The AI infrastructure ecosystem is growing rapidly, with providers offering diverse approaches to building, hosting, and accelerating models. While they all aim to power AI applications, each focuses on a different layer of the stack.
We benchmarked the most widely used providers on OpenRouter: Cerebras, DeepInfra, Fireworks AI, Groq, Nebius, and SambaNova, using the GPT-OSS-120B model.
We send 108 questions (35 article-based knowledge questions + 73 math problems) to each provider every 5 minutes throughout the day and calculate daily accuracy averages. Alongside these questions, we send a specific reference question each time to measure FTL and E2E latency metrics. Read our benchmark methodology.
AI providers accuracy benchmark
We tested GPT-OSS-120B on a RunPod H200 GPU instance and it achieved 98% accuracy on the dataset we used in our benchmark.
AI providers latency benchmark
Latency and cost comparison
We identified the most widely used models that are also the most commonly offered across AI providers, and then collected the providers’ blended prices per 1M input/output tokens and their first token latency metrics.
AI providers: Detailed comparison
Model hosting platforms
Baseten
Baseten positions itself as a model hosting platform for deploying and running AI models, focusing on production reliability and detailed observability.
Capabilities
- Breaks down API call duration into model loading, inference, and response serialization, allowing developers to pinpoint latency sources.
- Cold starts are tracked at the replica level to measure performance impact.
- Users configure autoscaling parameters such as replica counts and concurrency thresholds. This allows flexibility but introduces the risk of misconfiguration, leading to either wasted cost or higher latency.
- This system provides per-request cost tracking linked to GPU type and usage, enabling performance and cost comparisons when switching between hardware such as A100 and H100 GPUs.
- Real-time log streaming is available, though filtering and search are limited.
Limitations
- Monitoring is detailed at the request level, but log search and filtering are basic, which makes it more challenging to debug large workloads.
- Misconfigured autoscaling can directly impact cost and latency.
Use case: Baseten is ideal for AI developers seeking transparent observability for generative AI models in production environments.
Parasail
Parasail offers an AI inference network designed for flexible GPU utilization and cost optimization.
Capabilities
- The system supports switching between GPU types, with automatic resource allocation based on workload needs.
- The dashboard highlights aggregated usage metrics, including uptime and GPU allocation.
- It offers pricing flexibility through different GPU classes, enabling cost-performance tradeoffs.
Limitations
- Does not offer request-level tracing. Developers cannot analyze the cost or performance of individual requests.
- Observability remains at an aggregate level, limiting the depth of debugging.
Use case: Parasail is designed for organizations prioritizing low-cost, flexible AI solutions, but it provides less insight for teams requiring detailed observability.
DeepInfra
DeepInfra delivers serverless GPU hosting across multiple regions, enabling scalable deployment of AI models as APIs.
Capabilities
- Multi-region support allows inference closer to end users, reducing latency.
- Provides latency and throughput metrics at the dashboard level.
- Offers pay-as-you-go pricing with aggregate cost reporting.
- Supports deployment of open-source generative AI models with simple APIs.
Limitations
- Does not provide request-level tracing, making root cause analysis difficult.
- Cost breakdown is aggregate only, with no per-request or per-region detail.
- Model versioning and rollback mechanisms are not automated, requiring manual handling.
Use case: Best suited for organizations deploying AI workloads across regions, where cost flexibility and geographic coverage matter more than deep debugging.
Together AI
Together AI operates as an AI acceleration cloud offering both model hosting and training capabilities.
Capabilities
- Provides metrics at both the aggregate and request levels, including latency histograms and version-wise call breakdowns.
- Built-in model versioning and rollback enable quick reverting to previous versions.
- Traffic splitting enables A/B testing between model versions.
- Strong SDK support with multi-language client libraries.
- CI/CD integrations make deployment pipelines more mature than other hosting platforms.
Limitations
- This solution offers more operational maturity, but it comes at the cost of higher system complexity compared to lighter-weight hosting platforms.
Use case: Together AI is suitable for AI companies and professional services firms that need reliable version control, advanced monitoring, and integration of generative AI tools into structured workflows.
Hardware-optimized / specialized infrastructure
Cerebras
Cerebras focuses on hardware-optimized AI infrastructure, built around its wafer-scale engine (WSE).
Capabilities
- The WSE integrates millions of processing units on a single chip, providing extremely high throughput for AI workloads.
- Dashboards expose standard metrics such as tokens per second and overall throughput.
- Suitable for training and inference on advanced AI models at scale.
Limitations
- Deployment is not instant; it requires infrastructure preparation.
- Internal hardware details, such as scheduling and memory usage, are abstracted from users.
- Limited support for bringing arbitrary custom models.
Use case: Effective for large-scale, high-throughput machine learning tasks in AI labs, the defense industry, or government agencies where throughput matters more than flexibility.
SambaNova
SambaNova builds AI hardware and software solutions based on its dataflow architecture, which is optimized at the compute graph level.
Capabilities
- Provides platforms such as SambaCloud (cloud service), SambaStack (on-premise), and SambaManaged (managed service).
- Optimized for inference and training of generative AI models.
- Standard dashboard metrics for token-level latency and throughput.
Limitations
- Deployment requires model compatibility with its architecture, demanding additional optimization.
- Internal performance metrics, such as memory bandwidth, are not exposed to users.
- Rollouts are not immediate; implementation phases are required.
Use case: Suited for enterprises that need AI-powered solutions combining hardware and software, especially in industries requiring controlled IT infrastructure.
Groq
Groq offers an AI inference platform powered by its Language Processing Units (LPUs).
Capabilities
- Optimized for sequential token generation with low-latency streaming responses.
- Dashboards expose token counts, latency, and error rates.
- Cost is tracked at the token level.
Limitations
- Does not support custom model deployment. Only Groq-provided models are available.
- Minimal debugging tools are available; if performance issues arise, submitting a support ticket is required.
- Internal operations of LPUs remain opaque.
Use case: Best suited for applications where ultra-low-latency responses for large language models are critical, such as conversational AI or decision-making algorithms.
API-based hosting
Fireworks AI
Fireworks AI provides a lightweight API-based hosting service for AI models.
Capabilities
- Quick model deployment with immediate API endpoints.
- Supports fine-tuning of generative AI models.
- Dashboards provide metrics such as call latency, token usage, error rate, and request count.
Limitations
- Request-level tracing is absent, limiting detailed debugging.
- Cost data is aggregate only, without per-request visibility.
- Rollback is manual; reverting to older versions requires redeployment.
Use case: Suitable for AI developers who need fast access to generative AI capabilities without deep observability or complex deployment management.
Data & ML pipeline integration
Databricks
Databricks provides a unified platform combining data analytics, machine learning, and model management.
Capabilities
- Built on Spark infrastructure, enabling end-to-end integration of data preparation, model training, and inference.
- Uses MLflow for model tracking, including parameters, metrics, and experiment history.
- Unity Catalog ensures data lineage and governance for responsible AI practices.
- Strong in batch processing and model comparison.
Limitations
- Not optimized for real-time inference. Monitoring and metrics are designed for batch jobs, not per-request latency.
- Better suited for managing complex processes across data and models, rather than latency-critical AI workloads.
Use case: Effective for enterprises that need to integrate AI into data science pipelines, particularly for predictive modeling and enterprise applications where governance and traceability are required.
What is an AI provider?
An AI provider is an artificial intelligence company that delivers the infrastructure, models, and services needed for others to develop and run AI-powered solutions.
AI providers are critical because they:
- Lower barriers for AI adoption, especially for companies without deep in-house expertise.
- Provide scalability by handling complex processes such as autoscaling and distributed training.
- Offer cost efficiency with on-demand infrastructure instead of upfront investments in AI hardware.
- Ensure responsible AI practices through governance, traceability, and compliance features.
Types of AI providers
AI providers can be grouped into three main categories:
- AI infrastructure providers focus on specialized AI hardware, including custom processors and high-performance chips, for training and inference.
- Model hosting platforms provide access to generative AI models via APIs, facilitating the integration of AI into applications. They often offer features like autoscaling, latency monitoring, and fine-tuning.
- Data and machine learning platforms emphasize the end-to-end integration of data analytics, model training, and governance, with a focus on responsible AI.
Key features of AI providers
Across categories, most AI providers share several core characteristics that shape how they deliver value and enable organizations to adopt AI capabilities effectively:
Access to large language models and other generative AI models
AI providers offer direct access to large language models (LLMs) and a range of generative AI models for tasks including text generation, speech processing, and image recognition. These models are typically offered through APIs, which makes it easier for organizations to embed AI-powered solutions into applications without requiring extensive model training expertise.
AI infrastructure to handle demanding AI workloads
Providers supply compute environments tailored for advanced AI models and large-scale AI workloads. This includes the processing power needed for training, fine-tuning, and inference, often designed to support both high-throughput batch operations and latency-sensitive tasks. Such infrastructure enables enterprises to run complex processes efficiently and reliably.
Deployment and monitoring dashboards with latency, throughput, and cost metrics
Dashboards are a standard feature, giving visibility into the performance and efficiency of AI systems. Typical metrics include latency per request, overall throughput, token processing rates, and error counts. Cost visibility is also provided, ranging from per-request reporting to aggregate summaries. These tools support effective resource management and optimization.
Options for fine-tuning and model management
Many platforms include the ability to fine-tune generative AI models for specialized use cases. This allows organizations to adapt models to industry-specific needs, such as predictive modeling in supply chain or conversational AI in customer support. Model management features often include version control, rollback, and traffic splitting for experiments, which help maintain reliability while iterating on new deployments.
Pricing flexibility, often based on pay-per-use or token consumption
Instead of relying on heavy upfront investments in AI hardware, providers commonly use consumption-based pricing. This can be structured per request, per token, or by compute time. Flexible pricing lowers the entry barrier for organizations experimenting with AI adoption, while allowing enterprises to align spending with workload demands and optimize for both cost and performance.
What are AI gateways?
An AI gateway is a middleware platform that manages the integration, routing, and governance of AI models and services within enterprise environments. Instead of providing the models themselves, AI gateways act as a unified entry point between applications and multiple AI tools, including large language models, image recognition systems, and other generative AI services.
They handle functions such as API standardization, model orchestration, monitoring, security enforcement, and cost tracking, allowing organizations to control how AI workloads are accessed and used across diverse providers.
Key differences between AI gateways and AI providers
Function
- AI providers deliver AI infrastructure, AI models, and the computing power needed to run them.
- AI gateways manage and orchestrate interactions with those models, offering consistency and governance.
Position in the stack
- AI providers operate at the infrastructure and model layer, supplying the actual AI capabilities.
- AI gateways sit above providers, connecting applications to one or more models through a single control layer.
Scope of responsibility
- AI providers focus on training, fine-tuning, hosting, and serving models.
- AI gateways focus on API unification, workload routing, observability, and policy enforcement across models.
Governance and security
- AI providers implement governance for their own models, such as version control and cost monitoring.
- AI gateways provide centralized governance, enabling compliance, access control, and data protection across multiple models and vendors.
Deployment approach
- AI providers offer various infrastructure choices, including cloud APIs, dedicated clusters, and on-premises hardware.
- AI gateways provide deployment models (global, multicloud, sidecar, or micro-gateway) that optimize traffic routing between applications and models.
Benchmark methodology
In this benchmark, GPT-OSS-120B, the most widely used open-source model on the OpenRouter platform, was analyzed selected. Before proceeding with the benchmark, the baseline performance of the GPT-OSS-120B model was established. The model was tested in a self-hosted environment on a RunPod H200 GPU instance and achieved 98% accuracy on the 108-question dataset used in the benchmark (35 article-based questions + 73 math problems).
Prior to initiating the benchmark, market share data on OpenRouter was analyzed to identify the top six AI providers with the highest share, and only these providers were used in the test. All API requests were sent through the same OpenRouter API endpoint to ensure consistency in test conditions.
Dataset and Test Process
The benchmark dataset consists of a total of 108 questions. Of these questions, 35 are real-world knowledge questions derived from CNN News articles and matched with verified ground truth. The purpose of this section is to measure whether the model accurately recalls numerical information such as percentages, dates, and quantities, and to assess its hallucination tendency. The remaining 73 questions consist of mathematical reasoning problems and test the model’s numerical consistency, logical inference, and computational accuracy.
The 108 questions used in the test process are questions that the model consistently answers correctly. The purpose of this test is to observe performance and quality degradation of the model at specific times of day or during changes in system load.
The test process is conducted as follows:
- The 108 questions are sent individually at 5-minute intervals, and this process continues continuously.
- True/False answers obtained from each question are used in accuracy calculations.
- Simultaneously, with each submission, a fixed reference question is also sent to all providers. The metrics measured from this reference question are:
- First Token Latency (FTL): The time from sending the request until the model produces the first token.
- End-to-End Latency (E2E latency): The time for the model to completely generate the response.
Requests are sent to all providers simultaneously for the same model and through the same API endpoint. The benchmark system operates cyclically; at the end of each day, the accuracy values obtained from the 108 questions and the daily averages of FTL/E2E latency values measured from the fixed reference question are reflected in charts.
Self-Hosted Baseline Test Details
The baseline performance test was conducted by running the openai/gpt-oss-120b model in a self-hosted environment on a RunPod H200 GPU instance. The test environment was built using the RunPod PyTorch template, with the vLLM inference engine (version 0.10.2) installed as the core serving library. A critical component of the software stack was the openai-harmony SDK, which is mandatory for correctly encoding prompts and decoding responses for the GPT-OSS model series. The vLLM engine was configured with gpu_memory_utilization=0.85 and max_model_len=4096 to accommodate the model’s MXFP4 quantization and context requirements. To optimize performance, the flashinfer library was also installed, which provides a significant speedup for inference on H200 hardware.
The benchmark was executed using the test_baseline_harmony_correct.py script, which processes a consolidated dataset of 108 questions (35 article-based questions and 73 math problems). For each question, a prompt was programmatically constructed using the openai-harmony SDK. This involved creating a Conversation object with distinct Role.SYSTEM, Role.DEVELOPER, and Role.USER messages; the DeveloperContent specifically included the “Reasoning: high” instruction to elicit detailed responses. This object was rendered into token IDs using the HarmonyEncodingName.HARMONY_GPT_OSS encoding. Inference was conducted with deterministic sampling parameters (temperature=0.0) and max_tokens=2048 to capture the full reasoning. The stop_token_ids were supplied directly from the harmony encoding’s stop_tokens_for_assistant_actions() method. Finally, the model’s output tokens were parsed by the harmony SDK to extract the structured answer, which was then normalized and validated against the ground truth to calculate accuracy.
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