{"schemaVersion":"1.0","title":"VibeLeaderboard inference provider registry","description":"A reviewed decision dataset for agents choosing among model routers, inference clouds, cloud catalogs, and direct model-lab APIs.","reviewedAt":"2026-08-22","selectionRule":{"router":"Choose for model comparison, portability, and cross-provider failover.","inference-cloud":"Choose for optimized open or custom models and explicit serving controls.","cloud-catalog":"Choose when cloud identity, networking, procurement, and governance dominate.","direct-lab":"Choose for native model features, release-day access, and a direct support boundary."},"groups":[{"kind":"router","label":"Routers","description":"One account and request shape across multiple model labs or execution providers. Choose this layer for portability and failover, not for the shortest possible supply chain."},{"kind":"inference-cloud","label":"Inference clouds","description":"Specialists that run open or custom models. Their real differences are workload shape, hardware, deployment control, and which modalities they optimize."},{"kind":"cloud-catalog","label":"Cloud catalogs","description":"Model access inside a larger cloud security and procurement boundary. They become compelling when your data, identity, and operations already live there."},{"kind":"direct-lab","label":"Direct lab APIs","description":"The model maker is also the API provider. This is the cleanest path to native features and release-day access, with the strongest single-vendor dependency."}],"providers":[{"slug":"openrouter","name":"OpenRouter","kind":"router","homepage":"https://openrouter.ai","docsUrl":"https://openrouter.ai/docs/quickstart","modelCatalogUrl":"https://openrouter.ai/models","bestFor":"One integration with broad model choice, explicit provider routing, and application-level fallbacks.","caution":"A model name can resolve to several execution providers; pin routing and data policies when reproducibility matters.","modelFamilies":["Claude","GPT","Gemini","Llama","Qwen","DeepSeek","Mistral","Kimi"],"compatibility":"OpenAI-compatible","deployment":"Managed multi-provider routing","intelSources":[{"kind":"sitemap","identifier":"https://openrouter.ai/sitemap.xml?include_prefix=/blog/","label":"OpenRouter Blog","pollIntervalMinutes":120}]},{"slug":"hugging-face-inference-providers","name":"Hugging Face Inference 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video, and audio models with routing, fallbacks, budgets, and AI SDK integration.","caution":"Its strongest advantages sit inside the Vercel and AI SDK workflow; compare portability if that is not your stack.","modelFamilies":["Claude","GPT","Gemini","Grok","Llama","FLUX","Veo"],"compatibility":"OpenAI-compatible","deployment":"Managed multi-provider routing","intelSources":[{"kind":"feed","identifier":"https://vercel.com/changelog/rss.xml","label":"Vercel Changelog","pollIntervalMinutes":180}]},{"slug":"together-ai","name":"Together AI","kind":"inference-cloud","homepage":"https://www.together.ai","docsUrl":"https://docs.together.ai/docs/introduction","modelCatalogUrl":"https://www.together.ai/models","bestFor":"Open-model applications that may grow from serverless calls into fine-tuning or dedicated deployments.","caution":"Its broad platform is more than a thin inference endpoint; compare only the pieces your workload will actually use.","modelFamilies":["Llama","Qwen","DeepSeek","Kimi","FLUX"],"compatibility":"OpenAI-compatible","deployment":"Serverless, dedicated, and custom","intelSources":[{"kind":"sitemap","identifier":"https://www.together.ai/sitemap.xml?include_prefix=/blog/","label":"Together AI Blog","pollIntervalMinutes":180}]},{"slug":"fireworks-ai","name":"Fireworks AI","kind":"inference-cloud","homepage":"https://fireworks.ai","docsUrl":"https://docs.fireworks.ai/getting-started/introduction","modelCatalogUrl":"https://fireworks.ai/models","bestFor":"Production open models, model customization, and teams that need both serverless and dedicated serving.","caution":"Performance depends on the exact model and deployment shape; a platform-wide speed claim is not a useful comparison.","modelFamilies":["Llama","Qwen","DeepSeek","Kimi","FLUX"],"compatibility":"OpenAI-compatible","deployment":"Serverless, on-demand, and 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readiness.","modelFamilies":["Llama","Qwen","DeepSeek","Mistral","Community models"],"compatibility":"OpenAI-compatible","deployment":"Serverless open-model inference","intelSources":[],"coverage":"extended"},{"slug":"hyperbolic","name":"Hyperbolic","kind":"inference-cloud","homepage":"https://hyperbolic.xyz","docsUrl":"https://docs.hyperbolic.xyz","modelCatalogUrl":"https://app.hyperbolic.xyz/models","bestFor":"Hosted open-model inference plus on-demand GPU access in the same compute marketplace.","caution":"Evaluate the managed inference surface separately from raw GPU rentals and community compute supply.","modelFamilies":["Llama","Qwen","DeepSeek","FLUX"],"compatibility":"OpenAI-compatible for selected models","deployment":"Serverless inference and GPU marketplace","intelSources":[],"coverage":"extended"},{"slug":"nscale","name":"Nscale","kind":"inference-cloud","homepage":"https://www.nscale.com","docsUrl":"https://docs.nscale.com","modelCatalogUrl":"https://console.nscale.com","bestFor":"European AI infrastructure spanning hosted inference, fine-tuning, and dedicated GPU capacity.","caution":"Check regional and product availability because serverless endpoints and dedicated infrastructure differ.","modelFamilies":["Llama","Qwen","DeepSeek","FLUX"],"compatibility":"OpenAI-compatible for selected models","deployment":"Serverless and dedicated GPU cloud","intelSources":[],"coverage":"extended"},{"slug":"ovhcloud-ai-endpoints","name":"OVHcloud AI 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general-purpose text inference layer.","modelFamilies":["FLUX","Kling","Wan","Hunyuan Video","Image models"],"compatibility":"Provider-specific APIs","deployment":"Serverless media inference","intelSources":[],"coverage":"extended"},{"slug":"friendli-ai","name":"FriendliAI","kind":"inference-cloud","homepage":"https://friendli.ai","docsUrl":"https://friendli.ai/docs","modelCatalogUrl":"https://friendli.ai/models","bestFor":"Serving open or custom language models through managed endpoints and optimized dedicated deployments.","caution":"Confirm which model families are available serverlessly versus only through custom endpoints.","modelFamilies":["Llama","Qwen","DeepSeek","Custom weights"],"compatibility":"OpenAI-compatible for selected models","deployment":"Serverless and dedicated endpoints","intelSources":[],"coverage":"extended"},{"slug":"siliconflow","name":"SiliconFlow","kind":"inference-cloud","homepage":"https://www.siliconflow.com","docsUrl":"https://docs.siliconflow.com","modelCatalogUrl":"https://cloud.siliconflow.com/models","bestFor":"Broad access to Chinese and global open models through regional API surfaces.","caution":"Regions, model availability, billing, and data-handling terms must be checked for the exact endpoint.","modelFamilies":["Qwen","DeepSeek","GLM","Llama","FLUX"],"compatibility":"OpenAI-compatible for selected models","deployment":"Managed regional inference","intelSources":[],"coverage":"extended"},{"slug":"clarifai","name":"Clarifai","kind":"inference-cloud","homepage":"https://www.clarifai.com","docsUrl":"https://docs.clarifai.com","modelCatalogUrl":"https://clarifai.com/explore","bestFor":"Composing hosted, third-party, and custom models into governed multimodal AI workflows.","caution":"Its application and workflow platform is broader than inference, so compare the serving path you will actually use.","modelFamilies":["Llama","Qwen","Vision","Audio","Custom models"],"compatibility":"Provider-specific APIs","deployment":"Managed, dedicated, and on-premises","intelSources":[],"coverage":"extended"},{"slug":"public-ai","name":"Public AI Inference Utility","kind":"inference-cloud","homepage":"https://www.publicai.co","docsUrl":"https://platform.publicai.co/docs","modelCatalogUrl":"https://platform.publicai.co/models","bestFor":"Low-cost access to open public-interest models from initiatives such as Swiss AI and AI Singapore.","caution":"The nonprofit catalog is intentionally selective and should be evaluated for capacity and production support needs.","modelFamilies":["Apertus","SEA-LION","OLMo","Public open models"],"compatibility":"OpenAI-compatible","deployment":"Nonprofit managed inference utility","intelSources":[],"coverage":"extended"},{"slug":"cloudflare-workers-ai","name":"Cloudflare Workers 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Bedrock","kind":"cloud-catalog","homepage":"https://aws.amazon.com/bedrock/","docsUrl":"https://docs.aws.amazon.com/bedrock/","modelCatalogUrl":"https://docs.aws.amazon.com/bedrock/latest/userguide/models-supported.html","bestFor":"Organizations that need multiple model labs inside AWS identity, networking, governance, and procurement.","caution":"Model availability, features, and data residency are region-specific; the AWS boundary is part of the choice.","modelFamilies":["Claude","Llama","Mistral","Amazon Nova","Cohere","DeepSeek"],"compatibility":"Provider-native API","deployment":"Managed and provisioned throughput","intelSources":[{"kind":"feed","identifier":"https://aws.amazon.com/blogs/machine-learning/feed/","label":"AWS Machine Learning Blog","pollIntervalMinutes":180}]},{"slug":"google-vertex-ai","name":"Google Vertex 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Alibaba Cloud, especially for workloads serving Asian regions.","caution":"International and mainland-China regions can differ in endpoints, catalogs, billing, and governance.","modelFamilies":["Qwen","DeepSeek","Kimi","Embedding","Image and video"],"compatibility":"OpenAI-compatible for selected models","deployment":"Managed and dedicated cloud endpoints","intelSources":[],"coverage":"extended"},{"slug":"databricks-mosaic-ai","name":"Databricks Mosaic AI","kind":"cloud-catalog","homepage":"https://www.databricks.com/product/machine-learning/ai-model-serving","docsUrl":"https://docs.databricks.com/en/machine-learning/model-serving/index.html","modelCatalogUrl":"https://docs.databricks.com/en/machine-learning/foundation-model-apis/supported-models.html","bestFor":"Serving foundation, fine-tuned, and custom models next to governed enterprise data in Databricks.","caution":"Its value depends heavily on adopting the wider Databricks data and governance boundary.","modelFamilies":["Llama","DBRX","Qwen","Embedding","Custom models"],"compatibility":"OpenAI-compatible for selected models","deployment":"Pay-per-token and provisioned endpoints","intelSources":[],"coverage":"extended"},{"slug":"ibm-watsonx-ai","name":"IBM watsonx.ai","kind":"cloud-catalog","homepage":"https://www.ibm.com/products/watsonx-ai","docsUrl":"https://dataplatform.cloud.ibm.com/docs/content/wsj/analyze-data/fm-api.html","modelCatalogUrl":"https://dataplatform.cloud.ibm.com/docs/content/wsj/analyze-data/fm-models.html","bestFor":"Regulated enterprises standardizing models, governance, and deployment through IBM's AI platform.","caution":"The enterprise platform and contract are the differentiator; its public model catalog is not the broadest.","modelFamilies":["Granite","Llama","Mistral","Embedding"],"compatibility":"Provider-native API","deployment":"Managed and dedicated enterprise 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platform without a third-party routing layer.","caution":"Build an adapter boundary if the application may need another lab; native features can deepen lock-in quickly.","modelFamilies":["GPT","o-series","gpt-oss","Image","Audio","Embedding"],"compatibility":"Provider-native API","deployment":"Direct managed API","intelSources":[{"kind":"feed","identifier":"https://openai.com/news/rss.xml","label":"OpenAI News","pollIntervalMinutes":120}]},{"slug":"anthropic-api","name":"Anthropic API","kind":"direct-lab","homepage":"https://www.anthropic.com/api","docsUrl":"https://docs.anthropic.com/","modelCatalogUrl":"https://docs.anthropic.com/en/docs/about-claude/models/overview","bestFor":"Native Claude features, long-running agent work, and direct access to Anthropic's model and tool surface.","caution":"Its Messages API is not an OpenAI clone; use the native contract deliberately or isolate it behind your own interface.","modelFamilies":["Claude Opus","Claude Sonnet","Claude 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