Top 5 AI Gateways for OpenAI: OpenRouter Alternatives
The increasing number of LLM providers complicates API management. AI gateways simplify this by serving as a unified access point, allowing developers to interact with multiple providers through a single API.
We benchmarked OpenRouter, SambaNova, TogetherAI, Groq, and AI/ML API as AI gateways since they provide unified API access to multiple models.
However, Groq and SambaNova are primarily AI providers with proprietary hardware, while TogetherAI functions as both an AI provider and a hardware vendor. OpenRouter and AI/ML API are pure gateways, routing to external providers without hosting models themselves.
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/providers performance benchmark
In this benchmark, we compared 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
We analyzed First Token Latency (FTL) because this metric directly reflects how effectively a gateway selects the appropriate provider and delivers the initial portion 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 deliver top-tier performance across different scenarios, with Groq having a slight edge on 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
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, since token counts can vary significantly across long prompts.
- Despite generating the highest number of tokens (1,997), SambaNova maintains strong latency performance, ranking second-fastest with a response time of 3 seconds.
- 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, ranking as the slowest AI gateway at 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 first-token latency.
- TogetherAI performed better than OpenRouter, though their performance was relatively close.
- The AI/ML API, with 0.90 seconds, was the slowest, consistent with its performance in the first token latency measurement.
Cost comparison
You can see the cost comparison for the Llama 4 Scout (17Bx16E) model with 1 million output/input tokens.
You can read more about LLM pricing.
Prepare your API request with our tool
Use the tool below to prepare your OpenAI-compatible API request for any of the models provided by AI gateways.
Supported model counts
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, and performance, with features such as automatic failovers, prompt caching, and standardized request formats, eliminating the need to manage multiple provider APIs.
Developers can switch between different models without code changes, enhancing flexibility and reliability.
Figure 1: OpenRouter dashboard: AI model comparison interface with multiple models, search functionality, and conversation history.1
AI/ML API
AI/ML API provides a unified interface for sending requests to multiple LLMs, streamlining integration for tasks such as text generation and embeddings.
Its standardized interface supports multiple models, enabling developers to send requests without dealing with provider-specific complexities.
The API abstracts infrastructure management, enabling efficient, scalable access to AI models with consistent request formats for rapid development.
Figure 2: AI/ML API playground: LLM testing interface with adjustable parameters, model selection, and sample conversation.2
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.
Figure 3: Together AI interface: LLM playground featuring Llama model selection, adjustable parameters, and detailed response metrics.3
Groq
Groq, developed by Groq Inc., is an AI gateway that provides a unified API for sending requests to large language models (LLMs) such as 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.
Figure 4: Groq interface: LLM testing platform with Llama model, adjustable parameters, and response performance metrics.4
SambaNova
SambaNova’s unified API, accessible via platforms like Portkey, enables sending requests to high-performance LLMs such as Llama 3.1 405B, leveraging its custom Reconfigurable Dataflow Units to process 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.
Figure 5: SambaNova playground: DeepSeek model interface with reasoning capabilities and detailed performance metrics.5
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 such as model deployment, handling 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 provide a structured approach to integrating and managing multiple AI models and services. They act as a control layer between applications and AI providers, improving efficiency, consistency, and governance across the AI lifecycle.
Centralized model management
An AI gateway enables organizations to manage connections to multiple AI providers through a single interface. This reduces the need for maintaining separate integrations and simplifies version control, monitoring, and auditing of models.
Faster deployment and updates
With unified access and configuration, developers can deploy new models or update existing ones without significant code changes. This supports faster implementation and shortens development cycles.
Reliability and scalability
AI gateways distribute requests across available resources, helping maintain consistent performance as usage increases. Load balancing and automated failover minimize downtime and ensure service continuity.
Integration with CI/CD processes
Linking AI gateways with CI/CD pipelines allows organizations to automate model testing, validation, and deployment. This supports continuous improvement while maintaining stability and compliance.
Security and access control
Gateways consolidate authentication, encryption, and usage monitoring into a single layer. This reduces exposure to security risks and ensures compliance with internal and external data protection policies.
Performance and cost optimization
By tracking performance metrics and usage patterns, an AI gateway can direct traffic to the most efficient or cost-effective model. This helps balance performance requirements with budget constraints.
For example, AI gateways such as Portkey and Gantry provide these capabilities by allowing teams to connect to various large language model (LLM) providers through a single API. They help standardize access, monitor performance, and manage updates efficiently.
How does an AI Gateway ensure enhanced security architecture?
AI Gateways provide an advanced 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
Kong AI Gateway (See Figure 6) functions as a middleware layer that connects applications and agents to AI providers such as OpenAI, Anthropic, and LLaMA, as well as vector databases such as Pinecone and Qdrant.
It provides a unified API interface compatible with OpenAI, allowing developers to access multiple large language models (LLMs) through a single integration. This design reduces complexity and improves consistency across AI interactions.
The gateway includes several features that improve system performance and efficiency:
- AI semantic caching to store and reuse responses, reducing latency.
- AI traffic control and load balancing to manage request distribution and maintain stable performance.
- AI Retries to handle transient errors and improve reliability.
Security is built into the core architecture. Kong AI Gateway includes AI prompt guard to detect and block prompt injection attacks, authentication and authorization (AuthNZ) for controlled access, and data encryption to meet enterprise compliance standards.
In addition to these capabilities, the gateway provides:
- AI observability tools for monitoring performance and usage,
- AI flow and transformation features for managing input and output data,
- Deployment options across multi-cloud, on-premises, and hybrid environments.
These capabilities make it suitable for organizations that handle large-scale AI workloads.
Figure 6: Advanced Kong AI Gateway architecture: Unified API interface connecting AI providers (LLMs and vector DBs) with apps and agents through security, governance, and observability plugins.6
Learn more about advanced LLMOps platforms, such as Kong AI.
What is the difference between AI Gateways and AI Providers?
AI Providers are platforms that host and serve AI models through their own infrastructure. They handle the technical aspects like compute resources, model deployment, APIs, autoscaling, and monitoring. Examples include Baseten, Groq (with its proprietary LPU hardware), and SambaNova (with RDU infrastructure).
AI Gateways act as middleware that sits between your applications and multiple AI providers. Instead of connecting to each provider separately, gateways offer a unified API to access many models through a single interface, handling intelligent routing, load balancing, security, and cost optimization. Examples include OpenRouter and AI/ML API.
Some platforms like TogetherAI function as both. They host their own models (provider functionality) while also offering unified API access to multiple external models (gateway functionality).
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 in which the first-token latency could be measured were included in the final analysis to maintain 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 across variations of the same model was 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 differences in model variation and enabled a fairer, more consistent comparison across providers.
The benchmarking script used stream = True mode to measure the time to the first token and capture the full response generation time. The 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 runs.
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 the AI gateway
Reference Links
Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE and NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and resources that referenced AIMultiple.
Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised enterprises on their technology decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.
He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.
Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.
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