AI Gateways for OpenAI: OpenRouter Alternatives (original) (raw)

We benchmarked OpenRouter, SambaNova, TogetherAI, Groq, and AI/ML API across three indicators (first-token latency, total latency, and output-token count), with 300 tests using short prompts (approx. 18 tokens) and long prompts (approx. 203 tokens) for total latency.

If you plan to use one of these AI gateways, you can:

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AI gateway/providers performance benchmark

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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.

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.

You can see our methodology.

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 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 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.

Factors explaining the performance differences observed in the benchmark

Differences in infrastructure ownership and hardware design

• Groq and SambaNova operate on proprietary, purpose-built hardware (LPUs and RDUs), which is explicitly optimized for low-latency inference.
• This architectural advantage explains their consistently superior first-token latency and total latency, especially under both short and long prompt conditions.
• In contrast, pure gateways such as OpenRouter and AI/ML API rely on routing requests to external providers, introducing additional network hops and coordination overhead.

Provider vs. gateway role distinction

Performance differences are strongly influenced by whether a platform is:

• A model provider with direct control over inference infrastructure (Groq, SambaNova),
• A hybrid provider–gateway (TogetherAI),
• Or a pure routing gateway (OpenRouter, AI/ML API).

Providers and hybrid platforms can tightly optimize inference, batching, and caching, whereas pure gateways trade some performance for flexibility and broader provider support.

Inference-level optimizations

Despite using the same base model (Llama 3.1 8B), gateways differ in:

• Kernel-level optimizations,
• Token streaming efficiency,
• Scheduling and load-balancing strategies.

These inference-level differences are identified in the methodology as the primary source of latency variation, rather than model architecture itself.

First-token latency sensitivity

First-token latency reflects:

• Network routing efficiency,
• Provider selection logic,
• Internal queueing and resource availability.

Groq and SambaNova’s near-identical, minimal first-token latency indicates highly optimized request pipelines.

Higher first-token latency for AI/ML API and OpenRouter suggests greater overhead in provider selection and request forwarding.

Throughput versus latency trade-offs

• SambaNova achieves the highest token output while maintaining low latency, indicating strong throughput optimization.
• Groq achieves slightly lower token counts but delivers faster total latency, reflecting a design optimized for speed over verbosity.
• TogetherAI and AI/ML API generate fewer tokens yet exhibit higher latency, implying less efficient throughput-to-latency ratios.

Gateway optimization and routing strategy

OpenRouter prioritizes:

• Model diversity,
• Failover resilience,
• Cost and availability optimization.

These design goals increase routing and decision-making overhead, contributing to its higher total latency despite moderate first-token latency.

The benchmark, therefore, captures a deliberate trade-off between flexibility and raw performance.

Model availability breadth and operational complexity

Gateways supporting a large number of models (e.g., OpenRouter with 500+ models) face:

• Increased routing logic complexity,
• More heterogeneous backend performance profiles.

Platforms with fewer supported models can apply more aggressive, model-specific optimizations, improving latency consistency.

Benchmark design effects

The use of:

• Streaming mode,
• Fixed temperature,
• Sequential execution with delay,

Ensures fairness while also highlighting system-level efficiency differences rather than peak-throughput scenarios.

Excluding failed runs favors platforms with stable streaming behavior, indirectly penalizing gateways with higher coordination complexity.

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.

Use the tool below to prepare your OpenAI-compatible API request for any of the models provided by AI gateways.

Supported model counts

Top AI gateways

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 200 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.

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.3

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.4

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.

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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.

What are the downsides of using an AI gateway?

While AI gateways simplify access to multiple models and providers, they also introduce trade-offs that organizations should weigh before adoption. These limitations affect performance, cost, and operational complexity, and may outweigh the benefits in certain scenarios.

Added latency from routing overhead

Every request passing through a gateway involves additional network hops and processing logic before reaching the underlying model provider.

• Pure routing gateways such as OpenRouter and AI/ML APIs show higher first-token latency than providers running on proprietary inference hardware (Groq, SambaNova) in our benchmark, with the AI/ML API the slowest at 0.84-0.90 seconds.
• The overhead becomes more noticeable in latency-sensitive applications such as real-time chat, voice assistants, or agentic workflows with multiple sequential calls.
• Applications that prioritize sub-second response times may find direct integration with a single provider more efficient than routing through a gateway.

Additional point of failure

Introducing a gateway adds another layer to the request path, which can affect overall system reliability.

• If the gateway experiences downtime, rate limiting, or degraded performance, all downstream AI calls are affected, even when the underlying providers remain available.
• Debugging becomes more complex because failures can originate from the gateway, routing logic, or the selected provider, making root-cause analysis harder.
• Organizations relying on a single gateway essentially shift their dependency from one provider to another, without fully eliminating vendor risk.

Cost markup and pricing opacity

Most gateways operate on a markup or subscription model, which can offset the cost savings they advertise.

• Pure gateways often pass through provider costs with an added margin, meaning per-token pricing may be higher than going directly to the provider.
• Enterprise-focused gateways such as Kong AI Gateway typically require annual licensing fees, which may be significant for smaller teams.
• Pricing structures are not always transparent, making it difficult to predict monthly costs at scale.

Vendor lock-in at the gateway layer

While AI gateways are often marketed as a way to avoid lock-in with model providers, they can introduce a new form of dependency.

• Custom features such as semantic caching, prompt management, or proprietary routing logic are not portable across gateways.
• Migrating away from a gateway later requires re-implementing observability, security policies, and routing rules, which can be time-consuming.
• Standardized OpenAI-compatible APIs reduce this risk somewhat, but advanced gateway features remain proprietary.

Limited access to provider-specific features

Gateways standardize requests across providers, but this abstraction can hide capabilities unique to individual models.

• Provider-specific parameters, response formats, or beta features may not be exposed through the gateway’s unified API.
• Newly released models or capabilities often appear on gateways with a delay, as the gateway must update its integration first.
• Teams that depend on cutting-edge features (such as extended context windows, structured outputs, or multimodal inputs) may find direct provider access more flexible.

Operational complexity for smaller teams

For small teams or early-stage projects, a gateway can add more complexity than it removes.

• Configuring routing rules, fallbacks, observability, and access controls requires upfront engineering effort.
• A simple wrapper around a single provider’s SDK may be sufficient for prototypes or applications with low traffic volumes.
• The benefits of gateways become more meaningful at scale, where managing multiple providers, monitoring costs, and enforcing governance justify the added overhead.

For example, a startup serving a few thousand requests per day with one model may find that direct integration with OpenAI or Anthropic is faster to set up and easier to maintain than configuring a full gateway stack.

More advanced AI Gateways

Kong AI Gateway

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: Kong AI Gateway architecture: Unified API interface connecting AI providers (LLMs and vector DBs) with apps and agents through security, governance, and observability plugins.5

Learn more about advanced LLMOps platforms, such as Kong AI.

Envoy AI Gateway

Envoy AI Gateway is an open-source gateway built on Envoy Proxy for managing and routing traffic to large language model providers. It provides a centralized control plane for invoking AI models via standardized APIs, supporting multiple providers and deployment environments.

The gateway is designed to integrate with Kubernetes and the Gateway API, and to expose OpenAI-compatible and Responses-compatible endpoints to applications while handling provider-specific differences internally.

Key features include:

API & provider support:

• Support for OpenAI Responses API (/v1/responses), including streaming, tool calls, multimodal inputs, and reasoning
• Compatibility with OpenAI-style APIs across providers (e.g., Anthropic, Gemini, Cohere, Bedrock)
• Configurable endpoint prefixes for providers with non-standard OpenAI-compatible paths

Configuration & routing

• GatewayConfig CRD for gateway-scoped configuration shared across multiple gateways
• Route-level request body mutation for backend-specific parameter handling
• Inference pools for dynamic backend selection with consistent security policies

Security & access control

• CEL-based authorization for MCP routes
• Authorization using request attributes, JWT claims, and external authorization services
• Tool-level access control for MCP-based integrations

Caching & cost controls

• Prompt caching support for Claude models on AWS Bedrock and GCP Vertex AI
• Separate accounting for cached input tokens and cache creation tokens

Agent & tooling support

• Native support for Model Context Protocol (MCP) servers and tools
• Automatic tool list synchronization for MCP clients
• Proxying of stdio-based MCP servers

Grounding & retrieval

• Google Search grounding for Gemini models
• Enterprise search integration for organization-specific data sources

Observability & operations

• Per-provider cost attribution metrics
• OpenTelemetry and OpenInference-compatible tracing
• Token usage and latency metrics across providers

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. The 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. The 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.

FAQs

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 that streamlines access, optimizes performance, and ensures scalability.

By abstracting the complexities of AI infrastructure, AI Gateways enable developers to focus on building applications rather than managing underlying systems.

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.

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 optimize infrastructure management, reducing the need for extensive in-house resources and helping organizations save on maintenance and scaling costs while maintaining high performance.

Cite this benchmark

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Cem Dilmegani (2026) - "AI Gateways for OpenAI: OpenRouter Alternatives". Published online at AIMultiple.com. Retrieved May 13, 2026, from: https://aimultiple.com/ai-gateway [Online Resource]

Dilmegani, C. (2026, May 13). AI Gateways for OpenAI: OpenRouter Alternatives. AIMultiple. https://aimultiple.com/ai-gateway

@misc{dilmegani2026, author = {Dilmegani, Cem}, title = {{AI Gateways for OpenAI: OpenRouter Alternatives}}, year = {2026}, month = may, howpublished = {\url{https://aimultiple.com/ai-gateway}}, note = {AIMultiple. Retrieved May 13, 2026} }

Cem Dilmegani

Principal Analyst

Cem has been the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per similarWeb) including 55% of Fortune 500 every month.

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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