# AI Platform Published articles for AI Platform. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## How NVIDIA Groq 3 LPX Deterministic Execution Drives Power-Efficient High-Interactivity Inference on NVIDIA Vera Rubin DevFeed: [How NVIDIA Groq 3 LPX Deterministic Execution Drives Power-Efficient High-Interactivity Inference on NVIDIA Vera Rubin]() Original publisher: [Read original article]() Author: Tanya Lenz Published: 2026-09-15T16:55:00Z Content type: article Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [Groq 3 LPX](), [LPX](), [NVIDIA Vera Rubin](), [Vera Rubin NVL72](), [groq](), [Inference](), [Low Latency](), [long-context]() Tags: [agentic-ai-generative-ai](), [ai-factory](), [ai-inference](), [ai-platform](), [drive](), [dsx](), [groq](), [groq-3-lpx](), [inference](), [inference-performance](), [latency](), [low-latency](), [lpx](), [nvidia-vera-rubin](), [nvl72](), [performance](), [power-management](), [vera-rubin](), [vera-rubin-nvl72]() ### AI overview This NVIDIA developer article explains how Groq 3 LPX uses deterministic execution across 256 LPU chips to support low-latency inference on NVIDIA Vera Rubin. It describes compiler-scheduled execution and power-management techniques including Preemptive Power and Clock Period Synthesis. ### Source excerpt Power is a defining constraint for AI factories. As AI workloads demand a full compute platform to serve them, each component of that platform must maximize... ## 9 top conversational AI platforms in 2026 DevFeed: [9 top conversational AI platforms in 2026]() Original publisher: [Read original article]() Author: Jesse Sumrak Published: 2026-09-15T00:00:00Z Content type: comparison Language: en Sources: [Twilio Blog]() Topics: [Conversational AI](), [AI Platform](), [Deployment](), [Large Language Model](), [Messaging]() Tags: [ai-platform](), [article](), [conversational-ai](), [industry-insights](), [platforms]() ### AI overview A comparison of conversational AI platforms in 2026, covering hosted agent platforms, infrastructure and APIs, enterprise offerings, and open-source options. It explains how these products differ in model support, deployment ownership, channel coverage, integrations, and human handoff. ### Source excerpt Learn about the top conversational AI platforms in 2026, including infrastructure, enterprise, open source, and agentic options. See what fits your stack. ## Red Hat AI 3.5 tackles the GPU queue that can stall AI pilots DevFeed: [Red Hat AI 3.5 tackles the GPU queue that can stall AI pilots]() Original publisher: [Read original article]() Author: Adrian Bridgwater Published: 2026-09-10T17:01:36Z Content type: news Language: en Sources: [The New Stack]() Topics: [Multi-tenancy](), [AI Platform](), [GPU](), [benchmarking]() Tags: [ai](), [ai-operations](), [ai-platform](), [gpu](), [multi-tenancy](), [observability](), [platform-engineering]() ### AI overview Red Hat AI 3.5 adds multi-tenancy, isolation, priority-aware GPU scheduling, safety benchmarking, observability, and GPU resource management for enterprise AI workloads. ### Source excerpt Red Hat released Red Hat AI 3.5 this week, a move designed to let software engineering teams run AI with The post Red Hat AI 3.5 tackles the GPU queue that can stall AI pilots appeared first on The New Stack. ## d-Matrix Adopts NVIDIA NVLink Fusion for Rack-Scale XPU Deployment DevFeed: [d-Matrix Adopts NVIDIA NVLink Fusion for Rack-Scale XPU Deployment]() Original publisher: [Read original article]() Author: Jesse Clayton Published: 2026-09-10T13:00:21Z Content type: news Language: en Sources: [NVIDIA Blog]() Topics: [NVLink](), [AI Platform](), [AI Inference](), [Deployment](), [networking](), [Latency](), [cpu](), [GPU]() Tags: [ai-inference](), [ai-infrastructure](), [ai-platform](), [corporate](), [d-matrix](), [deployment](), [gpu](), [latency](), [networking](), [nvidia](), [nvidia-vera](), [nvidia-vera-rubin](), [nvlink](), [spectrum-x](), [xpu]() ### AI overview d-Matrix announced plans to use NVIDIA NVLink Fusion to connect its next-generation Raptor XPUs with NVIDIA AI infrastructure. The article describes using NVLink, Spectrum-X networking and MGX rack designs to support rack-scale, low-latency inference deployments. ### Source excerpt AI inference chipmaker d-Matrix today announced it will use NVLink Fusion to connect its next-generation Raptor XPUs to NVIDIA's AI infrastructure platform -- joining a growing roster of ecosystem partners. By connecting Raptor to NVIDIA NVLink scale-up and Spectrum-X scale-out networking, the NVIDIA MGX rack architecture and the broader NVIDIA AI platform, NVLink Fusion gives [...] ## Creating an AI Platform for classic ML online inference DevFeed: [Creating an AI Platform for classic ML online inference]() Original publisher: [Read original article]() Author: Rohith Leeladharan Published: 2026-09-10T07:26:46Z Content type: tutorial Language: en Sources: [Amex GBT Technology]() Topics: [AI Platform](), [Inference](), [MLOps](), [Machine learning](), [model-deployment]() Tags: [ai](), [ai-platform](), [ai-platform-engineering](), [deploy](), [experiments](), [feature-store](), [inference](), [machine-learning](), [ml](), [mlops](), [monitoring](), [predictions]() ### AI overview This article describes how American Express Global Business Travel built an AI platform for deploying classic machine-learning systems and supporting online inference. It explains the platform's requirements--simplicity, self-service, experimentation, and continuous improvement--and details the pre-process, predict, post-process pattern used by inference engines. ### Source excerpt Introduction In 2021, we were given the mission to have AI Systems running in production. The team, instead of just following a classical MLOps process, that involves transforming a Jupyter notebook into a product running in production, decided to go further by creating a platform to deploy AI systems in production. The team decided the platform should respect these requirements: Simplicity: The code powering AI systems should be simple, readable, and easy to maintain -- less intricacy means fewer bugs in production and greater reliability. Self-service: Anyone should be able to build and deploy AI systems autonomously, without depending on a central team. Experimentation: The platform should make it easy to run and iterate on experiments. Continuous improvement: Data related to events and interactions within AI systems must be captured, enabling monitoring and continuous improvement over time. In this article, we will walk through the work done to build a platform that fulfills these four requirements. Background At American Express Global Business Travel, we use machine learning (ML) models for a variety of user experiences like ranking hotel and flight search results. Our ML models are wrapped in inference engines that handle both pre-processing of input data before we run a prediction with the model, and post-processing of output data before returning the output to the caller. The overall flow looks something like this: Figure 1: Handling an inference request A client service that would like the ML model's predictions provides necessary context about the request like which user the request is for. Then, optionally, the inference engine fetches any necessary features for inference from our feature store [part 1][part 2]. Finally, it pre-processes the data, runs the predictions using the trained ML model, and does any necessary post-processing of the model output before returning the response to the caller. We call this the pre-process, predict, post-process patter ## How to Carry User Identity Across Federated Kubernetes and AI Platforms DevFeed: [How to Carry User Identity Across Federated Kubernetes and AI Platforms]() Original publisher: [Read original article]() Author: Elizabeth Goodman Published: 2026-09-03T22:36:02Z Content type: tutorial Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [AI Platform](), [Kubernetes](), [AI, ML & Data Engineering]() Tags: [ai-platform](), [ai-platforms-deployment](), [api](), [aws](), [cloud](), [cloud-services](), [data](), [data-center-cloud](), [developer-tools-techniques](), [how-to](), [identity](), [infrastructure](), [kubernetes](), [software-defined-data-center]() ### AI overview The article presents a central identity-gateway pattern for carrying user identity across federated Kubernetes, data, and AI platforms. It uses OIDC, a shared session store, stateless data-plane gateways, and an identity-validation API to establish trusted local identity context without distributing raw tokens to every application. ### Source excerpt Modern AI platforms are no longer a single application behind one login screen. A user may start in a central portal, open a governed dataset, launch a notebook... ## Polimill builds Japan's next-generation public AI infrastructure DevFeed: [Polimill builds Japan's next-generation public AI infrastructure]() Original publisher: [Read original article]() Published: 2026-08-31T07:00:00Z Content type: article Language: en Sources: [OpenAI News]() Topics: [AI Chat](), [AI search]() Tags: [ai-infrastructure](), [ai-platform](), [codex](), [data](), [generative-ai](), [government](), [gpt](), [japan](), [openai](), [platform](), [productivity](), [public-ai](), [public-sector](), [search](), [workflows]() ### AI overview Polimill's QommonsAI uses OpenAI GPT models to organize and search municipal administrative knowledge across Japan, supporting public-sector workflows and development productivity. ### Source excerpt Polimill uses OpenAI GPT models and Codex to help municipalities search and use administrative knowledge while accelerating development. ## How an MIT research project became a global programming language DevFeed: [How an MIT research project became a global programming language]() Original publisher: [Read original article]() Author: Zach Winn | MIT News Published: 2026-08-31T04:00:00Z Content type: news Language: en Sources: [MIT AI News]() Topics: [The Julia Language](), [Programming language](), [Data Science](), [Complex Systems](), [Data analysis](), [Open Source]() Tags: [ai](), [ai-platform](), [alan-edelman](), [analysis](), [applications](), [artificial-intelligence](), [chris-rackauckas](), [complex-systems](), [computer-science-and-artificial-intelligence-laboratory-csail](), [deshpande-center](), [jeff-bezanson](), [julia-programming-language](), [juliahub](), [language](), [mit-schwarzman-college-of-computing](), [open-source](), [programming](), [programming-language](), [software](), [startups](), [stefan-karpinski](), [viral-shah]() ### AI overview An MIT research project created Julia, a free and open-source programming language for scientific research, data analysis, and complex-systems modeling. The article describes Julia's adoption by researchers, engineers, companies, and universities, and introduces JuliaHub's Dyad 3.0 AI platform. ### Source excerpt With millions of users across the world, Julia has been used to conduct cutting-edge research and to design new drugs, jet engines, heat pumps, and more. ## Using deterministic systems and provenance to make AI-generated financial analysis verifiable DevFeed: [Using deterministic systems and provenance to make AI-generated financial analysis verifiable]() Original publisher: [Read original article]() Published: 2026-08-25T00:00:00Z Content type: article Language: en Sources: [Chainguard: Unchained]() Topics: [Trustworthy AI](), [Artificial Intelligence](), [AI Platform](), [Large Language Model](), [Code](), [Finance]() Tags: [ai](), [ai-platform](), [financial](), [models](), [provenance](), [trustworthy-ai](), [verification]() ### AI overview Kepler describes a model-agnostic approach to trustworthy AI that combines language models with deterministic tools for retrieval, computation, provenance, and traceability. The article focuses on making financial analysis outputs verifiable by linking numbers to their sources, formulas, or computations. ### Source excerpt Trustworthy AI takes more than a powerful model. See how Kepler uses deterministic systems and provenance to make financial analysis verifiable. ## Mistral x HUMAIN DevFeed: [Mistral x HUMAIN]() Original publisher: [Read original article]() Published: 2026-08-24T16:02:41Z Content type: article Language: en Sources: [Mistral AI Blog]() Topics: [AI Strategy](), [AI Infrastructure](), [AI Models](), [Model Development](), [model-deployment](), [Localization (l10n)](), [datacenter](), [Cybersecurity]() Tags: [ai](), [ai-infrastructure](), [ai-platform](), [arabic](), [data](), [data-center](), [deployment](), [enterprise](), [europe](), [financial-services](), [manufacturing](), [model-development](), [public-sector](), [security]() ### AI overview Mistral and HUMAIN announce a strategic collaboration to advance sovereign AI in Saudi Arabia and across the Middle East. The initiative covers AI infrastructure, advanced model development, localized Arabic-capable models, and deployment of AI solutions for regulated industries. ### Source excerpt The most powerful AI platform for enterprises. Customize, fine-tune, and deploy AI assistants, autonomous agents, and multimodal AI with open models. ## Apollo Summit 2026: Turn Your API Platform Into Your AI Platform DevFeed: [Apollo Summit 2026: Turn Your API Platform Into Your AI Platform]() Original publisher: [Read original article]() Author: Eric Holzhauer Published: 2026-08-04T12:00:00Z Content type: article Language: en Sources: [Apollo Blog]() Topics: [GraphQL](), [GraphOS](), [AI Platform](), [API Platform]() Tags: [2026](), [agent](), [agents](), [ai-agents](), [ai-platform](), [announcement](), [api-platform](), [apollo](), [graphos](), [graphql](), [mcp](), [summit]() ### AI overview Apollo Summit 2026 will take place in San Francisco from October 6 to 8. The article previews sessions and workshops on using governed GraphQL graphs as an access and context layer for applications and AI agents, including case studies from Block, Brex, Coolblue, and Starbucks. ### Source excerpt Turning an API platform into an AI platform starts with a governed graph. See real patterns from Block, Brex, and Starbucks at Apollo Summit 2026, Oct 6-8. ## Hot trends in platform engineering for AI: Two pathways, one transformation DevFeed: [Hot trends in platform engineering for AI: Two pathways, one transformation]() Original publisher: [Read original article]() Author: Mallory Haigh Published: 2026-07-23T05:40:01Z Content type: article Language: en Sources: [Platform Engineering Blog]() Topics: [Platform Engineering](), [AI Platform](), [Artificial Intelligence](), [MLOps](), [DevOps](), [AI Agent](), [GPU](), [Kubernetes](), [Orchestration](), [CI/CD](), [datasets](), [Infrastructure as code]() Tags: [ai](), [ai-agent](), [ai-platform](), [automation](), [ci-cd](), [datasets](), [devops](), [gpu](), [infrastructure-as-code](), [kubernetes](), [orchestration](), [platform](), [platform-engineering]() ### AI overview This article describes two connected paths in platform engineering for AI: using LLMs and agents to improve existing platform capabilities, and building specialized platforms for AI/ML workloads. It highlights infrastructure-as-code generation, intelligent troubleshooting, security policy automation, GPU orchestration, model registries, feature stores, experiment tracking, and the need to support data scientists and ML engineers. It argues that conventional software-delivery platforms may not fit ML workloads and points toward more autonomous platforms. ### Source excerpt Discover the hot trends shaping platform engineering for AI and learn more about the shift toward autonomous, governance-first, and unified DevOps/MLOps workflows. ## Setting a World Record for MoE Pre-Training on NVIDIA GB300 NVL72 DevFeed: [Setting a World Record for MoE Pre-Training on NVIDIA GB300 NVL72]() Original publisher: [Read original article]() Author: Kirthi Devleker Published: 2026-07-21T18:30:00Z Content type: article Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [Mixture of Experts (MoE)](), [GPU](), [Nvidia](), [AI Platform](), [NCCL](), [networking](), [deepseek]() Tags: [agentic-ai-generative-ai](), [ai](), [ai-platform](), [ai-training](), [collective](), [communication](), [compute](), [data-center-cloud](), [deepseek](), [developer-tools-techniques](), [efficiency](), [featured](), [frontier-model](), [gb300-nvl72](), [gpu](), [infrastructure](), [latency](), [llm-techniques](), [low-latency](), [megatron](), [mixture-of-experts-moe](), [moe](), [nvidia](), [nvl72](), [performance](), [top-stories](), [train](), [training-ai-models]() ### AI overview The article explains how NVIDIA GB300 NVL72 achieved a world record for DeepSeek-V3 671B mixture-of-experts pre-training. It focuses on the communication demands of MoE models, including all-to-all traffic between GPUs, and the need for tightly coupled scale-up and predictable scale-out networking to sustain delivered training performance. ### Source excerpt Frontier model pre-training has converged on mixture of experts (MoE), which is fundamentally changing what limits large-scale AI training. As compute per token... ## In-House LLM Serving at Netflix DevFeed: [In-House LLM Serving at Netflix]() Original publisher: [Read original article]() Author: Netflix Technology Blog Published: 2026-07-17T21:32:39Z Content type: article Language: en Sources: [Netflix](), [Netflix TechBlog - Medium]() Topics: [LLMs](), [Netflix](), [Inference](), [AI Platform](), [model-deployment](), [autoscaling](), [GPU](), [gRPC]() Tags: [ai](), [ai-platform](), [autoscaling](), [deployment](), [gpu](), [inference](), [llm](), [model-serving](), [netflix](), [platform-engineering]() ### AI overview Netflix describes its in-house LLM serving stack, covering deployment, inference, API access paths, and production trade-offs. ### Source excerpt By AI Platform's Model Runtime team and Inference team Introduction Most organizations consume LLMs through hosted APIs. Netflix went further -- we run the full stack ourselves, from model deployment through inference, inside our existing production environment rather than a separate ML silo. Some of those decisions weren't obvious, and a few revealed their trade-offs only under production load. This post focuses on the choices where alternatives were seriously considered: engine selection, model packaging, API surface design, deployment strategy, and output constraints enforcement. The goal is to share not just what was built, but why -- and what production revealed that the design phase didn't anticipate. Architecture Overview Member-scale ML at Netflix is fronted by a unified JVM-based serving system that handles the end-to-end flow for downstream consumers: routing and A/B test logic, candidate generation, feature fetching, inference, post-processing, and logging at each stage. Both real-time and cached batch paths are supported. Figure 1 shows the two ways callers reach inference today: the gRPC path through this serving system and a direct HTTP path used by newer LLM-driven applications. Where inference runs depends on the model. Small CPU models run in-process, avoiding remote-call overhead. Larger models need GPUs -- the serving system handles pre- and post-processing locally but delegates inference to a remote service, Model Scoring Service (MSS). MSS is the shared inference backend, supporting XGBoost, TensorFlow, PyTorch, and LLMs behind a unified interface, with NVIDIA Triton Inference Server underneath managing model loading, batching, and GPU scheduling. On top of Triton sits a Java control plane that handles deployment, versioning, health checking, autoscaling, and multi-region rollout. Model authors package their artifacts and configure the deployment; the control plane provisions GPU instances, configures Triton, and orchestrates zero-downtime upgrades ## How BetterTracker Replaced Its Vector Store with CockroachDB DevFeed: [How BetterTracker Replaced Its Vector Store with CockroachDB]() Original publisher: [Read original article]() Author: Yohan Shirazi Published: 2026-07-15T00:00:00Z Content type: article Language: en Sources: [Cockroach Labs]() Topics: [CockroachDB](), [AI Platform](), [Databases](), [AI search](), [Retrieval Augmented Generation (RAG)](), [Availability](), [Software as a service](), [Transactions](), [data]() Tags: [ai](), [ai-platform](), [availability](), [cockroachdb](), [compliance](), [consistency](), [data](), [database](), [rag](), [saas](), [search](), [soc2](), [transactions](), [vector-database](), [vector-search]() ### AI overview BetterTracker replaced a standalone vector database by running transactional workloads and vector search together on CockroachDB. The article describes how this supports the company's AI-powered platform while reducing infrastructure complexity and compliance risk. ### Source excerpt BetterTracker eliminated a standalone vector database by running OLTP and vector search together on CockroachDB--cutting costs, complexity, and compliance risk in one move. ## Harness Named a Leader in the 2026 Gartner® Magic Quadrant™ DevFeed: [Harness Named a Leader in the 2026 Gartner® Magic Quadrant™]() Original publisher: [Read original article]() Author: Harness Team Published: 2026-06-17T00:00:00Z Content type: release Language: en Sources: [Harness Blog]() Topics: [DevSecOps](), [AI Platform](), [AI-assisted coding](), [Artificial Intelligence](), [Software]() Tags: [2026](), [agents](), [ai](), [ai-agents](), [ai-coding](), [ai-platform](), [blog](), [devsecops](), [gartner](), [harness](), [platforms](), [software](), [software-delivery]() ### AI overview Harness announces that it was named a Leader in the 2026 Gartner Magic Quadrant for DevSecOps Platforms for the third consecutive year and was positioned furthest on the report's Completeness of Vision axis. The article presents Harness's view that governed AI and autonomous agents will shape the future of software delivery. ### Source excerpt Harness has been named a Leader in the 2026 Gartner® Magic Quadrant™ for DevSecOps Platforms for the third consecutive year and positioned furthest in Completen | Blog ## How Endava is redesigning software delivery around AI agents DevFeed: [How Endava is redesigning software delivery around AI agents]() Original publisher: [Read original article]() Published: 2026-06-04T12:00:00Z Content type: article Language: en Sources: [OpenAI News]() Topics: [Artificial Intelligence](), [Software Engineering](), [AI-assisted coding](), [OpenAI](), [ChatGPT](), [codex](), [AI Platform](), [Deployment]() Tags: [ai](), [ai-agents](), [ai-assisted-coding](), [ai-platform](), [chatgpt](), [codex](), [deployment](), [developers](), [software-engineering]() ### AI overview Endava is making AI central to enterprise work by adopting OpenAI as its enterprise AI platform and providing employees with ChatGPT Enterprise and Codex. Its DavaFlow methodology embeds OpenAI technology across meeting preparation, business planning, product discovery, software engineering, and deployment, while AI-assisted workflows also support legal, project management, and commercial teams. ### Source excerpt Learn how Endava is using AI agents, ChatGPT Enterprise, and Codex to accelerate software delivery, automate workflows, and build an AI-native culture across the enterprise. ## NetEase Games reduced LLM model load time to 3 minutes with Fluid prefetching DevFeed: [NetEase Games reduced LLM model load time to 3 minutes with Fluid prefetching]() Original publisher: [Read original article]() Author: Haifeng Liao Published: 2026-05-06T13:00:00Z Content type: article Language: en Sources: [Kubernetes Overview, News and Trends | The New Stack]() Topics: [Large Language Model](), [AI Inference](), [AI Platform](), [autoscaling](), [Kubernetes](), [Cache](), [Serverless](), [GPU]() Tags: [ai-infrastructure](), [ai-platform](), [autoscaling](), [cache](), [cncf](), [gpu](), [inference](), [kubernetes](), [large-language-models](), [latency](), [llm](), [post-contributed](), [serverless](), [sponsor-cncf](), [sponsored-post-contributed]() ### AI overview NetEase Games describes how slow model loading limited serverless LLM inference across regions. Using an Alluxio-based cache and then Fluid's prefetching workflow, the representative model load time fell from 42 minutes to 3 minutes. ### Source excerpt At NetEase Games, we learned a hard lesson about large language model (LLM) inference in production: elastic compute is only The post How NetEase Games cut LLM cold starts from 42 minutes to 30 seconds appeared first on The New Stack. ## DigitalOcean Dedicated Inference: A Technical Deep Dive DevFeed: [DigitalOcean Dedicated Inference: A Technical Deep Dive]() Original publisher: [Read original article]() Author: dgupta Published: 2026-04-25T02:51:09Z Content type: article Language: en Sources: [DigitalOcean]() Topics: [AI Platform](), [Digital Ocean](), [Large Language Model](), [model-serving](), [GPU](), [autoscaling](), [Kubernetes](), [VPC]() Tags: [ai-platform](), [architecture](), [autoscaling](), [deep-dive](), [digitalocean](), [engineering](), [gpu](), [inference](), [kubernetes](), [llm](), [model-serving](), [production](), [technical]() ### AI overview This technical deep dive explains DigitalOcean Dedicated Inference, a managed LLM hosting service on dedicated GPUs. It describes the Kubernetes-native inference stack, public and private VPC endpoints, OpenAI-compatible APIs, serving and routing components, autoscaling, observability, and cost considerations for sustained, high-volume workloads. ### Source excerpt Getting a model to answer 10 inference requests concurrently is tricky but simple enough; getting it to handle 2,000 engineers hitting a coding assistant with long contexts, all day, without runaway costs, is where teams stall. A working endpoint is only the beginning. Teams need to identify the supporting hardware and wire up the right components--serving, scaling, observability, and cost guardrails--so the deployment can support expected SLAs and SLOs under real, sustained load. DigitalOcean already offers Serverless Inference on the DigitalOcean AI Platform: a fast path to models from OpenAI, Anthropic, Meta, or other providers, with minimal setup and token-based pricing. This offering works well for many use cases. However, when you need your own weights, predictable performance on dedicated GPUs, and economics that favor sustained, high-volume token generation over pay-per-token bursts, a different approach makes sense Dedicated Inference, our managed LLM hosting service on the DigitalOcean AI Platform, fills that gap. Dedicated Inference deploys and operates an opinionated inference stack on dedicated GPUs, with Kubernetes-native orchestration under the hood. You interact through the control plane and APIs you already use in the DigitalOcean ecosystem; the data plane exposes public and private endpoints so applications inside, or outside, your VPC can call your models securely. The service is designed to collapse a vast combinatorial space--GPU SKUs, runtimes, routers, autoscaling policies--into guided defaults so teams hit production milestones faster than DIY stacks, while retaining knobs that matter for model serving: replicas, scaling behavior, and advanced optimizations as you roll out your product roadmap. What we manage vs. what you control Every managed product draws a line between operator-owned and customer-owned concerns. Dedicated Inference aims to put day-two operations--cluster lifecycle integration, ingress, core serving and routing components, and t ## The model is the easy part: Building the LLM Platform at Whatnot DevFeed: [The model is the easy part: Building the LLM Platform at Whatnot]() Original publisher: [Read original article]() Author: Whatnot Engineering Published: 2026-04-14T15:01:05Z Content type: article Language: en Sources: [Whatnot Engineering]() Topics: [AI Platform](), [LLMs](), [AI Development](), [Tooling]() Tags: [ai-agent](), [ai-platform](), [building](), [llm](), [llm-evaluation](), [llms](), [platform](), [platform-engineering](), [production](), [prompt-engineering](), [quality](), [teams](), [trust](), [workflows]() ### AI overview This article explains that building an LLM platform involves much more than calling a model. At Whatnot, the platform is organized around reliability, velocity, and trust, supported by existing data, logging, analytics, integration, and internal tooling foundations. ### Source excerpt Stas Sajin, Faithful Alabi, Peiyun Zhang, Peicheng Yu | AI Platform Introduction A decade ago, one of the more useful ways to explain machine learning systems was with the diagram below: the ML model itself was a tiny box in the middle, surrounded by everything else you actually had to build to make it work in production. Figure 1: Complexity surfaced by ML systems. Source. The same thing is happening with LLMs now. Making the API call is the small box, maybe even smaller than the one in that original diagram. Calling the model is the easy part. The hard part is everything around it: there is less stable ground truth, inputs are harder to constrain, outputs are non-deterministic, and the system is much easier for users to push in unintended directions. The hard part is giving teams the ability to iterate fast, trust that the system is working, and know that it is getting better. From our perspective, this is what an LLM platform actually has to solve for. It has to be reliable enough to support real product and operational workflows. It has to enable velocity, because many of the highest-leverage improvements are small changes that need to move quickly. And it has to create trust, so teams can understand output quality, catch regressions, and ship with confidence. These pillars reinforce each other. Reliability makes teams willing to depend on the platform in production. That production usage creates the data and feedback loops needed to build trust. And trust, in turn, makes it much easier for teams to move with velocity, because they can tell whether a change actually helped. The rest of this post is about those three strategic pillars and the concrete actions behind each of them that allowed us to build the LLM Platform at Whatnot. Figure 2: The LLM platform is organized around three self-reinforcing pillars: velocity, reliability, and trust, each supported by a distinct set of platform enablers.We built on foundations that were already there A big reason we were ## Building a Robust Documentation Agent with DigitalOcean Gradient AI Platform DevFeed: [Building a Robust Documentation Agent with DigitalOcean Gradient AI Platform]() Original publisher: [Read original article]() Author: Anna Lushnikova Published: 2026-04-13T16:59:45Z Content type: article Language: en Sources: [DigitalOcean]() Topics: [Documentation](), [AI Agent](), [AI Platform](), [Prompt Engineering](), [Retrieval Augmented Generation (RAG)](), [CI/CD](), [Digital Ocean]() Tags: [agent](), [agents](), [ai](), [ai-platform](), [ci-cd](), [demo](), [documentation](), [engineering](), [llm](), [prompt-engineering](), [rag]() ### AI overview DigitalOcean describes how it built and shipped a production documentation agent on Gradient AI Platform. The article covers the agent architecture, knowledge-base and retrieval setup, prompt engineering, golden datasets, LLM-judged evaluations, and CI/CD-gated prompt iteration used to improve and monitor answer quality. ### Source excerpt At DigitalOcean, documentation has always been a priority. Developers come to our docs to get unstuck, and the faster they find what they need, the better. Traditional docs pages work, but they require users to know which page to visit, scan for the relevant section, and map generic instructions to their specific setup. That process takes minutes (or longer) when it could take seconds. So we built an AI documentation assistant. Ask a question in plain language, get an answer with working links and ready-to-use commands. Simple enough as a demo. Getting it production-ready was a different story. It took us several iterations to reach an agent we were confident enough to ship. The LLM could generate plausible-sounding answers from day one. Knowing whether those answers were grounded, and keeping them grounded after every model update and prompt change -- that's where we spent most of our time. This post covers what we built, how we validated it, and the specific decisions that moved our metrics from "not great" to "ready for launch." We'll walk through prompt engineering, evaluation pipelines, and the CI/CD glue that holds it all together. Throughout, we relied on DigitalOcean's Agentic Inference Cloud so we could focus on product behavior instead of stitching together inference, RAG, and evaluation tooling ourselves -- one place to run agents, attach knowledge bases, and measure quality, with scale when we need it, and straightforward operational patterns. Architecture Gradient™ AI Platform is the control plane and runtime for standing up production AI agents without assembling pieces by hand. In one place you can attach a knowledge base, define an agent, and tune how it behaves: pick an LLM (managed or open-source), set temperature and top P, choose retrieval behavior, and edit the system prompt. The goal is to get from an empty project to a working agent in minutes, not to wire up inference, RAG, and evaluation from scratch. A Gradient AI Agent is the concrete thing ## How Replicate Handles Billing: A Complete Breakdown DevFeed: [How Replicate Handles Billing: A Complete Breakdown]() Original publisher: [Read original article]() Author: Ayush Agarwal Published: 2026-04-09T00:00:00Z Content type: article Language: en Sources: [Dodo Payments Blog]() Topics: [AI Platforms/Deployment](), [AI Infrastructure](), [GPU](), [Multi-GPU](), [llama](), [stable-diffusion](), [Whisper]() Tags: [ai](), [ai-infrastructure](), [ai-platform](), [analysis](), [billing](), [compute](), [gpu](), [infrastructure](), [llama](), [models](), [multi-gpu](), [stable-diffusion](), [usage-based-billing](), [whisper]() ### AI overview The article analyzes Replicate's usage-based billing model, which charges for compute time by hardware type rather than by subscription, model, or token package. It explains hardware-tier pricing, multi-GPU committed-spend requirements, and model-agnostic billing, and discusses how to implement similar per-second billing for an AI platform. ### Source excerpt A detailed analysis of Replicate's pure usage-based billing model - per-second compute pricing across hardware tiers, cold start costs, and how to build the same pay-per-second infrastructure billing for your own AI platform. ## The Hidden Cost of Complex AI Platforms: Why Developer Experience Matters DevFeed: [The Hidden Cost of Complex AI Platforms: Why Developer Experience Matters]() Original publisher: [Read original article]() Author: Shaoni Mukherjee Published: 2026-04-03T15:44:39Z Content type: article Language: en Sources: [DigitalOcean]() Topics: [AI Platform](), [Developer experience](), [Cloud](), [Inference](), [Deployment]() Tags: [ai](), [ai-ml](), [ai-platform](), [cloud](), [deployment](), [developer-experience](), [engineering](), [inference](), [pipelines](), [scalability](), [troubleshooting]() ### AI overview The article argues that complex AI platforms impose hidden costs through setup friction, unclear documentation, credential configuration, dependency installation, troubleshooting, and fragmented workflows. These issues increase the time required to achieve a working result and can slow team iteration and complicate scaling. ### Source excerpt The cloud AI platform ecosystem today looks more powerful than ever, with access to powerful GPUs like NVIDIA H100 and H200, massive libraries of pre-trained models, and full pipelines for fine-tuning and inference. I recently tried deploying a simple inference endpoint for a model. Ideally, it should have taken a few minutes: provision compute load the model send a request Instead, it took closer to two hours before I got a successful response. Not because the model was difficult to run, but because of everything around it: Figuring out where to start No clear documentation Generating and configuring the right credentials Troubleshooting why the instance wasn't accessible Installing dependencies that weren't preconfigured Retrying after unclear or failed setup steps None of these steps was particularly complex on its own. But together, they created enough friction to delay even a basic task. This pattern shows up often when working with AI platforms today. Most discussions focus on visible costs like: Compute pricing Storage usage API costs But in practice, the higher cost is harder to measure. It's the time spent navigating setup, resolving infrastructure issues, and figuring out how different parts of a platform fit together before any real work begins. Key Takeaways Developer experience is a real cost, not a soft metric: Time lost in setup, debugging, and switching tools directly slows down how fast teams can build and iterate. Most friction comes from fragmented workflows: When model hosting, compute, and deployment live in different places, even simple tasks become multi-step processes. Time-to-First-Value (TTFV) is a critical signal: The longer it takes to get a working output, the more likely teams are to lose momentum or abandon ideas early. Scaling introduces a hidden breaking point: Moving from a simple API to dedicated infrastructure often forces teams to relearn workflows and rebuild systems. This is a systems problem, not a feature gap: Many platform ## DigitalOcean Gradient™ AI Platform Now Integrates with LlamaIndex DevFeed: [DigitalOcean Gradient™ AI Platform Now Integrates with LlamaIndex]() Original publisher: [Read original article]() Author: Narasimha Badrinath Published: 2026-02-18T20:23:52Z Content type: release Language: en Sources: [DigitalOcean]() Topics: [Digital Ocean](), [llamaindex](), [Retrieval Augmented Generation (RAG)](), [AI Platform](), [Large Language Model](), [Embeddings]() Tags: [ai-platform](), [async](), [digitalocean](), [llamaindex](), [product-updates](), [pypi](), [rag](), [streaming]() ### AI overview DigitalOcean Gradient AI Platform now natively integrates with LlamaIndex through two PyPI packages. The integration connects Gradient Knowledge Bases and hosted LLMs to LlamaIndex workflows, supporting hybrid search, metadata filtering, streaming responses, and asynchronous operations. ### Source excerpt We're excited to announce that DigitalOcean Gradient™ AI Platform now integrates natively with LlamaIndex - one of the most popular frameworks for building RAG applications. This means you can now connect your Gradient AI Platform Knowledge Base and LLMs directly to LlamaIndex workflows, using the abstractions you already know. No additional infrastructure. No complex setup. Just install two packages and start building. Why This Matters If you've built RAG applications before, you know the drill: provision a vector database, set up an embedding pipeline, manage credentials across services, and stitch everything together. It's a lot of overhead before you write a single line of application logic. With these new integrations, we've done the heavy lifting. Your Knowledge Base handles document ingestion, chunking, and embeddings. The LlamaIndex retriever connects directly to it. Add our LLM integration, and you have a complete RAG pipeline running on managed DigitalOcean infrastructure. What's New Two packages are now available on PyPI: llama-index-retrievers-digitalocean-gradientai Connect to your Knowledge Base as a LlamaIndex retriever. Supports hybrid search (keyword + semantic), metadata filtering, and async operations. llama-index-llms-digitalocean-gradientai Use Gradient AI Platform-hosted LLMs in your LlamaIndex workflows. Supports streaming responses and async for high-throughput applications. Both packages work with LlamaIndex query engines, chat engines, callbacks, and the broader ecosystem. Get Started in Minutes Install the packages: pip install llama-index-retrievers-digitalocean-gradientai llama-index-llms-digitalocean-gradientai From there, configure your Gradient AI Platform credentials and drop the retriever and LLM into your existing LlamaIndex code. Check out our documentation for a complete walkthrough and code examples. What You Can Build These integrations open up a range of possibilities: Support assistants grounded in your product documentation [Next page]()