The growing number of LLM providers creates significant API management hurdles. AI gateways address this complexity by acting as a central routing point, enabling developers to interact with multiple providers through a single, unified API, thereby simplifying development and maintenance. If you plan to use one of these AI gateways, you can:
- compare the efficiency of AI gateways with our benchmarks
- compare the pricing of services with the tool below
- prepare your OpenAI-compatible API request with our tool
AI gateway performance benchmark
In this benchmark, we compared the infrastructure performance of five major AI gateways. OpenRouter, SambaNova, TogetherAI, Groq, and the AI/ML API using the Llama 3.1 8B model. Since each gateway offers different variants of the Llama 3.1 8B model (such as Instruct, Turbo, and Instant), we applied a normalization strategy to ensure these variations did not affect the performance comparison.
You can see our methodology here.
First token latency comparison of AI gateways
We analyzed First Token Latency (FTL) across AI gateways because this metric directly reflects how effectively a gateway selects the right provider and delivers the first part of the response to the user. It provides a clear indication of real-world performance and user experience. Additionally, FTL showcases the efficiency of an AI gateway’s infrastructure resource management and network optimization.
Groq and SambaNova demonstrate the lowest FTL values, indicating highly optimized and fast infrastructures. For short prompts, both SambaNova and Groq deliver responses in just 0.13 seconds, making them the fastest. For long prompts, Groq takes the lead with 0.14 seconds, slightly outperforming SambaNova. This shows that both providers offer top-tier performance in different scenarios, with Groq having a slight edge in longer prompts, though overall, their performance is very close and consistently strong.
OpenRouter and TogetherAI show moderate performance, with FTLs of 0.40 and 0.43 seconds respectively for short prompts, and 0.45 seconds for both in long prompts. Their results are quite similar, though OpenRouter is slightly faster, especially noticeable in short prompts.
In contrast, the AI/ML API shows the highest latency, with 0.84 seconds for short prompts and 0.90 seconds for long prompts, making it significantly slower than the other providers.
Token and latency performance comparison of AI gateways
Next, we examined the number of output tokens and latency values to understand how well AI gateways select the appropriate provider and maintain the user experience. These metrics reflect the overall efficiency of the entire response process. Within this context, we also evaluated the gateways’ ability to choose the most efficient and fastest provider optimization during the benchmark.
We wanted to examine how AI gateways handle optimization given that token counts can vary significantly for long prompts.
Despite generating the highest number of tokens (1,997 tokens), SambaNova maintains strong latency performance, ranking as the second fastest with a response time of 3 seconds. On the other hand, Groq is about 1 second faster than SambaNova (2.7 seconds) but produces slightly fewer tokens (1,900).
Although using fewer tokens than both SambaNova and Groq (1,812 for TogetherAI and 1,880 for AI/ML API), TogetherAI and AI/ML API have considerably higher latency (11 seconds and 13 seconds respectively) making them significantly slower. OpenRouter, which produces the same number of tokens as TogetherAI, shows moderate latency performance for it emerges as the slowest AI gateway with a latency of 25 seconds.
Since the token count is the same across all providers for short prompts, our comparison focused entirely on latency:
In this case, Groq and SambaNova are nearly identical and the fastest in terms of first token latency. TogetherAI performed better than OpenRouter, though their performance was relatively close. AI/ML API, with 0.90 seconds, was the slowest, consistent with its performance in the first token latency measurement as well.
Cost comparison of AI gateways
You can enter the token count information and see the cost comparison of the gateways for Llama 4 Scout (17Bx16E) model.
You can read more about LLM pricing.
Prepare your API request with our tool
You can use the tool below to prepare your OpenAI-compatible API request for any of the models provided by AI gateways.
Supported model counts
| AI gateway | Supported model count |
|---|---|
| OpenRouter | 301 |
| AI/ML API | 196 |
| Together AI | 92 |
| Groq | 19 |
| SambaNova | 14 |
Top AI gateways analyzed
OpenRouter
OpenRouter’s unified API simplifies sending requests to large language models (LLMs) by providing a single, OpenAI-compatible endpoint to access over 300 models from providers like Anthropic, Google, and Grok. It intelligently routes requests to optimize cost, latency, or performance, with features like automatic failovers, prompt caching, and standardized request formats, eliminating the need to manage multiple provider APIs. Developers can seamlessly switch between different models without code changes, enhancing flexibility and reliability.
AI/ML API
AI/ML API offers a unified API for sending requests to various LLMs, streamlining integration for tasks like text generation and embeddings. Its standardized interface supports multiple models, allowing developers to send requests without handling provider-specific complexities. The API abstracts infrastructure management, enabling efficient, scalable access to AI models with consistent request formats for rapid development.
Together AI
Together AI’s unified API enables sending requests to over 50 open-source LLMs with a single interface, supporting high-performance inference and sub-100ms latency. It handles token caching, model quantization, and load balancing, allowing developers to send requests without managing infrastructure. The API’s flexibility supports easy model switching and parallel requests, optimized for speed and cost.
Groq
Groq, developed by Groq Inc., is an innovative AI gateway offering a unified API for sending requests to large language models (LLMs) like Llama 3.1. It leverages custom-designed Language Processing Units (LPUs) to deliver high-speed, low-latency responses. With an OpenAI-compatible API, it provides developers with flexibility, though it operates solely over HTTP without WebSocket support.
SambaNova
SambaNova’s unified API, accessible via platforms like Portkey, facilitates sending requests to high-performance LLMs like Llama 3.1 405B, leveraging its custom Reconfigurable Dataflow Units for up to 200 tokens per second. The API standardizes requests for enterprise-grade models, ensuring low-latency, high-throughput processing with seamless integration, ideal for complex AI workloads.
What is the role of an AI gateway in AI application development?
AI Gateways serve as a centralized platform that connects AI models, services, and data to end-user applications. They facilitate seamless integration by providing standardized APIs, often OpenAI-compatible, to interact with multiple AI providers (e.g., OpenAI, Anthropic, or Google). This reduces the need to manage provider-specific APIs, handles tasks like load balancing and caching, and ensures efficient operation, allowing developers to prioritize application logic over infrastructure management.
How does an AI gateway differ from a traditional API gateway?
A traditional API Gateway serves as a single entry point for client requests to backend services, managing and securing API traffic. In contrast, an AI Gateway is tailored for AI models and services, addressing specific challenges like model deployment, large data volumes, and performance monitoring. AI Gateways offer advanced features such as semantic caching, prompt management, and AI-specific traffic management, ensuring compliance with security and regulatory standards, unlike general-purpose API Gateways.
What are the key benefits of using an AI gateway for AI integration?
AI Gateways significantly enhance AI integration by:
- Centralizing and automating AI model deployment and management, reducing complexity.
- Accelerating time-to-market, enabling businesses to adapt quickly to market changes.
- Ensuring reliability and scalability through automated resource management and load balancing.
- Seamlessly integrating with CI/CD pipelines for continuous model updates and improved productivity.
- Providing a secure and scalable platform to minimize downtime and optimize performance.
For example, gateways like OpenRouter and Kong AI Gateway simplify multi-model access while ensuring enterprise-grade reliability.
How does an AI Gateway ensure enhanced security architecture?
AI Gateways provide a robust security architecture through:
- Data encryption, access control, and authentication to protect sensitive data.
- Role-based access control to manage permissions for AI models and services.
- A single point of control for authenticating and authorizing AI traffic.
- Support for virtual keys to securely manage AI models and services.
- Prompt security features to prevent misuse, like prompt injection attacks.
These measures ensure compliance and safeguard AI applications in enterprise settings.
What deployment options are available for AI Gateways?
AI Gateways offer flexible deployment options, including:
- On-premises, cloud, or hybrid environments to suit organizational needs.
- Support for containerization and serverless architectures for scalability.
- Integration with existing security infrastructure for seamless and secure deployment.
- Automated deployment and scaling to ensure high availability and performance.
- A self-service portal for developers to easily deploy and manage AI models.
For instance, Kong AI Gateway supports multi-cloud and on-premises deployments, enhancing flexibility.
More advanced AI Gateways
As depicted in the architecture diagram below, Kong AI Gateway serves as a powerful middleware platform bridging Apps & Agents with AI Providers (e.g., OpenAI, Anthropic, LLaMA) and Vector DBs (e.g., Pinecone, Qdrant). It offers an OpenAI-compatible unified API interface, streamlining access to multiple LLM providers while abstracting complexities. The gateway enhances performance through features like AI Semantic Caching, AI Traffic Control, Load Balancing, and AI Retries, ensuring low latency and optimized operations.
Security is a priority, with AI Prompt Guard to prevent prompt injection attacks, AuthNZ for access control, and data encryption for compliance in enterprise settings. Additionally, Kong AI Gateway provides AI Observability, AI Flow & Transformations, and flexible deployment options across multi-cloud, on-premises, and hybrid environments, making it a scalable and reliable choice for complex AI workloads.
You can read more about advanced LLMOps platforms like Kong AI.
Benchmark methodology
To evaluate the latency and performance of various AI gateways under consistent and controlled conditions, a Python-based benchmark was developed. The benchmark focused on three key performance indicators: first token latency, total latency and output token count. Each test was executed 50 times per AI gateway to ensure statistical reliability. Only successful runs where first-token latency could be measured were included in the final analysis to maintain result accuracy.
Two prompt types were used to simulate different load scenarios:
- Short prompts, averaging approximately 18 input tokens
- Long prompts, averaging approximately 203 input tokens
The long prompt consisted of a detailed analytical request, structured around eight thematic areas related to recent AI advancements. This ensured that all models were evaluated on both low and high-complexity tasks.
All tests were conducted using the Llama-3.1-8B model across each AI gateway. Although the model name was the same, the gateways used different variations of the model. These differences were carefully taken into account, and the results were normalized accordingly. We identified that the primary source of latency differences between variations of the same model was due to differences in inference-level optimizations. Therefore, during comparisons, we focused solely on the impact of these inference optimizations. This approach helped minimize deviations caused by model variation differences and enabled a fairer and more consistent comparison across providers.
The benchmarking script used stream = True mode to measure the time until the first token was received and to capture the full response generation time. Temperature parameter was fixed at 0.7 across all runs to ensure consistency in response variability. To avoid rate-limiting or load-based performance interference, a 0.5-second delay was applied between each run.
All test executions were monitored for potential failures, including non-200 HTTP responses, timeouts, and incomplete or malformed outputs. Only successful responses with valid first-token latency measurements were included in the aggregated results. Failed runs were excluded to maintain accuracy and consistency in reported metrics.
FAQ about AI gateway
What is an AI Gateway?
An AI Gateway is a middleware platform that simplifies the integration, management, and deployment of AI models and services within an organization’s infrastructure. It acts as a bridge between AI systems (such as large language models or LLMs) and end-user applications, providing a centralized environment to streamline access, optimize performance, and ensure scalability. By abstracting the complexities of AI infrastructure, AI Gateways enable developers to focus on building applications rather than managing underlying systems.
What AI Services Can an AI Gateway Unlock for You?
AI Gateways open the door to a wide range of AI services by providing a unified interface to interact with multiple large language models (LLMs) and AI providers. For example, platforms like OpenRouter allow access to over 300 models from providers such as Anthropic and Google, enabling services like text generation, embeddings, and more. Features like prompt caching and standardized APIs simplify the process, letting developers leverage diverse AI capabilities (such as natural language processing or semantic search) without juggling multiple provider-specific integrations.
How Can an AI Gateway Improve Cost Management?
AI Gateways enhance cost management by optimizing resource usage and reducing operational overhead. They intelligently route requests to the most cost-effective models based on performance and pricing, as seen with Together AI’s load balancing and token caching. This minimizes redundant processing and lowers API call expenses. Additionally, gateways like SambaNova streamline infrastructure management, reducing the need for extensive in-house resources, which helps organizations save on maintenance and scaling costs while maintaining high performance.
Comments
Your email address will not be published. All fields are required.