AI API Gateway Custom Endpoints

Customization Capabilities

Flexible tools to create API endpoints that match your exact requirements.

🔀

Dynamic Routing Logic

Implement sophisticated routing rules that direct requests to different AI models based on request parameters, user context, or custom business logic. Route high-priority requests to premium models while handling standard queries with cost-effective alternatives.

🔄

Request Transformation

Transform incoming requests to match the exact format expected by your AI models. Apply custom validation, inject system prompts, restructure payloads, and normalize inputs from diverse client applications into standardized model requests.

🎯

Domain-Specific Interfaces

Create specialized API interfaces tailored to specific domains such as healthcare, finance, or legal applications. Define custom schemas, validation rules, and response formats that align with industry standards and regulatory requirements.

Common Endpoint Patterns

Proven patterns for designing effective custom AI API endpoints.

PATTERN

Versioned Endpoints

Maintain multiple API versions simultaneously, allowing clients to upgrade at their own pace while ensuring backward compatibility and smooth transitions between versions.

POST /api/v1/completions POST /api/v2/completions POST /api/v3/completions // Version-specific routing v1 → gpt-3.5-turbo v2 → gpt-4-turbo v3 → gpt-4o

PATTERN

Action-Based Endpoints

Expose high-level actions rather than raw model interactions. Clients specify intent while the gateway handles prompt engineering, model selection, and response formatting internally.

POST /api/analyze/sentiment POST /api/extract/entities POST /api/summarize/article POST /api/classify/topics // Semantic routing Each action → optimized prompt

PATTERN

Multi-Model Aggregation

Aggregate multiple AI models behind a single endpoint, returning combined results or selecting the best response based on quality metrics and response characteristics.

POST /api/ensemble/analyze // Internal routing → GPT-4 → result_1 → Claude → result_2 → Gemini → result_3 Response: { combined, best, scores }

PATTERN

Streaming Transform

Custom streaming endpoints that transform AI responses in real-time, applying formatting, filtering sensitive content, or enriching output with additional metadata as data flows to clients.

GET /api/stream/chat // Stream transformation chunk_1 → parse → transform → emit chunk_2 → parse → transform → emit chunk_3 → parse → transform → emit

Implementation Examples

Practical code for implementing custom endpoints in your AI gateway.

custom_endpoints.py Python

# Custom AI API Gateway Endpoints Implementation
from dataclasses import dataclass
from typing import Callable, Dict, Any, Optional
from functools import wraps

@dataclass
class CustomEndpoint:
    """Custom endpoint configuration"""
    path: str
    method: str
    model: str
    request_transform: Optional[Callable] = None
    response_transform: Optional[Callable] = None
    middleware: Optional[list] = None

class CustomEndpointRegistry:
    """Registry for custom API endpoints"""
    
    def __init__(self):
        self.endpoints: Dict[str, CustomEndpoint] = {}
        self.middleware_stack = []
    
    def register(self, endpoint: CustomEndpoint):
        """Register custom endpoint configuration"""
        key = f"{endpoint.method}:{endpoint.path}"
        self.endpoints[key] = endpoint
        print(f"Registered: {endpoint.method} {endpoint.path}")
        return endpoint
    
    def endpoint(
        self, 
        path: str, 
        method: str = "POST",
        model: str = "gpt-4-turbo"
    ):
        """Decorator for defining custom endpoints"""
        def decorator(func: Callable):
            endpoint_config = CustomEndpoint(
                path=path,
                method=method,
                model=model,
                request_transform=func
            )
            self.register(endpoint_config)
            
            @wraps(func)
            def wrapper(*args, **kwargs):
                return func(*args, **kwargs)
            
            return wrapper
        return decorator

# Initialize registry
registry = CustomEndpointRegistry()

# Define custom sentiment analysis endpoint
@registry.endpoint(
    path="/api/v1/analyze/sentiment",
    method="POST",
    model="gpt-4-turbo"
)
def sentiment_analysis_transform(request: dict) -> dict:
    """Transform request for sentiment analysis"""
    return {
        "model": "gpt-4-turbo",
        "messages": [
            {
                "role": "system",
                "content": """Analyze the sentiment of the following text.
                Return JSON with: sentiment (positive/negative/neutral),
                confidence (0-1), and key_emotions array."""
            },
            {
                "role": "user",
                "content": request.get("text", "")
            }
        ],
        "response_format": {"type": "json_object"}
    }

# Define custom entity extraction endpoint
@registry.endpoint(
    path="/api/v1/extract/entities",
    method="POST",
    model="gpt-4-turbo"
)
def entity_extraction_transform(request: dict) -> dict:
    """Transform request for entity extraction"""
    entity_types = request.get("types", ["person", "org", "location"])
    
    return {
        "model": "gpt-4-turbo",
        "messages": [
            {
                "role": "system",
                "content": f"""Extract entities of types: {entity_types}.
                Return JSON array with: entity, type, confidence."""
            },
            {
                "role": "user",
                "content": request.get("text", "")
            }
        ],
        "response_format": {"type": "json_object"}
    }

# Define custom summarization endpoint
@registry.endpoint(
    path="/api/v1/summarize",
    method="POST",
    model="gpt-4-turbo"
)
def summarization_transform(request: dict) -> dict:
    """Transform request for summarization"""
    length = request.get("length", "medium")
    style = request.get("style", "neutral")
    
    length_guide = {
        "short": "in 2-3 sentences",
        "medium": "in 1-2 paragraphs",
        "long": "comprehensively with key points"
    }
    
    return {
        "model": "gpt-4-turbo",
        "messages": [
            {
                "role": "system",
                "content": f"""Summarize the text {length_guide[length]}.
                Maintain a {style} tone."""
            },
            {
                "role": "user",
                "content": request.get("text", "")
            }
        ]
    }

# Process incoming request through custom endpoint
async def handle_custom_endpoint(
    method: str, 
    path: str, 
    request_data: dict
) -> dict:
    """Route request through appropriate custom endpoint"""
    key = f"{method}:{path}"
    
    if key not in registry.endpoints:
        raise ValueError(f"Endpoint not found: {key}")
    
    endpoint = registry.endpoints[key]
    
    # Apply request transformation
    transformed_request = endpoint.request_transform(request_data)
    
    # Forward to AI model
    response = await call_model_api(
        endpoint.model, 
        transformed_request
    )
    
    # Apply response transformation if defined
    if endpoint.response_transform:
        response = endpoint.response_transform(response)
    
    return response

AI API Gateway Custom Endpoints

Customization Capabilities

Dynamic Routing Logic

Request Transformation

Domain-Specific Interfaces

Common Endpoint Patterns

Versioned Endpoints

Action-Based Endpoints

Multi-Model Aggregation

Streaming Transform

Implementation Examples

Partner Resources