# Product updates Published articles for Product updates. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Managing ClickHouse Dedicated Clusters Through Tinybird's Organization UI and API DevFeed: [Managing ClickHouse Dedicated Clusters Through Tinybird's Organization UI and API]() Original publisher: [Read original article]() Author: Aitana Azcona Published: 2026-09-03T15:00:00Z Content type: release Language: en Sources: [Tinybird]() Topics: [clickhouse](), [ui](), [API]() Tags: [api](), [clickhouse](), [product-updates](), [ui]() ### AI overview Tinybird provides controls to observe workload signals, resize Dedicated clusters, and rebalance traffic through its Organization UI or API while it operates ClickHouse. ### Source excerpt Observe workload signals, resize Dedicated clusters, and rebalance traffic from the Organization UI or API while Tinybird operates ClickHouse. ## Introducing v5 Droplets: next-generation performance, sized to your workload DevFeed: [Introducing v5 Droplets: next-generation performance, sized to your workload]() Original publisher: [Read original article]() Author: Krishna Nallamothu Published: 2026-08-26T02:19:47Z Content type: release Language: en Sources: [DigitalOcean]() Topics: [Digital Ocean](), [Hardware](), [cpu](), [AI Platform](), [web applications]() Tags: [agentic-ai](), [amd](), [audio](), [compute](), [digitalocean](), [droplets](), [performance](), [product-updates](), [v5](), [video](), [web-applications]() ### AI overview DigitalOcean announces the general availability of v5 Droplets, built on 5th Gen AMD EPYC processors. The release targets demanding workloads and offers independently configurable vCPU, memory, and storage, with up to 30% higher performance per core than previous-generation Droplets. ### Source excerpt We're excited to introduce v5 Droplets, a new generation of compute built on 5th Gen AMD EPYC™ processors. v5 Droplets are purpose built to deliver higher performance for demanding workloads such as compute-intensive agentic AI platforms, AI/ML tools, high throughput audio/video transcoding, and high-traffic distributed web applications and APIs. v5 Droplets deliver up to 30% higher performance per core than our previous-generation Droplets. For the first time, you can select vCPU, memory, and storage independently and pay for only the resources you choose. Your Droplet fits your application, and your bill reflects exactly what you used, nothing more. You can continue creating bundled Droplets the way you're used to, or choose v5 Droplets for next-generation workload-optimized performance. Designed for workloads that need more More teams are building AI applications, agent platforms, bursty data pipelines, and hosting high-traffic web applications on DigitalOcean than ever before, and those workloads demand higher performance. They need the faster cores, flexible memory ratios, and compute configurations that v5 Droplets provide. Starting with v5, every Droplet is tied to a hardware generation, so you get the same silicon and the same performance every time. You can choose Shared Droplets (s5) for bursty, variable work that doesn't need a full dedicated core, or General purpose Droplets (g5) for guaranteed, dedicated CPU, with memory ratios from 2x to 8x per vCPU. Pricing is simple and based on an hourly rate. You see each resource's price as you configure, a running total as you go, and one line per Droplet on your bill. Early customers running game servers and high throughput e-commerce applications saw 2x performance compared to their existing Droplets. No changes to existing Droplets pricing or experience Every existing Droplet plan stays exactly as it is and maintains the same prices, same bundles, same monthly caps, no migrations, and nothing new on your invoi ## Private Preview: DigitalOcean Managed Agents Runtime Services DevFeed: [Private Preview: DigitalOcean Managed Agents Runtime Services]() Original publisher: [Read original article]() Author: Salman Paracha Published: 2026-08-25T19:01:05Z Content type: release Language: en Sources: [DigitalOcean]() Topics: [Digital Ocean](), [Agent Harness](), [Artificial Intelligence](), [Cloud](), [Claude Code](), [codex](), [Langgraph](), [Command-line interface]() Tags: [agents](), [ai-agents](), [apis](), [claude-code](), [cli](), [cloud](), [codex](), [coding-agents](), [digitalocean](), [gateway](), [harness](), [langgraph](), [preview](), [product-updates](), [services](), [workflows]() ### AI overview DigitalOcean announces Managed Agents Runtime Services (M.A.R.S.) in private preview, combining Harness Runtime and Action Gateway. The managed service provides cloud infrastructure for persistent, scalable agent sessions and governed access to tools, APIs, and SaaS systems. ### Source excerpt AI agents are helping developers, teams, and businesses do more: writing and executing code, conducting research, and running dynamic workflows across systems. But that ability is often bounded by where they run. Close the laptop, and the work stops there. You can't pick it up on another device, hand off to a teammate, or scale it across users. Moving agents to cloud VMs solves part of this problem; developers and companies building agent harness frameworks still have to build a high-fidelity experience that can match a local session, including agent friendly execution environments, session persistence, secure tool access, human-in-the-loop approvals, and observability. Now available in Private Preview, DigitalOcean Managed Agents Runtime Services (M.A.R.S.) provides that infrastructure as a fully managed service. It gives developers, teams, and ISVs a powerful yet lightweight environment for operating coding agents and long-running, multi-tool agentic workflows without building and managing the underlying infrastructure themselves. M.A.R.S. brings together two products: Harness Runtime provides the managed execution environment in which agents run, persist, and scale. Action Gateway gives those same agents governed access to the tools, APIs, and SaaS systems they need to complete real-world work. Rather than requiring you to rebuild your agent around a proprietary framework, M.A.R.S. lets you define an environment template that packages your preferred harness, dependencies, tools, and configuration. Designed to work with Claude Code, Codex CLI, and OpenCode, as well as agents built with LangGraph or CrewAI, giving teams the freedom to choose the agent experience that best fits their needs without being locked into a single harness or framework. Agent sessions that outlive your laptop Harness Runtime helps agent sessions run independently of any local machine. They can start in under a second and resume from a pause in as little as 200 milliseconds while preserving ## Upcoming GPU Pricing Updates DevFeed: [Upcoming GPU Pricing Updates]() Original publisher: [Read original article]() Author: Krishna Nallamothu Published: 2026-07-21T00:30:53Z Content type: release Language: en Sources: [DigitalOcean]() Topics: [Digital Ocean](), [GPU](), [model-deployment]() Tags: [2026](), [amd](), [billing](), [capacity](), [compute](), [cost](), [digitalocean](), [droplets](), [gpu](), [inference](), [infrastructure](), [nvidia](), [pricing](), [product-updates](), [training](), [updates]() ### AI overview DigitalOcean announces price increases for select NVIDIA and AMD GPU droplets effective August 1, 2026. Active workloads will be billed at the new rates, while existing reserved contracts retain their locked-in rates until renewal. ### Source excerpt Effective August 1st, 2026, we will be updating prices on select GPUs. This change reflects strong demand for advanced GPU capacity and helps us expand reliable access to high-performance compute for customers. Even with the updated rates, DigitalOcean continues to offer some of the most competitive GPU infrastructure pricing in the market. Below is a detailed breakdown of these upcoming changes and how they affect you. On-Demand GPU Price Adjustments Effective August 1, 2026, on-demand pricing for NVIDIA and AMD GPU droplets will be updated as follows: What this means for your bill: Any active workloads running on or after August 1, 2026 will be billed at the new rate. By continuing to access or use the services on or after August 1, 2026, you are agreeing to accept and pay the updated rates. These changes will be reflected in your total bill on September 1, 2026. If you do not wish to continue using the service at the updated rate, you will need to take action by August 1, 2026 to destroy your GPU Droplets. 12-Month Reserved GPU Price Adjustments For teams running predictable, continuous training or inference workloads, reserved plans remain the most cost-effective way to lock in lower rates. Effective August 1, 2026, we're also adjusting our 12-month reserved pricing: What this means for your bill: If you're currently in a contract with us that locks in your rate, there is no change to your rate. If you choose to renew after your terms expires, your rates will be adjusted to the new 12-month reserved rate outlined above. If you have questions about how these changes will impact your specific workloads, or if you want to explore reserving capacity, please contact our sales team--we're here to help you find the most cost-efficient path forward. Helpful Resources Visit DigitalOcean pricing for the most up-to-date pricing for GPU Droplets and all DigitalOcean products and services. Visit the billing dashboard for the latest on your account bill. Your use of the Digita ## The DynamoDB Connector is now in Tinybird Forward DevFeed: [The DynamoDB Connector is now in Tinybird Forward]() Original publisher: [Read original article]() Author: Tomás Healy Published: 2026-07-16T11:00:00Z Content type: release Language: en Sources: [Tinybird]() Topics: [DynamoDB]() Tags: [dynamodb](), [product-updates]() ### AI overview Tinybird Forward now supports mirroring DynamoDB tables using a .connection file and a single deploy. The change addresses a migration gap for Tinybird Classic users. ### Source excerpt Mirror your DynamoDB tables to Tinybird Forward with a .connection file and a single deploy. For Classic users, this closes the last gap to migrating. ## Scale Faster with Managed Weaviate: Now in Public Preview on DigitalOcean DevFeed: [Scale Faster with Managed Weaviate: Now in Public Preview on DigitalOcean]() Original publisher: [Read original article]() Author: Waverly Swinton Published: 2026-07-09T19:08:52Z Content type: release Language: en Sources: [DigitalOcean]() Topics: [Digital Ocean](), [Cloud](), [Databases](), [Retrieval Augmented Generation (RAG)](), [autoscaling](), [Self-hosted](), [GraphQL](), [gRPC]() Tags: [autoscaling](), [configuration](), [database](), [databases](), [digitalocean](), [graphql](), [high-availability](), [hosting](), [infrastructure](), [preview](), [product-updates](), [real-time](), [rest](), [retrieval](), [run](), [security](), [storage](), [upgrades](), [workflows]() ### AI overview DigitalOcean has placed Managed Weaviate into public preview. The service provides fully managed Weaviate clusters with automated backups, security patching, version upgrades, high availability, and storage autoscaling, with pricing starting at $20 per month. ### Source excerpt Production Weaviate in minutes, managed by DigitalOcean. Starting at $20/month. Vector databases have become a core piece of the AI application stack. Whether you're building retrieval-augmented generation (RAG), semantic search, agentic workflows and memory, or similarity-based recommendations, you need a vector store that's reliable, fast, and doesn't require a dedicated ops engineer to keep running. Weaviate has become a critical part of that stack -- its open-source AI-native vector database powers semantic search, RAG, and agentic workflows for thousands of companies. Self-hosting Weaviate is doable but it comes at a cost. You're on the hook for backups, version upgrades, security patches, high availability configuration, and storage scaling. That's real time and real engineering capacity that isn't going toward your product. Managed alternatives from larger cloud vendors exist, but they often come with per-query fees, per-dimension surcharges, and pricing models that are difficult to predict as usage grows. Today, we're announcing that Managed Weaviate is now in public preview on DigitalOcean, offering an easy way for you to run Weaviate in production, at a price that makes sense from day one. The easiest way to run Weaviate in production Managed Weaviate on DigitalOcean handles the operational work so you don't have to. Provision a fully managed Weaviate cluster directly from the DigitalOcean control panel. From there, automated backups, security patching, version upgrades, high availability, and storage autoscaling are handled for you. Full Weaviate client compatibility via GraphQL, REST, and gRPC on port 443 means your existing code works without modification. This means you get Weaviate's full capabilities -- semantic and hybrid search, RAG pipelines, and support for agent-driven workflows -- without spending engineering time on the infrastructure beneath them. Predictable pricing, starting at $20/month We built Managed Weaviate with flat, predictable monthly ## Harness June 2026 Product Updates DevFeed: [Harness June 2026 Product Updates]() Original publisher: [Read original article]() Author: Chinmay Gaikwad Published: 2026-07-03T00:00:00Z Content type: release Language: en Sources: [Harness Blog]() Topics: [Automation](), [Artificial Intelligence](), [AI Engineering](), [Playwright](), [Test automation](), [Testing]() Tags: [2026](), [agents](), [ai](), [ai-agents](), [ai-engineering](), [automation](), [playwright](), [product-updates](), [release](), [test-automation]() ### AI overview Harness's June 2026 product updates included 62 features, including Autonomous Worker Agents, parallel DAG pipeline execution, AI Test Automation with Playwright, AI SAST, feature flag updates, and AI Engineering Insights. ### Source excerpt See everything Harness shipped in June 2026, including Autonomous Worker Agents, parallel DAG pipelines, AI Test Automation with Playwright, AI SAST, feature fl | Blog ## DigitalOcean Evaluations: Production Model and Router Testing for the Inference Stack DevFeed: [DigitalOcean Evaluations: Production Model and Router Testing for the Inference Stack]() Original publisher: [Read original article]() Author: Grace Morgan Published: 2026-07-01T15:41:47Z Content type: release Language: en Sources: [DigitalOcean]() Topics: [Digital Ocean](), [inference-endpoints](), [Large Language Model](), [model-deployment](), [Testing]() Tags: [configuration](), [cost](), [data](), [dataset](), [digitalocean](), [evaluation](), [inference](), [inference-endpoints](), [latency](), [llm](), [metric](), [model](), [models](), [performance](), [pii](), [product-updates](), [production](), [quality](), [router](), [testing](), [token]() ### AI overview DigitalOcean Evaluations adds production testing for models and inference router configurations in the DigitalOcean Inference Engine. Teams can run LLM-as-a-Judge evaluations on their own prompts and data, compare quality, latency, and cost, and use built-in or custom rubrics across models, imports, and router setups. ### Source excerpt Choosing the right model or inference router for production means more than reading a leaderboard. It means validating any model or routing configuration on your own data using your prompts and your evaluation criteria before it ever reaches production, and comparing quality, latency, and cost in one place. Evaluations, now available on the DigitalOcean Inference Engine, lets teams validate any model or inference router configuration on their own data before production. Run structured LLM-as-a-Judge evaluations across catalog models, fine-tuned models, BYOM imports, and router setups without stitching together a separate evaluation stack. DigitalOcean Evaluations Capabilities Evaluations provide everything teams need to validate model and router performance before production. LLM-as-a-Judge scoring runs across any candidate in your inference stack and returns per-item scores with judge rationale, plus latency, token, and cost tracking per run. Six pre-built metrics cover the most common evaluation needs out of the box. For teams that need full control: custom rubrics, reusable presets, MCP support, and full dataset management -- all in the same platform as the inference endpoints you use in production. View YouTube video Pre-Built and Custom Rubrics: Score Against Criteria That Match Your Domain The six pre-built metrics, correctness, completeness, faithfulness, PII, toxicity, and bias, cover common evaluation needs. For specialized domains, custom rubrics let teams define their own judge instructions and scoring criteria directly in the judge prompt. The judge evaluates responses against these criteria and returns per-item scores with rationale. Custom rubrics can also adapt the built-in correctness metric to different data formats instead of relying on a default interpretation. Evaluation Presets: Save Configurations and Re-Run Without Rebuilding Without saved configurations, every re-run becomes a rebuild with different judge models, parameters, or prompts, making ## Lightweight deletes for Tinybird Data Sources now in Beta DevFeed: [Lightweight deletes for Tinybird Data Sources now in Beta]() Original publisher: [Read original article]() Author: Jesús Botella Published: 2026-06-30T12:00:00Z Content type: release Language: en Sources: [Tinybird]() Topics: [clickhouse](), [data]() Tags: [clickhouse](), [data](), [partition](), [product-updates]() ### AI overview Tinybird has added a beta endpoint for ClickHouse lightweight DELETE operations. It supports synchronous deletion and asynchronous deletion with polling for partition progress. ### Source excerpt A new endpoint exposes ClickHouse's lightweight DELETE functionality in Tinybird: pick between a synchronous call that blocks until the delete is done, or an asynchronous one that you poll for partition progress. ## Run Codex in the cloud - DigitalOcean for Codex is now available DevFeed: [Run Codex in the cloud - DigitalOcean for Codex is now available]() Original publisher: [Read original article]() Author: Ari Sigal Published: 2026-06-25T21:16:06Z Content type: release Language: en Sources: [DigitalOcean]() Topics: [codex](), [Digital Ocean](), [remote-development](), [Cloud](), [Provisioning](), [ssh](), [ChatGPT]() Tags: [ai-ml](), [cloud](), [codex](), [digitalocean](), [mobile](), [oauth](), [plugin](), [preview](), [product-updates](), [provisioning](), [remote-development](), [ssh]() ### AI overview DigitalOcean announces a Public Preview plugin for Codex that lets developers provision persistent, Codex-ready cloud development machines in their own DigitalOcean accounts using natural-language prompts. The resulting Droplets include the Codex CLI, common programming-language tooling, and SSH access. ### Source excerpt As your agents are working on more complex, long-running work, they need a clean, persistent environment to keep running. Setting up a persistent remote machine by hand means creating a cloud server, configuring SSH keys, installing dependencies, and wiring everything back to your workflow. It's a lot of infrastructure work before you write a single line of code. Today, we're making that easier. The DigitalOcean plugin for Codex is now available in Public Preview, letting developers create and connect Codex-ready cloud development machines in their own DigitalOcean account directly from within Codex -- using natural language, with no manual setup. This means that not only can your work continue running when you step away, but with Codex in the ChatGPT mobile app you can stay in control -- starting, steering, or monitoring work from wherever you are. What is DigitalOcean for Codex? The DigitalOcean plugin connects your DigitalOcean account to the Codex app, letting you provision a persistent remote development environment on demand. Instead of manually spinning up a server and configuring it, you can ask Codex to do it. The result: a DigitalOcean Droplet® that's pre-configured with the Codex CLI, common programming language tooling (based on the codex-universal Docker image), and SSH access -- so your work can keep running and stay within reach, even when you're not at your desk. How it works Starting from Codex Install the DigitalOcean plugin from the Codex plugin directory. During installation, you'll connect your DigitalOcean account via OAuth -- no API tokens to create or paste. Then prompt: @DigitalOcean create a new remote machine Codex will: Provision a new Droplet from the Codex Droplet template Generate and configure an SSH key on your device Wait for the machine to finish provisioning (and check back automatically when it's ready) Return a deeplink to the Codex SSH connections page to finalize the connection Once connected, you're running Codex on a persistent ## Server-Side Tools Are Now Available for DigitalOcean Inference Engine DevFeed: [Server-Side Tools Are Now Available for DigitalOcean Inference Engine]() Original publisher: [Read original article]() Author: Grace Morgan Published: 2026-06-17T16:33:00Z Content type: release Language: en Sources: [DigitalOcean]() Topics: [Digital Ocean](), [Inference](), [Artificial Intelligence](), [Model Context Protocol](), [Web](), [Tool](), [real-time]() Tags: [agent](), [agentic](), [digitalocean](), [fetch](), [inference](), [mcp](), [preview](), [product-updates](), [search](), [web]() ### AI overview DigitalOcean announces that Server-Side Tools are available in Public Preview for its Inference Engine. The tools let models use web search, web fetch, DigitalOcean Knowledge Bases, MCP servers, and supported Anthropic and OpenAI tools within inference requests. ### Source excerpt AI applications and agents are only as capable as the tools, data, and systems they can access. With Server-Side Tools, now in Public Preview for DigitalOcean Inference Engine, a model can call out to search the web, read your data, call your systems, and take action all from inside a single inference request. You can enable the new tools with your existing DigitalOcean Model Access Key. No separate tool infrastructure to assemble, no new credentials, no orchestration layer to operate. Server-Side Tools bring web search, web fetch, DigitalOcean Knowledge Bases, MCP servers, and supported Anthropic and OpenAI tools into your inference requests, each covered below. Bring Real-Time Information Into Your AI Applications When applications need current information such as news, documentation, or live data, models can access the web directly during inference. Web Search: Get live answers from the web Web Search enables retrieval of up-to-date information from the web. This enables research workflows, support experiences, and agentic applications that need to reason over recent events, changing information, or content that is not available in a model's training data. Web Fetch: Pull in content from URLs and documents Web Fetch pulls in content from specific URLs or PDFs during inference. It is useful for summarizing pages, extracting structured data from documents, or pulling in reference material on demand. Both Web Search and Web Fetch are powered by Exa. Pricing is usage-based; see the pricing page for current rates. Web Mode: Enable web access through the model URL Some agent frameworks only allow you to configure a model name and do not expose tool configuration. For these cases, DigitalOcean supports Web Mode, which automatically enables Web Search and Web Fetch through the model field. This gives the model access to Web Search and Web Fetch without explicitly defining tools, making it easier to integrate with agent frameworks that only allow model-level configuration ## DigitalOcean Model Evaluations Public Preview for Comparing Inference Strategies DevFeed: [DigitalOcean Model Evaluations Public Preview for Comparing Inference Strategies]() Original publisher: [Read original article]() Author: Sathish Jothikumar Published: 2026-06-04T19:52:49Z Content type: tutorial Language: en Sources: [DigitalOcean]() Topics: [Digital Ocean](), [LLM evaluation / benchmarking](), [Inference]() Tags: [digitalocean](), [evaluation](), [inference](), [product-updates](), [routing]() ### AI overview A guide to using DigitalOcean Model Evaluations in Public Preview to compare models and routing strategies by output quality, latency, and cost before changing production inference traffic. ### Source excerpt Most teams running inference at scale do not fail because they cannot find a "good" model. They fail because they ship a routing policy that looks fine in a playground, but drifts the moment it sees real prompts, real latency tails, and real per-token cost. The routing policy breaks on the prompts you never tested and your users find out before you do. Now you can use Model Evaluations, available in Public Preview on the DigitalOcean Inference Engine, to evaluate models available on the platform, or models that you have imported from Hugging Face or DigitalOcean Spaces. Model Evaluations helps you make comparable, reproducible decisions across models, routing strategies, cost, latency, and output quality. In this guide, we walk through setting up, running, and interpreting a Model Evaluation across three inference strategies: using a single frontier model for every request, deploying a task-specific fine-tuned model, or using the Inference Router with a cost- or latency-optimized policy. The goal is simple: determine which approach performs best on your workload before you change production traffic. The scenario Let's say you are running a legal-adjacent assistant (think contract summarization, clause extraction, policy Q&A). You currently call one expensive frontier model for every request as you believe it is the most accurate. Your CFO sees inference as COGS whereas your users see latency and p95 as key metrics on long documents. The Inference Router is attractive: it can send "easy" work to a cheaper or faster model and keep the heavy lifter for edge cases, if the routing policy is aligned with your use case. Your evaluation job is to compare these three candidates on the same dataset, using the same judge and metrics, so the results are directly comparable: Endpoint Candidate What you are really testing Serverless Inference anthropic-claude-4.6-sonnet Single "always frontier" model (your baseline) Inference Router model-eval-blog-legal An Inference Router confi ## DigitalOcean's unified data and retrieval layer for AI applications DevFeed: [DigitalOcean's unified data and retrieval layer for AI applications]() Original publisher: [Read original article]() Author: Spoorthi Rao Nimmala Published: 2026-06-03T19:23:28Z Content type: article Language: en Sources: [DigitalOcean]() Topics: [Digital Ocean](), [Artificial Intelligence](), [data](), [Inference](), [PostgreSQL](), [MySQL](), [Latency](), [AI search]() Tags: [ai](), [data](), [digitalocean](), [inference](), [latency](), [multimodal](), [mysql](), [postgresql](), [product-updates](), [real-time](), [search](), [vector-search]() ### AI overview DigitalOcean describes a Data & Learning layer that combines structured transactional data, vector search, and retrieval tools for AI applications. The platform includes Managed PostgreSQL Advanced, MySQL Advanced Edition, Knowledge Bases, and Managed Weaviate, with integrations intended to support real-time, multimodal pipelines and grounded inference. ### Source excerpt Building an AI-native application requires a data layer that can do two things at once: handle the structured, transactional queries your application runs on, and understand meaning well enough to power semantic search across unstructured content. An AI application needs both -- precise SQL for account balances and transaction records, and vector search to surface conceptually related patterns, anomalies, or past cases that a keyword query would never find. Most teams end up stitching these together across different environments, where every query crosses a boundary. Latency compounds and costs grow with the complexity of the glue, not the value of the data. What holds together in a prototype starts to fracture under production load. The DigitalOcean Data & Learning layer is designed to close that gap by giving you structured, vector, and retrieval layers that work together in the same ecosystem. Real-Time Inference and Learning At the heart of any sophisticated AI application is the need for grounded, context-aware inference. DigitalOcean now supports a unified set of tools across the data layer: Managed PostgreSQL Advanced and MySQL Advanced Edition (Public Preview) for the structured, transactional data your application runs on Knowledge Bases (General Availability) to handle the full retrieval pipeline from ingestion to answer Managed Weaviate (Public Preview) for vector search on unstructured data Together, this unified platform allows developers to build real-time multimodal pipelines and manage enterprise knowledge bases with ease. Every retrieval your application or agent makes flows through this layer. When the data and retrieval layer is fully managed and scales with the application, your agent's answers stay grounded and your service stays available. These services run on the same platform as DigitalOcean's Inference Engine and Managed Agent infrastructure. This means zero egress between the data layer and inference, one billing relationship instead of thr ## Harness May 2026 Product Updates: 60+ New Features DevFeed: [Harness May 2026 Product Updates: 60+ New Features]() Original publisher: [Read original article]() Author: Chinmay Gaikwad Published: 2026-06-03T00:00:00Z Content type: article Language: en Sources: [Harness Blog]() Topics: [MCP](), [MCP Server](), [Development](), [Artificial Intelligence](), [ai-coding](), [Security](), [Claude]() Tags: [2026](), [agentic](), [ai](), [ai-coding](), [ai-infrastructure](), [api](), [claude](), [claude-code](), [coding-assistant](), [connectors](), [cves](), [development](), [features](), [infrastructure](), [integration](), [mcp](), [mcp-server](), [product-updates](), [security](), [updates]() ### AI overview Harness reports more than 60 product updates shipped in May 2026 across AI-native development, software delivery, security, cost management, and engineering insights. Highlights include AI cost tracking, AI adoption metrics, Claude connector access, and expanded MCP Server capabilities. ### Source excerpt See 60+ Harness updates from May 2026 across AI-native development, software delivery, security, artifact management, cost visibility, and engineering insights. | Blog ## New Tinybird Forward docs: one level of nesting and SDKs toggle DevFeed: [New Tinybird Forward docs: one level of nesting and SDKs toggle]() Original publisher: [Read original article]() Author: Gonzalo Gómez, Arístides Pérez Published: 2026-06-02T00:00:00Z Content type: release Language: en Sources: [Tinybird]() Topics: [Documentation](), [SDKs](), [Command-line interface](), [Python](), [TypeScript]() Tags: [cli](), [docs](), [product-updates](), [python](), [sdks](), [typescript]() ### AI overview Tinybird restructured its documentation around developer workflows, with sections for quickstarts, development workflows, core concepts, actions, and guides. The documentation lets readers choose CLI, TypeScript SDK, or Python SDK and follow examples aligned with that choice. ### Source excerpt We restructured the Tinybird docs around how developers actually use them: quickstart, dev workflow, core concepts, actions, and a flat pile of guides. Pick CLI, TypeScript SDK, or Python SDK once, then examples follow that choice across the docs. ## OpenCode Now Supports DigitalOcean Inference Router for Intelligent Model Routing DevFeed: [OpenCode Now Supports DigitalOcean Inference Router for Intelligent Model Routing]() Original publisher: [Read original article]() Author: Musa Malik Published: 2026-05-28T21:02:42Z Content type: release Language: en Sources: [DigitalOcean]() Topics: [Digital Ocean](), [Model Routing](), [AI Inference](), [AI-assisted coding](), [Open Source](), [coding]() Tags: [ai-coding](), [ai-inference](), [api](), [cost](), [digitalocean](), [latency](), [model-routing](), [open-source](), [product-updates]() ### AI overview DigitalOcean's Inference Router is in public preview and can be accessed through OpenCode, an open-source AI coding agent. It dynamically routes requests across models to help developers manage latency, cost, and output quality. ### Source excerpt Coding agents today have a massive spending problem. Every request, whether you're designing system architecture or writing a single-line docstring, often gets routed to the same expensive frontier model. The result: unnecessary token usage, higher inference costs, and little awareness of task complexity or budget constraints. This high cost stems from a "one-size-fits-all" approach to model usage, where premium frontier models are utilized for trivial tasks that don't require such intensive reasoning effort. In multi-agent workflows, where orchestrators delegate work to specialized subagents, this lack of discrimination frequently leads to runaway costs and opaque failure modes. Without intelligent routing, developers can essentially be forced into closed-provider lock-in and high API usage fees, which quickly escalate during exploratory building phases. DigitalOcean Inference Router, now in Public Preview, was built to solve this problem by dynamically routing requests to the right model for the job. As part of DigitalOcean's AI-Native Cloud, it gives developers a unified way to control, optimize, and evaluate AI inference across models. And as of today, you can access it through OpenCode, the open-source AI coding agent, in as little as a few seconds. What is an Inference Router? An Inference Router is the auto-mode pattern engineers are used to, but with deliberate control over the tradeoffs that matter: latency, cost, and output quality. Rather than statically pointing your coding agent to a single model, an Inference Router can analyze each request and route it to the model best suited for that specific task. Not the most powerful model available, but the right model. That distinction is what drives real savings without compromising on your desired quality of output. To use DigitalOcean's Inference Router: Create an Inference Router from the router catalog--pick a preset or build a custom router via the API or UI. No GPU management, no infrastructure to run. Us ## Temporal product releases and community highlights from May 2026 DevFeed: [Temporal product releases and community highlights from May 2026]() Original publisher: [Read original article]() Author: Temporal Technologies Published: 2026-05-28T00:00:00Z Content type: article Language: en Sources: [Temporal Blog]() Topics: [releases](), [AWS Lambda](), [Serverless](), [SDKs](), [Kubernetes]() Tags: [announcements](), [builder](), [community](), [launches](), [product-updates]() ### AI overview Temporal's May digest covers product releases and previews, including serverless Workers on AWS Lambda, Task Queue Priority and Fairness, Standalone Activities, Capacity Modes, Worker Versioning, and the Temporal Worker Controller. It also highlights Replay '26 sessions, livestreams, community projects, and builder spotlights. ### Source excerpt Major launches unveiled at Replay '26, speaker sessions now on demand, a new livestream series, product updates, community projects, and builder spotlights ## DigitalOcean Introduces Batch Inference for High-Volume AI Workloads DevFeed: [DigitalOcean Introduces Batch Inference for High-Volume AI Workloads]() Original publisher: [Read original article]() Author: smirza Published: 2026-05-27T17:43:40Z Content type: release Language: en Sources: [DigitalOcean]() Topics: [Digital Ocean](), [Inference](), [Artificial Intelligence](), [API](), [anthropic](), [OpenAI]() Tags: [ai](), [anthropic](), [api](), [batch](), [cost](), [digitalocean](), [inference](), [openai](), [product-updates]() ### AI overview DigitalOcean introduces Batch Inference for asynchronous processing of high-volume AI workloads through a unified API using OpenAI and Anthropic models. The company says it can reduce inference costs by up to 50%. ### Source excerpt At Deploy 2026, we introduced the DigitalOcean AI-Native Cloud, built for the inference era. Batch Inference on the DigitalOcean Inference Engine enables high-volume asynchronous workloads. As developers move from AI prototypes to production-scale applications, the challenges of cost and rate limits often become a bottleneck. Batch Inference addresses these hurdles by allowing you to process high-volume workloads asynchronously at a fraction of the cost of synchronous requests. Whether you are performing large-scale data transformation, content generation, building embeddings or offline evaluations, Batch Inference provides a unified, consistent way to leverage the world's leading models from OpenAI and Anthropic, all through a single DigitalOcean interface. The AI Scaling Bottleneck Real-time inference is essential for interactive AI applications such as chatbots, copilots, and search-as-you-type experiences. However, when the task involves processing 10,000 support tickets for sentiment analysis, generating SEO metadata for an entire product catalog, or benchmarking a new system prompt against a test suite, real-time inference becomes an expensive and inefficient tool for the job. Each of those requests competes for the same rate-limited throughput as your production traffic. Teams spend engineering time writing retry logic, managing backpressure, and monitoring scripts that work through sequential API calls for hours. If you use models from multiple providers, such as OpenAI for embeddings and Anthropic for generation, you are managing separate credentials, separate billing dashboards, and separate error-handling strategies, even though the core workflow is the same: submit requests, wait, retrieve results. Processing thousands of synchronous requests is not only slow, it is an architectural challenge. At scale, synchronous inference becomes inefficient requiring thousands of open connections, creating constant rate-limit pressure and wasting compute while waitin ## Request-Based Autoscaling Is Now Generally Available on App Platform DevFeed: [Request-Based Autoscaling Is Now Generally Available on App Platform]() Original publisher: [Read original article]() Author: Greeshma Pillai Published: 2026-05-22T18:02:26Z Content type: release Language: en Sources: [DigitalOcean]() Topics: [autoscaling](), [Digital Ocean](), [App](), [Latency](), [Containers]() Tags: [autoscaling](), [capacity](), [container](), [containers](), [cpu](), [digitalocean](), [http](), [load](), [performance](), [product-launch](), [product-updates](), [production](), [real-time]() ### AI overview DigitalOcean App Platform now generally supports request-based autoscaling for shared and dedicated CPU instances. Apps can scale horizontally using live HTTP requests per second and P95 response latency, with containers scaling up when thresholds are exceeded and down when load falls. ### Source excerpt Traffic doesn't spike on a schedule. A product launch, a viral moment, or a flash sale can send request volume through the roof in seconds, long before your CPU metrics catch up. That gap is where performance suffers. Today, we're excited to announce that request-based autoscaling on DigitalOcean App Platform is now generally available. Your apps can now automatically scale based on live HTTP traffic signals (requests per second and P95 response latency) so your infrastructure reacts to what's actually happening, not what happened minutes ago. Now Available for Shared and Dedicated CPU Instances Until now, autoscaling on App Platform required a dedicated CPU plan. That meant a good portion of App Platform users (anyone running on shared CPU instances) had no path to automatic horizontal scaling at all. That changes today. Request-based autoscaling works on both shared and dedicated CPU instances. Whether you're running an early-stage project on a shared plan or a high-throughput production service on dedicated resources, you can now configure autoscaling to match your traffic--no plan upgrade required. Faster, More Responsive Scaling CPU-based autoscaling is reactive by nature. CPU is a lagging indicator: your containers have to be visibly struggling before the scaler knows there's a problem, and by then, your users are already waiting. Request-based autoscaling acts on the signals that actually reflect user experience: Requests per second per instance: how many requests each container is handling right now P95 request latency: the response time that 95% of your users are seeing When traffic rises and either threshold is exceeded, new containers spin up immediately. When load drops and all metrics fall back below their targets, the scaler brings containers back down. You get the capacity headroom you need, faster, and pay only for what you use. You can also combine request-based and CPU-based metrics on dedicated plans. The autoscaler scales up when any configured th ## How We Built DigitalOcean Inference Router DevFeed: [How We Built DigitalOcean Inference Router]() Original publisher: [Read original article]() Author: Adil Hafeez Published: 2026-05-20T14:57:13Z Content type: article Language: en Sources: [DigitalOcean]() Topics: [Model Routing](), [Digital Ocean](), [Inference](), [Large Language Model](), [Multi Agent Systems]() Tags: [agentic-workflows](), [digitalocean](), [engineering](), [inference](), [llm](), [multi-agent-systems](), [product-updates](), [routing]() ### AI overview DigitalOcean describes its Inference Router, which uses purpose-built models and live metrics to route LLM requests to appropriate models. The article explains how routing can reduce the cost and maintenance burden of applying one frontier model uniformly across coding, analysis, debugging, and other agentic tasks. ### Source excerpt Most teams building on LLMs today make a single model decision and apply it uniformly across every request. They reach for a frontier model not because every task demands it, but because building the infrastructure to do anything smarter is hard, time-consuming, and easy to get wrong. When the tooling isn't there, the path of least resistance is to use a single model, even if it means that you end up overpaying for most tasks. Let's take an example. If you're a developer building with Cursor, Claude Code, Open Code or any coding agent today, you've already felt this. In a single session, your agent does deep codebase analysis, writes new functions, fixes bugs from test output, explains methods, searches documentation. These tasks are not equivalent but if you're on a single hardcoded model, you're paying frontier rates for all of them, including the ones that don't need it. The stakes are even higher in agentic workflows and multi-agent systems. When multiple agents are running in parallel each planning, executing, and evaluating across long-horizon tasks the cost of uniform model selection compounds with every step. Furthermore, major AI providers are moving toward token-based billing and tighter rate limits. Inference costs are about to get more expensive. The alternative is for hardcoded routing logic in the application layer with an intent classifier with the help of an LLM which adds to your cost and gets brittle fast. Even if you were to use a smaller model like Haiku to keep costs down, you're now paying for a routing call on top of every inference call. Also, accuracy takes a hit as the model is not purpose built for routing, and as your task types evolve or models change, the logic breaks in ways that are hard to catch. You've introduced double taxation: the cost of the classifier plus the cost of maintaining brittle routing code that needs updating with every change to your stack. You've turned model selection into a feature you own and maintain, which is ## DigitalOcean Launches AI-Native Cloud for Inference and Agentic Workloads DevFeed: [DigitalOcean Launches AI-Native Cloud for Inference and Agentic Workloads]() Original publisher: [Read original article]() Author: Vinay Kumar, Chief Product & Technology Officer Published: 2026-05-04T20:43:38Z Content type: release Language: en Sources: [DigitalOcean]() Topics: [Digital Ocean](), [Artificial Intelligence](), [Cloud](), [Inference](), [AI Infrastructure](), [cloud-infrastructure](), [Open Source]() Tags: [ai](), [ai-infrastructure](), [cloud](), [cloud-infrastructure](), [digitalocean](), [inference](), [open-source](), [product-updates]() ### AI overview DigitalOcean launched its AI-Native Cloud, a platform designed for inference and agentic workloads. The platform combines five layers, including managed agents, data and learning services, an inference engine, core cloud infrastructure, and DigitalOcean-owned silicon and facilities. ### Source excerpt I've spent the last fifteen years building cloud services: early days of AWS building S3 and EBS, helping launch Oracle Cloud Infrastructure from inception, and now building the agentic cloud at DigitalOcean for AI-natives. Every cloud I've worked on was designed for the workloads of its era. Those clouds were built for human-centric SaaS applications: a few users, a handful of requests per session, predictable data flows. AI workloads break every one of those assumptions. AI runs in loops. Agents think, then act, then think again. A single user task can span hundreds of thousands of tokens, traverse half a dozen tools, hit a knowledge base, write code, execute it, and persist state, all before returning an answer. The clouds we have weren't built for this. Hyperscalers give you hundreds of services built for yesterday's applications, and leave the integration to you. Inference-only providers sit on someone else's compute and stack their margin on top. GPU rental shops (frequently referred to as "Neoclouds") give you silicon, but not a system. This week at Deploy 2026, we launched the DigitalOcean AI-Native Cloud, a purpose-built platform for the inference and agentic era that integrates five layers from silicon to agents into a single open stack. We shipped fifteen products on Tuesday. Here's what's inside. The shape of the stack Our AI-Native Cloud is composed of five layers, each addressing a real workload pattern we've watched our customers wrestle with. They're independently useful and beautifully integrated: Managed Agents: production runtime for agents, with sandboxes, durable state, and a universal data plane Data & Learning: managed databases, vector stores, knowledge bases, and feedback loops Inference Engine: every open and frontier model on one endpoint, optimized at the kernel Core Cloud: compute, networking, and storage primitives, tuned for AI Infrastructure: DigitalOcean-owned silicon and facilities, co-engineered with the industry's best Open source ## Introducing DigitalOcean AI-Native Cloud for Production AI Workloads DevFeed: [Introducing DigitalOcean AI-Native Cloud for Production AI Workloads]() Original publisher: [Read original article]() Author: Paddy Srinivasan Published: 2026-04-28T19:14:06Z Content type: release Language: en Sources: [DigitalOcean]() Topics: [Digital Ocean](), [Cloud](), [Artificial Intelligence](), [Inference](), [model-deployment](), [Open Source]() Tags: [ai](), [cloud](), [digitalocean](), [inference](), [inference-providers](), [open-source](), [product-updates](), [production]() ### AI overview DigitalOcean introduced its AI-Native Cloud at Deploy 2026, describing it as a full-stack system for production AI workloads. The offering builds on DigitalOcean's cloud infrastructure and adds capabilities for AI systems, with a stated goal of reducing the complexity of assembling separate services. ### Source excerpt The AI industry has a compounding bottleneck, and it isn't the models. It's inference. What used to be a single model call has become a system of continuous interaction. Applications now orchestrate multiple models, retrieve and synthesize data, execute tools, and repeat this cycle in production. These are no longer stateless requests. They are dynamic systems that behave more like infrastructure than software features. Four shifts are redefining what infrastructure has to do: Inference has overtaken training as the center of gravity Reasoning models are becoming the default Autonomous agents are running at scale Open-source models are reaching quality parity at a fraction of the cost Most stacks were never designed for this. Hyperscalers expose hundreds of services that still need to be stitched together. Inference providers sit on top of someone else's compute, adding another layer of margin. GPU vendors give you silicon, but not a system. Inference has quietly become the most expensive and least owned layer of the modern stack. Every new capability is layered onto a fragmented foundation, and complexity compounds underneath it. Eventually, you stop having a model problem. You have a stack problem. Today at Deploy 2026, we revealed DigitalOcean's AI-Native Cloud, a full-stack system for production AI workloads. Our AI-Native Cloud builds on DigitalOcean's core cloud across compute, storage, networking, and managed services, and extends it with capabilities designed for how AI systems actually run in production. The goal is simple: reduce the stack so builders can focus on building, not stitching systems together. Open source is not an add-on here, it's the foundation. We removed unnecessary abstraction layers, eliminated margin stacking across vendors, and gave developers direct access to the primitives they need to build and scale AI systems. This isn't theoretical. Customers like Workato run a trillion automation tasks on DigitalOcean at 67% lower cost. Characte ## Temporal product updates and integrations in April DevFeed: [Temporal product updates and integrations in April]() Original publisher: [Read original article]() Author: Temporal Technologies Published: 2026-04-28T00:00:00Z Content type: article Language: en Sources: [Temporal Blog]() Topics: [releases](), [SDKs](), [Replication](), [SIEM, Security, Observability](), [distributed-systems](), [cursor](), [Amazon S3](), [Google](), [.NET](), [OpenAI](), [Python](), [TypeScript]() Tags: [amazon-s3](), [announcements](), [blog](), [blog-post](), [cloud](), [cursor](), [integrations](), [multi-cloud](), [observability](), [product-updates](), [updates]() ### AI overview Temporal's April digest summarizes product updates and integrations, including OpenAI Agents SDK and Google ADK integrations, replication features, observability metrics, external storage, and a Cursor plugin. ### Source excerpt Highlights from April include new product updates, integrations, and builder spotlights from folks pushing Temporal in creative directions. ## Tinybird is now available in AWS ap-southeast-2 (Sydney) DevFeed: [Tinybird is now available in AWS ap-southeast-2 (Sydney)]() Original publisher: [Read original article]() Author: Tinybird Published: 2026-04-14T00:00:00Z Content type: release Language: en Sources: [Tinybird]() Topics: [Amazon Web Services](), [Latency](), [data]() Tags: [australia](), [aws](), [data](), [latency](), [product-updates]() ### AI overview Tinybird now supports AWS ap-southeast-2 in Sydney, providing lower latency for workloads in Australia and Oceania and data residency in Sydney. ### Source excerpt Tinybird now supports AWS ap-southeast-2. Lower latency for Australia and Oceania workloads and data residency in Sydney. [Next page]()