--- title: Where to Run Qwen 3 in Production: Inference Providers... description: A practical comparison of inference providers for running Qwen 3 and Qwen 3.5 models in production. Covers managed APIs (DeepInfra, Novita, Groq, Together AI,... canonical: https://daily.dev/posts/where-to-run-qwen-3-in-production-inference-providers-compared-ehou3sqw9 twitter:card: summary_large_image twitter:site: @dailydotdev og:type: website og:site_name: daily.dev og:title: Where to Run Qwen 3 in Production: Inference Providers Compared | daily.dev og:description: A practical comparison of inference providers for running Qwen 3 and Qwen 3.5 models in production. Covers managed APIs (DeepInfra, Novita, Groq, Together AI,... og:url: https://daily.dev/posts/where-to-run-qwen-3-in-production-inference-providers-compared-ehou3sqw9 og:image: https://api.daily.dev/og/posts/EhoU3sQW9.png og:image:alt: Where to Run Qwen 3 in Production: Inference Providers Compared og:image:width: 1200 og:image:height: 630 og:locale: en --- > ## Documentation Index > Fetch the complete documentation index at: https://daily.dev/llms.txt > Use this file to discover all available pages before exploring further. # Where to Run Qwen 3 in Production: Inference Providers Compared **[DigitalOcean Community](https://daily.dev/sources/do_community)** · 12 min read · 1 upvotes · 0 comments ## Summary A practical comparison of inference providers for running Qwen 3 and Qwen 3.5 models in production. Covers managed APIs (DeepInfra, Novita, Groq, Together AI, Fireworks AI), routing aggregators (OpenRouter), dedicated inference, and self-hosted GPU infrastructure (vLLM on GPU Droplets or RunPod). Key evaluation criteria include time-to-first-token, output speed, concurrency limits, context window, pricing based on realistic input/output token ratios, compliance, and OpenAI API compatibility. Includes code examples for benchmarking TTFT, using OpenRouter with fallback routing, and deploying Qwen locally with vLLM. Recommends building a reproducible evaluation process rather than relying on static provider rankings. ## Full article daily.dev links to this article rather than hosting it. Read it at the original source: ## Questions this post answers ### How much does it cost to run Qwen3.5-27B on DeepInfra for a typical agent request? DeepInfra listed Qwen3.5-27B at about $0.26 per million input tokens and $2.60 per million output tokens on its regular tier. For a request with 10,000 input tokens and 2,000 output tokens, this works out to roughly $7,800 per million such requests, excluding retries, embeddings, storage, or routing fees. _Anyone budgeting a Qwen deployment can track real provider pricing changes like this on daily.dev._ ### What does the -A number mean in Qwen3.5 model names like Qwen3.5-397B-A17B? The -A number indicates how many parameters are actively used per token in a mixture-of-experts model. Qwen3.5-397B-A17B has 397 billion total parameters, but only about 17 billion are activated for each token, which reduces the compute required during inference compared to using all parameters. _Developers comparing MoE model sizing can follow architecture details like this on daily.dev._ ### What are the tradeoffs between serverless and dedicated inference for running Qwen models? Serverless inference charges per token, requires no GPU management, and suits variable traffic or prototypes, but risks cold starts, shared-capacity queueing, and less predictable tail latency. Dedicated inference allocates fixed capacity per organization, offering more control over networking and scaling, and is economical at high utilization but costly when idle. _Teams weighing inference architecture choices can compare these tradeoffs further on daily.dev._ ## Similar posts on daily.dev - [Best OpenAI-compatible inference APIs: drop-in alternatives for 2026](https://daily.dev/posts/best-openai-compatible-inference-apis-drop-in-alternatives-for-2026-vrtnzttyy) · DigitalOcean Community · 1 upvotes · 0 comments - [Building an Intelligent Chatbot with Qwen3 Instruct and Thinking Models](https://daily.dev/posts/building-an-intelligent-chatbot-with-qwen3-instruct-and-thinking-models-rjickxrqy) · PyImageSearch · 0 upvotes · 0 comments --- Tags: [#gpu](https://daily.dev/tags/gpu), [#agentic-ai](https://daily.dev/tags/agentic-ai), [#ai-inference](https://daily.dev/tags/ai-inference), [#qwen](https://daily.dev/tags/qwen), [#vllm](https://daily.dev/tags/vllm) [View this post on daily.dev](https://daily.dev/posts/where-to-run-qwen-3-in-production-inference-providers-compared-ehou3sqw9) ```json {"@context":"https://schema.org","@graph":[{"@type":"Organization","@id":"https://daily.dev/#organization","name":"daily.dev","url":"https://daily.dev","logo":{"@type":"ImageObject","url":"https://daily.dev/apple-touch-icon.png","width":180,"height":180},"sameAs":["https://twitter.com/dailydotdev","https://github.com/dailydotdev","https://www.linkedin.com/company/daily-dev-ltd"]},{"@type":"WebSite","@id":"https://daily.dev/#website","url":"https://daily.dev","name":"daily.dev","publisher":{"@id":"https://daily.dev/#organization"},"potentialAction":{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https://daily.dev/search?q={search_term_string}"},"query-input":"required name=search_term_string"}}]} {"@context":"https://schema.org","@type":"TechArticle","headline":"Where to Run Qwen 3 in Production: Inference Providers Compared","url":"https://daily.dev/posts/where-to-run-qwen-3-in-production-inference-providers-compared-ehou3sqw9","mainEntityOfPage":{"@type":"WebPage","@id":"https://daily.dev/posts/where-to-run-qwen-3-in-production-inference-providers-compared-ehou3sqw9"},"datePublished":"2026-08-03T09:49:59.745Z","dateModified":"2026-09-14T06:39:36.313Z","description":"A practical comparison of inference providers for running Qwen 3 and Qwen 3.5 models in production. 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