is no longer a single-provider decision.
Most AI-native SaaS products now need to support multiple models across providers like OpenAI, Anthropic Claude, and Google Gemini. Teams are building:
- AI copilots
- agent workflows
- RAG pipelines
- embeddings infrastructure
- multi-model routing systems
The problem is that every AI provider exposes a different API.
Different request formats, different token handling, different model naming, different streaming behavior, and different error handling. Supporting multiple models quickly turns into fragmented infrastructure.
This guide covers the top generative AI APIs developers integrate with, and how to simplify multi-model support using a unified API.
What is a Generative AI API?
A generative AI API allows your application to send prompts to AI models and receive structured or unstructured outputs.
Typical capabilities include:
- text generation
- chat completion
- embeddings
- multi-turn conversations
- tool or function calling
- structured outputs
- streaming responses
Each provider offers similar capabilities, but the implementation details vary enough to create integration overhead.
Why SaaS products integrate with multiple AI APIs
Most teams do not stick to one model anymore.
Common reasons include:
Model performance differences
Some models perform better for reasoning, others for speed, others for cost.
Fallback and reliability
If one provider fails or rate limits, requests can be routed elsewhere.
Cost optimization
Different models have different pricing. Routing intelligently can reduce spend.
Feature differences
Some providers support better embeddings, tool calling, or longer context windows.
Customer requirements
Enterprise customers often require specific providers for compliance or internal standards.
This is why multi-model AI architecture is becoming the default.
11 generative AI APIs developers integrate with
Below are the most commonly used AI APIs across modern SaaS products.
OpenAI API
The most widely used generative AI API for chat, completions, embeddings, and tool calling.
Common use cases:
- AI copilots
- assistants
- chat interfaces
- embeddings for RAG
- structured outputs
Anthropic Claude API
Known for strong reasoning, long context windows, and enterprise-safe outputs.
Used for:
- complex reasoning workflows
- enterprise AI assistants
- long-form analysis
Google Gemini API
Google's AI platform for multimodal models and enterprise-scale AI infrastructure.
Used for:
- multimodal inputs
- enterprise AI tooling
- integrations with Google ecosystem
Azure OpenAI API
Enterprise deployment of OpenAI models with Microsoft infrastructure and compliance controls.
Used for:
- enterprise AI deployments
- regulated environments
- Azure-native products
Cohere API
Strong focus on embeddings, classification, and enterprise NLP.
Used for:
- semantic search
- embeddings
- classification tasks
Mistral AI API
High-performance open-weight models with competitive latency and cost.
Used for:
- cost-efficient inference
- high-performance applications
Hugging Face API
Large ecosystem of open-source models and inference APIs.
Used for:
- custom model deployment
- experimentation
- open-source AI workflows
Groq API
Known for ultra-fast inference speeds.
Used for:
- latency-sensitive applications
- real-time AI systems
DeepSeek API
Emerging provider with strong performance in reasoning and coding tasks.
AnyScale API
Infrastructure platform for deploying and scaling AI models.
X.ai Grok API
AI models integrated with real-time data and conversational interfaces.
Challenges when integrating multiple AI APIs
Building against one AI API is straightforward.
Supporting multiple introduces real complexity.
Different request and response formats
Each provider structures prompts, messages, and responses differently.
Model-specific parameters
Temperature, max tokens, streaming behavior, and tool calling vary by provider.
Authentication and rate limits
Each API has its own auth model, limits, and quotas.
Output inconsistency
Even when prompts are identical, outputs vary significantly across models.
Maintenance overhead
Every provider evolves quickly. Keeping up with changes becomes ongoing work.
For teams building AI features into their product, this becomes a bottleneck.
The role of unified Generative AI APIs
A unified Generative AI API standardizes how your application interacts with different models.
Instead of writing separate integrations for OpenAI, Anthropic, Gemini, and others, you:
- send one request format
- receive one response structure
- switch models without rewriting logic
- route requests dynamically
- compare outputs across providers
This allows you to build AI features once and evolve your model strategy over time.
Build once with the Unified Generative AI API
The Unified Generative AI API enables access to 11+ AI providers through a single API.
Supported providers include:
- OpenAI
- Anthropic Claude
- Google Gemini
- Azure OpenAI
- Cohere
- Mistral
- Hugging Face
- Groq
- DeepSeek
- AnyScale
- xAI Grok
Unified AI objects
Unified standardizes AI interactions into three core objects:
Model
Represents available AI models across providers.
Prompt
Handles prompt execution, messages, responses, and token usage.
Embedding
Generates vector embeddings for search, RAG, and semantic applications.
These objects allow consistent interaction across all providers.
Why teams choose Unified.to for AI integrations
One API across all models
Instead of writing separate integrations, you integrate once and support all major AI providers.
Real-time, pass-through execution
Every request hits the model provider directly. No caching, no stale responses, no intermediate storage.
Zero-storage architecture
Unified does not store prompt data or responses at rest, which reduces risk when working with sensitive inputs or proprietary data.
Multi-model routing
Route requests across providers based on:
- cost
- latency
- availability
- performance
Model comparison
Run the same prompt across multiple providers and compare outputs programmatically.
Built for AI-native products
Unified integrates directly into:
- RAG pipelines
- embeddings workflows
- agent architectures
- MCP-based tool systems
This is not just API aggregation. It is infrastructure for AI products.
Common use cases for a unified AI API
Multi-model AI applications
Support multiple providers without rewriting integration logic.
AI copilots
Access real-time data and generate responses using the best available model.
RAG pipelines
Combine embeddings and live data retrieval for accurate AI responses.
AI agents
Allow agents to interact with external systems using structured, secure tool calls.
Cost optimization
Route requests to lower-cost models when appropriate.
Final thoughts
Generative AI integration is no longer about choosing a single provider.
It is about building flexible infrastructure that can:
- support multiple models
- adapt quickly as providers evolve
- reduce engineering overhead
- keep data secure
- maintain real-time accuracy
A unified Generative AI API is the fastest way to get there.
Instead of rebuilding integrations every time a new model emerges, you build once and evolve your AI stack over time.