Top LLM Gateways 2025

LLMs improved rapidly. Almost every software today needs to integrate LLMs.

Managing many LLM providers in production comes with many challenges. While prototyping with a single API key is simple, operating LLM-powered applications at scale presents significant challenges. Developers frequently encounter issues like rate limits, provider outages, and inconsistent model performance (latency, accuracy, cost). Furthermore, continuous change management is required as providers update models, sometimes altering outputs without notice. API key management, access control, and the risk of vendor lock-in further complicate matters, highlighting that LLMs are not plug-and-play in real-world systems.

For practitioners, building robust, future-proof LLM applications demands fallback strategies, observability, and multi-provider architectures. This is precisely where LLM gateways become essential: a new infrastructure designed to abstract complexity, enhance reliability, and provide teams with the flexibility to adapt as the LLM ecosystem continues to evolve.

LLM APIs

For most teams building with large language models, APIs have become the default access point. Rather than self-hosting models which requires significant compute, fine-tuning pipelines, and operational expertise developers typically rely on hosted endpoints from providers like OpenAI, Anthropic, or Hugging Face. This approach simplifies integration but introduces a new set of challenges: every provider has its own API syntax, authentication mechanism, response structure, and conventions for advanced features such as tool calling or function execution.

Consider the following examples :

OpenAI Example

Even when working with a single provider like OpenAI, developers quickly discover that different generation tasks require different API calls or parameters. While the basic interaction pattern is always “prompt in → text out”, the surrounding API surface varies depending on the task.

1. Basic Freeform Text

Send a string and get a response:

from openai import OpenAI
client = OpenAI()

response = client.responses.create(
    model="gpt-5",
    input="Write a short bedtime story about a unicorn."
)

print(response.output_text)

• input → prompt

• response → aggregated response

2. Structured Outputs (JSON Mode)

Extract structured data instead of prose:

[
    {
        "id": "msg_67b73f697ba4819183a15cc17d011509",
        "type": "message",
        "role": "assistant",
        "content": [
            {
                "type": "output_text",
                "text": "Under the soft glow of the moon, Luna the unicorn danced through fields of twinkling stardust, leaving trails of dreams for every child asleep.",
                "annotations": []
            }
        ]
    }

Anthropic Example

Anthropic’s Claude models use the Messages API, following a prompt-in → text-out pattern with some differences.

1. Access & Authentication

• API keys generated in the Anthropic Console.

• Requests include x-api-key header.

• JSON required for all requests and responses.

2. Request Size Limits

• Standard endpoints: 32 MB

• Batch API: 256 MB

• Files API: 500 MB

  Exceeding limits → 413 request_too_large error.

3. Response Metadata

• request-id → unique request identifier

• anthropic-organization-id → links to org

4. Example (Python)

import anthropic

client = anthropic.Anthropic(api_key="my_api_key")

message = client.messages.create(
    model="claude-opus-4-1-20250805",
    max_tokens=1024,
    messages=[{"role": "user", "content": "Write a one-sentence bedtime story about a unicorn."}]
)

print(message.content)

Typical response:

{
  "id": "msg_01ABC...",
  "type": "message",
  "role": "assistant",
  "content": [
    { "type": "text", "text": "In a meadow of silver light, a unicorn whispered dreams into the stars." }
  ]
}

Groq Example

Groq focuses on ultra-fast inference using custom LPU (Language Processing Unit) chips. Ideal for latency-sensitive applications like chatbots, copilots, and edge AI.

• Direct API: Authorization: Bearer <GROQ_API_KEY>

• Hugging Face API (optional): provider="groq" with HF_TOKEN

Example via Hugging Face

import os
from huggingface_hub import InferenceClient

client = InferenceClient(
    provider="groq",
    api_key=os.environ["HF_TOKEN"],
)

completion = client.chat.completions.create(
    model="openai/gpt-oss-120b",
    messages=[{"role": "user", "content": "What is the capital of France?"}]
)
print(completion.choices[0].message)

Response:

{
  "choices": [
    {
      "message": {
        "role": "assistant",
        "content": "The capital of France is Paris."
      }
    }
  ]
}

Comparison Between APIs

We've seen that each provider (OpenAI, Anthropic, Groq, and others) has a different API. Each uses a different authentication method, request format, role definition, and response structure.

Now imagine you want to switch between providers (or combine multiple providers in one application). You'd need to rewrite the entire LLM logic each time. This quickly becomes complex and error-prone.

LLM gateways solve this problem. They provide a unified interface for LLM calls. LLM gateways act as a middleware layer that abstracts provider-specific differences. They give you a single, consistent API to interact with all models and providers.

Top LLM Gateways

LLM Gateways provide a single interface to access multiple large language models (LLMs), simplifying integration, observability, and management across providers.

1. LiteLLM

LiteLLM is a versatile platform allowing developers and organizations to access 100+ LLMs through a consistent interface. It provides both a Proxy Server (LLM Gateway) and a Python SDK, suitable for enterprise platforms and individual projects.

Key Features

• Multi-Provider Support: OpenAI, Anthropic, xAI, VertexAI, NVIDIA, HuggingFace, Azure OpenAI, Ollama, Openrouter, Novita AI, Vercel AI Gateway, etc.

• Unified Output Format: Standardizes responses to OpenAI style (choices[0]["message"]["content"]).

• Retry and Fallback Logic: Ensures reliability across multiple deployments.

• Cost Tracking & Budgeting: Monitor usage and spending per project.

• Observability & Logging: Integrates with Lunary, MLflow, Langfuse, Helicone, Promptlayer, Traceloop, Slack.

• Exception Handling: Maps errors to OpenAI exception types for simplified management.

LiteLLM Proxy Server (LLM Gateway)

The Proxy Server is ideal for centralized management of multiple LLMs, commonly used by Gen AI Enablement and ML Platform teams.

Advantages:

• Unified access to 100+ LLMs

• Centralized usage tracking

• Customizable logging, caching, guardrails

• Load balancing and cost management

LiteLLM Python SDK

For developers, the Python SDK provides a lightweight client interface with full multi-provider support.

Installation:

Basic Usage Example:

from litellm import completion
import os

os.environ["OPENAI_API_KEY"] = "your-api-key"

response = completion(
  model="openai/gpt-4o",
  messages=[{"content": "Hello, how are you?", "role": "user"}]
)

print(response["choices"][0]["message"]["content"])

Streaming Responses:

response = completion(
  model="openai/gpt-4o",
  messages=[{"content": "Hello, how are you?", "role": "user"}],
  stream=True
)

Exception Handling

LiteLLM standardizes exceptions across providers using OpenAI error types:

from openai.error import OpenAIError
from litellm import completion
import os

os.environ["ANTHROPIC_API_KEY"] = "bad-key"

try:
    completion(
        model="claude-instant-1",
        messages=[{"role": "user", "content": "Hey, how's it going?"}]
    )
except OpenAIError as e:
    print(e)

Logging & Observability

LiteLLM supports pre-defined callbacks to log input/output for monitoring and tracking:

import litellm
import os

os.environ["LUNARY_PUBLIC_KEY"] = "your-lunary-public-key"

litellm.success_callback = ["lunary", "mlflow", "langfuse", "helicone"]

response = completion(
    model="gpt-3.5-turbo",
    messages=[{"role": "user", "content": "Hi 👋"}]
)

• Custom callbacks track costs, usage, latency, and other metrics.

2. Helicone AI

Helicone AI Gateway provides a single, OpenAI-compatible API to access 100+ LLMs from multiple providers (GPT, Claude, Gemini, Vertex, Groq, etc.).

It simplifies SDK management by offering one interface, intelligent routing, automatic fallbacks, and unified observability.

Key Features

• Single SDK for All Models: No need to learn multiple provider APIs.

• Intelligent Routing: Automatic fallbacks, load balancing, cost optimization.

• Unified Observability: Track usage, costs, and performance in one dashboard.

• Prompt Management: Deploy and iterate prompts without code changes.

• Security & Access Control: Supports BYOK and passthrough routing.

Quick Integration

Step 1: Set Up Your Keys

1. Sign up for a Helicone account.

2. Generate a Helicone API key.

3. Add LLM provider keys (OpenAI, Anthropic, Vertex, etc.) in Provider Keys.

Step 2: Send Your First Request

JavaScript/TypeScript Example:

import { OpenAI } from "openai";

const client = new OpenAI({
  baseURL: "<https://ai-gateway.helicone.ai>",
  apiKey: process.env.HELICONE_API_KEY,
});

const response = await client.chat.completions.create({
  model: "gpt-4o-mini",
  messages: [{ role: "user", content: "Hello, world!" }],
});

console.log(response.choices[0].message.content);

Python Example:

from openai import OpenAI
import os

os.environ["HELICONE_API_KEY"] = "your-helicone-api-key"

client = OpenAI(api_key=os.environ["HELICONE_API_KEY"])

response = client.chat.completions.create(
    model="gpt-4o-mini",
    messages=[{"role": "user", "content": "Hello, world!"}]
)

print(response.choices[0].message.content)

Notes:

• Existing SDK users can leverage direct provider integrations for logging and observability.

• Switching providers only requires changing the model string no code changes needed.

3. BricksLLM

BricksLLM is a cloud-native AI gateway written in Go, designed to put large language models (LLMs) into production. It provides enterprise-grade infrastructure for managing, securing, and scaling LLM usage across organizations, supporting OpenAI, Anthropic, Azure OpenAI, vLLM, and Deepinfra natively.

A managed version of BricksLLM is also available, featuring a dashboard for easier monitoring and interaction.

Key Features

• User & Organization Controls: Track LLM usage per user/org and set usage limits.

• Security & Privacy: Detect and mask PII, control endpoint access, redact sensitive requests.

• Reliability & Performance: Failovers, retries, caching, rate-limited API key distribution.

• Cost Management: Rate limits, spend limits, cost analytics, and request analytics.

• Access Management: Model-level and endpoint-level access control.

• Integration & Observability: Native support for OpenAI, Anthropic, Azure, vLLM, Deepinfra, custom deployments, and Datadog logging.

Getting Started with BricksLLM-Docker

• Clone the repository:

git clone <https://github.com/bricks-cloud/BricksLLM-Docker>
cd

• Deploy locally with PostgreSQL and Redis:

docker compose up -d

• Create a provider setting:

curl -X PUT <http://localhost:8001/api/provider-settings> \\
  -H "Content-Type: application/json" \\
  -d '{
        "provider":"openai",
        "setting": { "apikey": "YOUR_OPENAI_KEY" }
      }'

• Create a Bricks API key:

curl -X PUT <http://localhost:8001/api/key-management/keys> \\
  -H "Content-Type: application/json" \\
  -d '{
        "name": "My Secret Key",
        "key": "my-secret-key",
        "tags": ["mykey"],
        "settingIds": ["ID_FROM_STEP_THREE"],
        "rateLimitOverTime": 2,
        "rateLimitUnit": "m",
        "costLimitInUsd": 0.25
      }'

• Use the gateway via curl:

curl -X POST <http://localhost:8002/api/providers/openai/v1/chat/completions> \\
  -H "Authorization: Bearer my-secret-key" \\
  -H "Content-Type: application/json" \\
  -d '{
        "model": "gpt-3.5-turbo",
        "messages": [{"role": "system","content": "hi"}]
      }'

Or point your SDK to BricksLLM:

import OpenAI from 'openai';

const openai = new OpenAI({
  apiKey: "my-secret-key",
  baseURL: "<http://localhost:8002/api/providers/openai/v1>"
});

Updates

• Latest version: docker pull luyuanxin1995/bricksllm:latest

• Specific version: docker pull luyuanxin1995/bricksllm:1.4.0

Environment Configuration

BricksLLM uses PostgreSQL and Redis. Key environment variables include:

• PostgreSQL: POSTGRESQL_HOSTS, POSTGRESQL_DB_NAME, POSTGRESQL_USERNAME, POSTGRESQL_PASSWORD

• Redis: REDIS_HOSTS, REDIS_PORT, REDIS_PASSWORD

• Proxy: PROXY_TIMEOUT, NUMBER_OF_EVENT_MESSAGE_CONSUMERS

• AWS keys for PII detection: AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY, AMAZON_REGION

These allow customization for deployment, logging, and security.

4. TensorZero

The TensorZero Gateway is an industrial-grade, Rust-based LLM gateway providing a unified interface for all LLM applications. It combines low-latency performance, structured inferences, observability, experimentation, and GitOps orchestration, ideal for production deployments.

Key Features

One API for All LLMs

Supports major providers:

• Anthropic, AWS Bedrock, AWS SageMaker, Azure OpenAI Service

• Fireworks, GCP Vertex AI Anthropic & Gemini, Google AI Studio (Gemini API)

• Groq, Hyperbolic, Mistral, OpenAI, OpenRouter, Together, vLLM, xAI

• Any OpenAI-compatible API (e.g., Ollama)

New providers can be requested via GitHub.

Blazing-Fast Performance

• Rust-based gateway with <1ms P99 latency overhead under heavy load (10,000 QPS)

• 25–100× lower latency than LiteLLM under high throughput

Structured Inferences & Multi-Step Workflows

• Enforces schemas for inputs/outputs for robustness

• Supports multi-step LLM workflows with episodes, enabling inference-level feedback

Built-In Observability

• Collects structured traces, metrics, and natural-language feedback in ClickHouse

• Enables analytics, optimization, and replay of historical inferences

Experimentation & Fallbacks

• Supports A/B testing between variants

• Automatic fallback to alternate providers or variants for high availability

GitOps-Oriented Orchestration

• Manage prompts, models, parameters, tools, and experiments programmatically

• Supports human-readable configs or fully programmatic orchestration

Getting Started: Python Example

Install the TensorZero client:

Run an LLM inference:

from tensorzero import TensorZeroGateway

# Build embedded client with configuration
with TensorZeroGateway.build_embedded(clickhouse_url="clickhouse://localhost:9000", config_file="config.yaml") as client:

    # Run an LLM inference
    response = client.inference(
        model_name="openai::gpt-4o-mini",
        input={
            "messages": [
                {
                    "role": "user",
                    "content": "Write a haiku about artificial intelligence."
                }
            ]
        }
    )

print(response)

from tensorzero import TensorZeroGateway

# Build embedded client with configuration
with TensorZeroGateway.build_embedded(clickhouse_url="clickhouse://localhost:9000", config_file="config.yaml") as client:

    # Run an LLM inference
    response = client.inference(
        model_name="openai::gpt-4o-mini",  # or "anthropic::claude-3-7-sonnet"
        input={
            "messages": [
                {
                    "role": "user",
                    "content": "Write a haiku about artificial intelligence."
                }
            ]
        }
    )

print(response)

5. Kong AI Gateway

Kong’s AI Gateway allows you to deploy AI infrastructure that routes traffic to one or more LLMs. It provides semantic routing, security, monitoring, acceleration, and governance of AI requests using AI-specific plugins bundled with Kong Gateway.

This guide shows how to set up the AI Proxy plugin with OpenAI using a quick Docker-based deployment.

Prerequisites

1. Kong Konnect Personal Access Token (PAT)

   Generate a token via the Konnect PAT page and export it:

export KONNECT_TOKEN='YOUR_KONNECT_PAT'

1. Run Quickstart Script

   Automatically provisions a Control Plane and Data Plane and configures your environment:

curl -Ls <https://get.konghq.com/quickstart> | bash -s -- -k $KONNECT_TOKEN --deck-output

Set environment variables as prompted:

export DECK_KONNECT_TOKEN=$KONNECT_TOKEN
export DECK_KONNECT_CONTROL_PLANE_NAME=quickstart
export KONNECT_CONTROL_PLANE_URL=https://us.api.konghq.com
export KONNECT_PROXY_URL='<http://localhost:8000>'

Verify Kong Gateway and decK

Check that Kong Gateway is running and accessible via decK:

deck gateway ping

Expected output:

Create a Gateway Service

Define a service for your LLM provider:

echo '
_format_version: "3.0"
services:
  - name: llm-service
    url: <http://localhost:32000>
' | deck gateway apply -

The URL can be any placeholder; the plugin handles routing.

Create a Route

Create a route for your chat endpoint:

echo '
_format_version: "3.0"
routes:
  - name: openai-chat
    service:
      name: llm-service
    paths:
    - "/chat"
' | deck gateway apply -

Enable the AI Proxy Plugin

Enable the AI Proxy plugin for the route:

echo '
_format_version: "3.0"
plugins:
  - name: ai-proxy
    config:
      route_type: llm/v1/chat
      model:
        provider: openai
' | deck gateway apply -

Notes:

• Clients must include the model name in the request body.

• Clients must provide an OpenAI API key in the Authorization header.

• Optionally, you can embed the OpenAI API key directly in the plugin configuration (config.auth.header_name and config.auth.header_value).

Validate the Setup

Send a test POST request to the /chat endpoint:

curl -X POST "$KONNECT_PROXY_URL/chat" \\
     -H "Accept: application/json" \\
     -H "Content-Type: application/json" \\
     -H "Authorization: Bearer $OPENAI_KEY" \\
     --json '{
       "model": "gpt-4",
       "messages": [
         {
           "role": "user",
           "content": "Say this is a test!"
         }
       ]
     }'

Expected outcome:

• HTTP 200 OK

• Response body contains the model’s reply, e.g., "This is a test."

Comparison :

Conclusion:

We explored in this article the major LLM APIs and gateways (LiteLLM, Helicone, BricksLLM, TensorZero, Bifrost, and Kong). We highlighting their strengths, use cases, and setup processes. Gateways simplify multi-model management, observability, cost control, and enterprise-grade deployment. Choosing the right solution depends on whether your priority is quick integration, production reliability, low-latency workflows, or governed routing. By understanding these platforms as detailed above, teams can design AI infrastructure that is scalable, efficient, and easy to maintain.

FAQ

What is an LLM Gateway?

An LLM Gateway is a tool that connects your app to different AI model providers. It makes it easier to send requests, switch providers, and keep things running smoothly.

Which LLM Gateway is best for beginners or small projects?

LiteLLM and Bifrost are the easiest to start with. LiteLLM works with a simple Python SDK, while Bifrost runs with almost no setup and gives you a web dashboard.

Which LLM Gateway is best for big companies?

BricksLLM and Kong are built for larger teams. They focus on security, access control, and detailed analytics that enterprises usually need.

Which LLM Gateway is the fastest?

TensorZero is made for speed. It’s built in Rust and adds less than a millisecond of delay, making it great for real-time or large-scale systems.

Do LLM Gateways help with monitoring and tracking?

Yes. Some have dashboards to track usage and costs (like Helicone), others work with popular tools like Datadog (BricksLLM), or provide detailed logs (TensorZero).

Do they handle errors automatically?

Most gateways include retries and fallbacks. For example, Helicone and TensorZero can switch providers if one fails, and LiteLLM makes errors look the same across providers.