# Top LLM Gateways 2025 We compare and test the top LLM gateways in 2025. These includes Litellm, Helicone, BricksLLM and Kong AI Gateway. Canonical HTML page: - Category: Engineering - Published: Sep 30, 2025 - Author: Ilyes Rezgui - Tags: Comparison 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](https://platform.openai.com/), 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: ```javascript 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: ```javascript [ { "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**](https://docs.claude.com/en/home) use the [**Messages API**](https://docs.claude.com/en/api/messages), 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) ```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:** ```javascript { "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](https://console.groq.com/home) 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 ` - **Hugging Face API (optional):** `provider="groq"` with `HF_TOKEN` #### Example via Hugging Face ```python 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:** ```javascript { "choices": [ { "message": { "role": "assistant", "content": "The capital of France is Paris." } } ] } ``` ### Comparison Between APIs
Feature / ProviderOpenAIAnthropicGroq (via HF or Direct)
Primary AccessResponses APIMessages APIDirect API / HF InferenceClient
Prompt PatternFreeform / JSON / Tool / RolesMessages (role-based)Chat completion
Structured OutputJSON Schema / Tool CallsText only (list of messages)Text only
Tool / Function CallingSupportedNot nativeNot native
Auth MethodAPI keyx-api-keyAPI key (direct) / HF token
Max Request SizeVaries (MB)Standard 32 MB, Batch 256 MBVaries (HF or direct)
Special NotesReusable prompts, rolesRich metadata in headersUltra-fast inference, deterministic latency
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](https://www.litellm.ai/) 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. ![1. LiteLLM](/blog/top-llm-gateways/img-1.webp) #### 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:** ```bash pip install litellm ``` **Basic Usage Example:** ```python 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:** ```python 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: ```python 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: ```python 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](https://www.helicone.ai/) 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. ![2. Helicone AI](/blog/top-llm-gateways/img-2.webp) #### 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:** ```javascript import { OpenAI } from "openai"; const client = new OpenAI({ baseURL: "", 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:** ```python 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](https://github.com/bricks-cloud/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. ![3. BricksLLM](/blog/top-llm-gateways/img-3.webp) #### 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:** ```bash git clone cd BricksLLM-Docker ``` - **Deploy locally with PostgreSQL and Redis:** ```bash docker compose up -d ``` - **Create a provider setting:** ```bash curl -X PUT \\ -H "Content-Type: application/json" \\ -d '{ "provider":"openai", "setting": { "apikey": "YOUR_OPENAI_KEY" } }' ``` - **Create a Bricks API key:** ```bash curl -X PUT \\ -H "Content-Type: application/json" \\ -d '{ "name": "My Secret Key", "key": "", "tags": ["mykey"], "settingIds": ["ID_FROM_STEP_THREE"], "rateLimitOverTime": 2, "rateLimitUnit": "m", "costLimitInUsd": 0.25 }' ``` - **Use the gateway via curl:** ```bash curl -X POST \\ -H "Authorization: Bearer " \\ -H "Content-Type: application/json" \\ -d '{ "model": "gpt-3.5-turbo", "messages": [{"role": "system","content": "hi"}] }' ``` Or point your SDK to BricksLLM: ```javascript import OpenAI from 'openai'; const openai = new OpenAI({ apiKey: "", baseURL: "" }); ``` #### 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**](https://www.tensorzero.com/docs/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. ![4. TensorZero](/blog/top-llm-gateways/img-4.webp) #### 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:** ```bash pip install tensorzero ``` **Run an LLM inference:** ```python 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) ``` ```python 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](https://konghq.com/products/kong-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. ![5. Kong AI Gateway](/blog/top-llm-gateways/img-5.webp) #### Prerequisites 1. **Kong Konnect Personal Access Token (PAT)** Generate a token via the Konnect PAT page and export it: ```bash export KONNECT_TOKEN='YOUR_KONNECT_PAT' ``` 1. **Run Quickstart Script** Automatically provisions a Control Plane and Data Plane and configures your environment: ```bash curl -Ls | bash -s -- -k $KONNECT_TOKEN --deck-output ``` Set environment variables as prompted: ```bash export DECK_KONNECT_TOKEN=$KONNECT_TOKEN export DECK_KONNECT_CONTROL_PLANE_NAME=quickstart export KONNECT_CONTROL_PLANE_URL=https://us.api.konghq.com export KONNECT_PROXY_URL='' ``` #### Verify Kong Gateway and decK Check that Kong Gateway is running and accessible via **decK**: ```bash deck gateway ping ``` Expected output: ```markdown Successfully Konnected to the Kong organization! ``` #### Create a Gateway Service Define a service for your LLM provider: ```bash echo ' _format_version: "3.0" services: - name: llm-service url: ' | deck gateway apply - ``` > The URL can be any placeholder; the plugin handles routing. #### Create a Route Create a route for your chat endpoint: ```bash 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: ```bash 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: ```bash 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 :
Feature / GatewayLiteLLMHeliconeBricksLLMTensorZeroBifrostKong
Primary FocusMulti-provider LLM access with Python SDK & ProxyUnified OpenAI-compatible API for 100+ LLMsEnterprise-grade production LLM gatewayIndustrial-grade Rust-based gateway for low-latency, structured workflowsZero-config OpenAI-compatible gatewayAI Gateway for routing & governance with plugins
Supported ProvidersOpenAI, Anthropic, xAI, VertexAI, NVIDIA, HuggingFace, Azure, Ollama, Novita AI, Vercel AI Gateway, othersGPT, Claude, Gemini, Vertex, Groq, othersOpenAI, Anthropic, Azure OpenAI, vLLM, Deepinfra, custom deploymentsAnthropic, AWS Bedrock & SageMaker, Azure OpenAI, Fireworks, Vertex AI, Groq, Mistral, OpenAI, OpenRouter, Together, xAIOpenAI, Anthropic, Mistral, Azure OpenAI, AWS Bedrock, Google Vertex, othersOpenAI, Anthropic, Mistral, Azure OpenAI, AWS Bedrock, Google Vertex, others
DeploymentProxy server / Python SDKCloud / APIDocker / Local / ManagedRust-based Gateway / Python SDKLocal / DockerKong Gateway (Docker / Konnect)
Latency / PerformanceStandard cloud latencyStandard cloud latencyProduction-grade, caching & failover<1ms P99 latency overhead, high throughputVery fast, zero-configStandard HTTP gateway, plugin-based routing
Observability & LoggingIntegrates with Lunary, MLflow, Langfuse, Helicone, Promptlayer, Traceloop, SlackUnified dashboard for usage, cost, and performanceDatadog integration, analytics, request loggingClickHouse traces, metrics, structured loggingWeb UI: live metrics, request logsdecK CLI, Kong dashboard, plugin metrics
Error / Exception HandlingUnified OpenAI-style errors across providersAutomatic fallbacks, unified loggingRate-limited, retries, PII masking, access controlAutomatic fallbacks, multi-step workflow safetyAutomatic retries, network & API key handlingConfigurable retries, network, and header settings
Structured / Multi-step SupportJSON outputs, function callsBasic text responsesSupports structured inputs via APISchemas, multi-step workflows, episodesSupports multiple providers but mostly freeform textSupports routing to structured endpoints via plugin
Access Control & SecurityAPI key management, cost trackingBYOK support, passthrough routingUser/org-level quotas, PII masking, access controlGitOps orchestration, model & endpoint controlVirtual keys, usage budgetsAPI key in Authorization header, optional embedded keys
Programming InterfacesPython SDK, REST APIREST API (OpenAI-compatible)REST API, cURL, SDKsPython SDK / Rust gatewayREST API / UIREST API, decK YAML, plugin config
Ease of Use / SetupEasy Python SDK integration, Proxy serverSingle API for all models, minimal code changesRequires Docker / managed deployment, enterprise setupRequires Rust gateway and Python SDKZero-config, local Docker, web UIRequires Kong setup, decK configuration, plugin management
Use Case FocusDevelopers / ML teamsDevelopers who want single API interfaceEnterprises needing governance & analyticsProduction-grade AI pipelines / structured workflowsFast prototyping, lightweight dev integrationEnterprise AI deployment with governance & routing
## 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. --- ## Machine-readable - [Sitemap](https://agenta.ai/sitemap-index.xml) - [llms.txt](https://agenta.ai/llms.txt) - [OpenAPI specification](https://agenta.ai/openapi.json) - [Documentation](https://docs.agenta.ai) Every page on https://agenta.ai serves this representation when requested with `Accept: text/markdown`.