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Stop overpaying for every LLM call. 💸 The DigitalOcean Inference Router resolves intent in ~200ms and beats GPT-5.1 and Claude Sonnet 4.5 on routing accuracy. Powered by Rust, WASM, and purpose-built SLMs, it adds milliseconds, not seconds, to every request. 🌊

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DigitalOcean

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19,853 просмотров • 3 месяцев назад •via X (Twitter)

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Treating every inference request the same is lighting money on fire. DigitalOcean Inference Router sends each request to the right model automatically–lower cost, faster response, and no quality tradeoff. Open-source engine. Drop-in API. Live since April. ✨
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Treating every inference request the same is lighting money on fire. DigitalOcean Inference Router sends each request to the right model automatically–lower cost, faster response, and no quality tradeoff. Open-source engine. Drop-in API. Live since April. ✨

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594,950 просмотров • 3 месяцев назад

DigitalOcean Inference Router is now cache-aware. 🔀 Before, switching models mid-conversation meant losing your cache, and the savings and consistency it bought you. Now, Inference Router weighs that cost before switching. Set a model affinity header or routing budget for full control.
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DigitalOcean Inference Router is now cache-aware. 🔀 Before, switching models mid-conversation meant losing your cache, and the savings and consistency it bought you. Now, Inference Router weighs that cost before switching. Set a model affinity header or routing budget for full control.

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318,366 просмотров • 24 дней назад

.Kimi.ai K3 from Moonshot AI is now live on DigitalOcean Inference Engine. 1M-token context, native vision, built to run agentic tasks for hours. Supported on Inference Router and model synthesis to maximize intelligence per dollar. No setup, no model ops.
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.Kimi.ai K3 from Moonshot AI is now live on DigitalOcean Inference Engine. 1M-token context, native vision, built to run agentic tasks for hours. Supported on Inference Router and model synthesis to maximize intelligence per dollar. No setup, no model ops.

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665,105 просмотров • 1 месяц назад

.DeepSeek-V4-Pro-0813 is live on DigitalOcean Serverless Inference and Inference Router. 🚀 1.6T params / 49B active, 1M-token context, AA Intelligence Index of 53 at max reasoning effort.
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.DeepSeek-V4-Pro-0813 is live on DigitalOcean Serverless Inference and Inference Router. 🚀 1.6T params / 49B active, 1M-token context, AA Intelligence Index of 53 at max reasoning effort.

DigitalOcean

347,944 просмотров • 27 дней назад

DigitalOcean’s model synthesis tool is now available in public preview. 🆕 A new server-side tool: run multiple models on one inference request and get a single synthesized response, no custom orchestration. Just add it to your existing inference request.
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DigitalOcean’s model synthesis tool is now available in public preview. 🆕 A new server-side tool: run multiple models on one inference request and get a single synthesized response, no custom orchestration. Just add it to your existing inference request.

DigitalOcean

2,192,210 просмотров • 1 месяц назад

DigitalOcean Inference Router is now available through OpenCode & setup is 3 steps. ☁️🆕🤝 Run/connect & it automatically routes to the right model for the job. No more paying frontier prices for a commit message.
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DigitalOcean Inference Router is now available through OpenCode & setup is 3 steps. ☁️🆕🤝 Run/connect & it automatically routes to the right model for the job. No more paying frontier prices for a commit message.

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20,992 просмотров • 3 месяцев назад

#Heroku is transitioning to a “sustaining engineering model”, or dev-speak for no new roadmap. 👀 Let us help you migrate your workloads to an Inference Cloud delivering trusted performance, radically simpler operations, and predictable economics—DigitalOcean. We’re ready to help you evaluate your options in a way that is realistic, structured, and built for the long term. 🌊
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#Heroku is transitioning to a “sustaining engineering model”, or dev-speak for no new roadmap. 👀 Let us help you migrate your workloads to an Inference Cloud delivering trusted performance, radically simpler operations, and predictable economics—DigitalOcean. We’re ready to help you evaluate your options in a way that is realistic, structured, and built for the long term. 🌊

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Today we're releasing Not Diamond… The world’s most powerful AI model router. Not Diamond maximizes LLM output quality by automatically recommending the best LLM on every request at lower cost and latency. And it takes <5m to set up. Watch this to see how to start using it:
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Today we're releasing Not Diamond… The world’s most powerful AI model router. Not Diamond maximizes LLM output quality by automatically recommending the best LLM on every request at lower cost and latency. And it takes <5m to set up. Watch this to see how to start using it:

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76,490 просмотров • 2 лет назад

Anthropic engineer: "Most people will use Sonnet 5 and Fable 5 wrong. You can set them up right in one afternoon and stop overpaying every single day." In 31 minutes he shows how to test every model against your real use case and stop overpaying for nothing. Watch the session, then read the guide below on the Claude features 99% of users never find.
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Anthropic engineer: "Most people will use Sonnet 5 and Fable 5 wrong. You can set them up right in one afternoon and stop overpaying every single day." In 31 minutes he shows how to test every model against your real use case and stop overpaying for nothing. Watch the session, then read the guide below on the Claude features 99% of users never find.

Anatoli Kopadze

154,839 просмотров • 2 месяцев назад

Among the fastest DeepSeek V3.2, MiniMax-M2.5, and Qwen 3.5 397B inference in the market, per Artificial Analysis benchmarks (April 2026). ⚡️🤖 Sub-1-second TTFT. 230 tokens per second. Co-designed every layer of the stack with Inferact, performance optimized vLLM, all on NVIDIA HGX B300. Live on DigitalOcean Serverless Inference now. Full breakdown in the comments. ⬇️
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Among the fastest DeepSeek V3.2, MiniMax-M2.5, and Qwen 3.5 397B inference in the market, per Artificial Analysis benchmarks (April 2026). ⚡️🤖 Sub-1-second TTFT. 230 tokens per second. Co-designed every layer of the stack with Inferact, performance optimized vLLM, all on NVIDIA HGX B300. Live on DigitalOcean Serverless Inference now. Full breakdown in the comments. ⬇️

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Claude Sonnet 4.5 is now the default model for Augment Code. We’re rolling it out to all customers over the next 24 hours, where it will be available alongside Sonnet 4 (for a limited time) and GPT-5 in the model picker. Here’s how Sonnet 4.5 compares to Sonnet 4:
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Claude Sonnet 4.5 is now the default model for Augment Code. We’re rolling it out to all customers over the next 24 hours, where it will be available alongside Sonnet 4 (for a limited time) and GPT-5 in the model picker. Here’s how Sonnet 4.5 compares to Sonnet 4:

Augment Code

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🚨 Announcing Smart Model Router - Create Your Own Custom Router To Route To Your Favorite LLM - Route to GPT Sol, Muse Spark, Grok 4.5 and Fable - Optimize for cost or performance - Use Fable only for hard coding Once you create a router, you can use it on our Chat, Agent or in an API! Coming on Monday - Support for all CLI clients including Code, Claude Code and others
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🚨 Announcing Smart Model Router - Create Your Own Custom Router To Route To Your Favorite LLM - Route to GPT Sol, Muse Spark, Grok 4.5 and Fable - Optimize for cost or performance - Use Fable only for hard coding Once you create a router, you can use it on our Chat, Agent or in an API! Coming on Monday - Support for all CLI clients including Code, Claude Code and others

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.Grok Build is on DigitalOcean Marketplace. 🆕☁️🤖 SSH in. Run grok. Build. @xAI's coding agent, pre-configured on DigitalOcean Serverless Inference.
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.Grok Build is on DigitalOcean Marketplace. 🆕☁️🤖 SSH in. Run grok. Build. @xAI's coding agent, pre-configured on DigitalOcean Serverless Inference.

DigitalOcean

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Production traffic is not uniform. You get a few requests that need your best model, but most are simple questions and lookups you can solve with cheaper, faster models. The most expensive mistake you can make today is sending every request to your strongest model. You need routing. Period. This is the simplest trick to improve the architecture of whatever you are building. Please, don't implement routing yourself. You don't have to. I'm currently working with TrueFoundry's Auto Routing. It reads each request, classifies it as simple, medium, or complex, and sends it to the model assigned to that tier. You have two choices: 1. Send every request to the free heuristic classifier to score signals such as technical vocabulary, code, prompt length, and multi-step reasoning. 2. Send the request to an LLM classifier when its difficulty requires a more nuanced judgment. The beauty of using routing is that nothing changes in your code. You still call a single endpoint model, but routing works behind the scenes to pair every request with the best possible model. TrueFoundry ran several experiments with two different setups: 1. Send every request to Claude Opus 2. Send every request to a router with Haiku, Sonnet, and Opus The first experiment ran 550 deterministically graded academic prompts through every setup. Auto Routing was 69% cheaper while retaining 98% of the baseline quality. The second experiment ran three production-shaped workloads through every setup, using user chats, developer chats, and long agent tasks. Auto Routing was 80% cheaper. Thanks to the TrueFoundry team for partnering with me on this post.
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Production traffic is not uniform. You get a few requests that need your best model, but most are simple questions and lookups you can solve with cheaper, faster models. The most expensive mistake you can make today is sending every request to your strongest model. You need routing. Period. This is the simplest trick to improve the architecture of whatever you are building. Please, don't implement routing yourself. You don't have to. I'm currently working with TrueFoundry's Auto Routing. It reads each request, classifies it as simple, medium, or complex, and sends it to the model assigned to that tier. You have two choices: 1. Send every request to the free heuristic classifier to score signals such as technical vocabulary, code, prompt length, and multi-step reasoning. 2. Send the request to an LLM classifier when its difficulty requires a more nuanced judgment. The beauty of using routing is that nothing changes in your code. You still call a single endpoint model, but routing works behind the scenes to pair every request with the best possible model. TrueFoundry ran several experiments with two different setups: 1. Send every request to Claude Opus 2. Send every request to a router with Haiku, Sonnet, and Opus The first experiment ran 550 deterministically graded academic prompts through every setup. Auto Routing was 69% cheaper while retaining 98% of the baseline quality. The second experiment ran three production-shaped workloads through every setup, using user chats, developer chats, and long agent tasks. Auto Routing was 80% cheaper. Thanks to the TrueFoundry team for partnering with me on this post.

Santiago

13,446 просмотров • 2 дней назад

Comparisons of Sonnet 4.5 vs GPT-5 Pro. I appreciate the comparison is not exactly fair, but I've had GPT-5 Pro videos ready to go, so forgive me. Saying that it does give a good benchmark, I don't think there was a single instance of Sonnet 4.5 being better, and I was testing it in Claude Code, so it should have had an advantage of not being stuck in the web client. It doesn't mean to say that Claude 4.5 would be worse on all dimensions, these are 1-shot single file HTML files, so not going to test the full agentic suite of capabilities. Song via Suno v5 Lyrics by Sonnet 4.5
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Comparisons of Sonnet 4.5 vs GPT-5 Pro. I appreciate the comparison is not exactly fair, but I've had GPT-5 Pro videos ready to go, so forgive me. Saying that it does give a good benchmark, I don't think there was a single instance of Sonnet 4.5 being better, and I was testing it in Claude Code, so it should have had an advantage of not being stuck in the web client. It doesn't mean to say that Claude 4.5 would be worse on all dimensions, these are 1-shot single file HTML files, so not going to test the full agentic suite of capabilities. Song via Suno v5 Lyrics by Sonnet 4.5

Peter Gostev (SF 24-28 August)

255,966 просмотров • 11 месяцев назад

Stop prototyping, start deploying. ☁️🤖 Join us at #DOdeploy SF to master production #AI inference. Grounded in real-world costs &amp; reliability, we'll show you how to scale with DigitalOcean. 🌁
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Stop prototyping, start deploying. ☁️🤖 Join us at #DOdeploy SF to master production #AI inference. Grounded in real-world costs &amp; reliability, we'll show you how to scale with DigitalOcean. 🌁

DigitalOcean

10,326 просмотров • 7 месяцев назад

.Character.AI, AMD, & #DigitalOcean are scaling the Qwen3-235B open-weight Mixture-of-Experts AI model to serve approximately 20M users. 🤖📈☁️ This required tuning a 5600/140 (ISL/OSL) workload with FP8 inference on AMD Instinct™ MI325X GPUs to maximize request throughput (QPS).
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.Character.AI, AMD, & #DigitalOcean are scaling the Qwen3-235B open-weight Mixture-of-Experts AI model to serve approximately 20M users. 🤖📈☁️ This required tuning a 5600/140 (ISL/OSL) workload with FP8 inference on AMD Instinct™ MI325X GPUs to maximize request throughput (QPS).

DigitalOcean

27,726 просмотров • 8 месяцев назад

.RadixArk brought DeepSeek V4 to production with DigitalOcean and AMD. AMD Instinct™ MI350X GPU Droplets, plus joint engineering that drove a ~10x throughput gain. When the team that builds the inference engine picks your stack, that's the signal.
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.RadixArk brought DeepSeek V4 to production with DigitalOcean and AMD. AMD Instinct™ MI350X GPU Droplets, plus joint engineering that drove a ~10x throughput gain. When the team that builds the inference engine picks your stack, that's the signal.

DigitalOcean

13,381 просмотров • 2 месяцев назад

This is the last chatbot you’ll ever need. Yesterday, Mckay Wrigley built an oss Not Diamond-powered chat app. We loved it. So today we’re releasing a hosted version. Get the best LLM on every message and hyper-personalize routing to your preferences with feedback. Watch how:
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This is the last chatbot you’ll ever need. Yesterday, Mckay Wrigley built an oss Not Diamond-powered chat app. We loved it. So today we’re releasing a hosted version. Get the best LLM on every message and hyper-personalize routing to your preferences with feedback. Watch how:

Tomas Hernando Kofman

19,963 просмотров • 2 лет назад

Open-sourcing the first unified LLM routing library 🔥 Meet LLMRouter 16+ routers in ONE framework. Stop reimplementing routing papers from scratch, simply run pip install llmrouter-lib And instantly deploy SOTA LLM routing tailored to your exact needs! 🔗 Get Started with LLMRouter 🚀 Code: 🌐 Project Page: 💸 Why LLMRouter? Why pay GPT-5 prices for "What's the weather?" Smart routing = Significant Savings Simple query → Cheap model Complex query → Powerful model Save 30–50% on inference costs without sacrificing quality 🧭 The 16+ Router Arsenal Switch between state-of-the-art methods with a single flag change. We cover every paradigm: ✅ Single-Round: From classic ML (KNN, SVM) to neural approaches (Graph, Contrastive). ✅ Multi-Round: RL-powered reasoning that "thinks" before routing. ✅ Agentic: Breaks down complex tasks and routes step-by-step. ✅ Personalized: Learns from user history to fit unique needs. 🛠️ Complete "Out-of-the-Box" Toolchain We didn't just ship the code; we shipped the pipeline. ✅ Unified CLI: Train, infer, and chat with any router. ✅ Interactive UI: Full Gradio interface included. ✅ Benchmarks: 11 datasets ready to use + full data generation pipeline. From LLM routing research to production in minutes. 🚀
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Open-sourcing the first unified LLM routing library 🔥 Meet LLMRouter 16+ routers in ONE framework. Stop reimplementing routing papers from scratch, simply run pip install llmrouter-lib And instantly deploy SOTA LLM routing tailored to your exact needs! 🔗 Get Started with LLMRouter 🚀 Code: 🌐 Project Page: 💸 Why LLMRouter? Why pay GPT-5 prices for "What's the weather?" Smart routing = Significant Savings Simple query → Cheap model Complex query → Powerful model Save 30–50% on inference costs without sacrificing quality 🧭 The 16+ Router Arsenal Switch between state-of-the-art methods with a single flag change. We cover every paradigm: ✅ Single-Round: From classic ML (KNN, SVM) to neural approaches (Graph, Contrastive). ✅ Multi-Round: RL-powered reasoning that "thinks" before routing. ✅ Agentic: Breaks down complex tasks and routes step-by-step. ✅ Personalized: Learns from user history to fit unique needs. 🛠️ Complete "Out-of-the-Box" Toolchain We didn't just ship the code; we shipped the pipeline. ✅ Unified CLI: Train, infer, and chat with any router. ✅ Interactive UI: Full Gradio interface included. ✅ Benchmarks: 11 datasets ready to use + full data generation pipeline. From LLM routing research to production in minutes. 🚀

Jiaxuan You

75,244 просмотров • 8 месяцев назад