A personal update. My grandmother passed away today, and I’m going through a lot right now. I’ll be taking 2–3 days to be with my family and process everything. The latest Avenro update was already completed and has been pushed. I’ll be back in a few days and will continue from where we left off. Thank you for your patience and understanding. I’ll be back stronger. Take care of yourselves and your loved ones.

Avenro Update — Spending Controls and Execution Reliability is now shipped. This update brings five important improvements to how developers manage inference spending and monitor agent execution. → Daily and monthly budgets: API-enforced spending limits for agent-used API keys. → Budget warnings: Configurable alerts when usage reaches defined thresholds or a budget limit. → Remaining-budget access: Applications can check their spending limits and remaining budget programmatically. → Failover visibility: Request history and exports show which model was requested and which model ultimately responded. → Provider health monitoring: Better visibility into provider availability and responsiveness. Together, these capabilities give developers greater control over spending, clearer insight into request execution, and better visibility into provider health. The focus is on making agent-driven applications more predictable and easier to manage. Phase 3: shipped.

A closer look at Avenro’s upcoming Spending Controls and Execution Reliability update. When an AI agent operates autonomously, developers need more than access to inference. They need to know how much it can spend, what happens when a model fails, and whether the infrastructure it’s relying on is responding as expected. This update focuses on three areas. 01 — Budget Management Daily and monthly budgets will let developers enforce spending limits per API key at the API level. Configurable warnings will indicate when usage reaches a chosen threshold or the budget limit. Applications will also be able to retrieve their spending limit and remaining budget programmatically, helping agents make more informed decisions before initiating further requests. 02 — Execution Transparency When failover occurs, the originally requested model and the model that ultimately responds aren’t always the same. Request history and exports will make that distinction visible, giving developers a clearer account of how requests were handled without having to reconstruct the execution path themselves. 03 — Provider Health A model being listed as available doesn’t necessarily mean its provider is responding successfully at that moment. Provider health monitoring will help developers distinguish general model availability from current provider responsiveness, making operational issues easier to identify. Together, these capabilities address three practical concerns in agent infrastructure: controlling costs, understanding execution, and identifying provider issues. The goal is to make autonomous AI workflows easier to manage, with fewer blind spots around spending and reliability.

Next up on Avenro: Spending Controls and Execution Reliability. As AI agents take on more tasks, managing inference costs and understanding how requests are executed become increasingly important. Giving an agent access to models is one thing; ensuring developers can control its spending and monitor its behavior is another. Phase 3 of the Avenro roadmap focuses on that layer of control. Here’s what we’re working toward: → Daily and monthly budgets: Set spending limits per API key used by your agents, enforced at the API level. → Configurable budget warnings: Receive notifications when usage reaches a threshold you define or a budget limit is reached. → Remaining-budget access: Let applications check their spending limit and remaining budget programmatically before making further requests. → Failover visibility: See which model was originally requested and which model ultimately responded, including in request history and exports. → Provider health monitoring: Get better visibility into whether model providers are responding, rather than relying on availability information alone. These capabilities address two connected challenges: keeping agent spending within defined limits and making inference execution easier to understand. The objective is to give developers more control over their applications without leaving critical spending and execution decisions hidden from view. Phase 3: Spending Controls and Execution Reliability.

Avenro Update — Agent Infrastructure is now shipped. Integrating AI inference into an agent workflow should no longer mean piecing together documentation, guessing model capabilities, or building every connection from the ground up. With this update, we’re making the path from API access to agent integration more straightforward. Here’s what’s now available: → Agent Quickstart: A practical guide covering API-key configuration, the base URL, model selection, request handling, usage metadata, and spending controls. → Framework integrations: Tested examples for the supported frameworks and workflows, alongside our OpenAI SDK, cURL, and Vercel AI SDK documentation. → Model discovery: The /v1/models endpoint lets developers programmatically retrieve supported models and inspect their available pricing, context windows, capabilities, and availability. → Documented model capabilities: A consistent schema makes it easier to understand supported input and output modalities and determine whether a model fits a particular application. The result is a more accessible integration path for developers building agent-powered applications with Avenro, backed by clearer documentation and discoverable model information. This update is about making the infrastructure easier to work with not adding complexity for the sake of adding features. Agent Infrastructure is shipped. The tools to build with Avenro are becoming more accessible.

A closer look at Avenro’s upcoming Agent Infrastructure update. Connecting an AI agent to an inference API should be straightforward. Understanding which models it can use, how to integrate them, and what to expect from each model should be just as simple. That’s the problem we’re addressing through four areas of development. 01 — Agent Quickstart A practical path from API-key setup to a working agent request, covering the base URL, model selection, error handling, usage metadata, and spending controls. The objective is to reduce the time between getting access and building something useful. 02 — Framework Integrations We’re working toward tested examples for LangChain, LlamaIndex, and CrewAI, building on Avenro’s existing OpenAI SDK, cURL, and Vercel AI SDK examples. Developers should be able to follow a documented integration path instead of figuring everything out from scratch. 03 — Model Discovery The /v1/models endpoint will provide a programmatic way to discover supported models and inspect their pricing, context windows, capabilities, and availability. This gives applications and developers a reliable starting point for model selection. 04 — Modalities & Documented Schema Model compatibility involves more than an identifier. Developers need to know which inputs and outputs a model supports. A consistent, documented schema will make those capabilities easier to inspect and use correctly. Together, these improvements are designed to reduce integration friction and make Avenro easier to build with across different agent frameworks and applications. The focus is simple: clearer integrations, discoverable capabilities, and fewer obstacles between an idea and a working application.

Next up on Avenro: Agent Quickstart & Integrations. Phase 2 of our roadmap focuses on making Avenro easier to integrate into AI agents and developer workflows. As autonomous agents become more capable, accessing inference infrastructure should be straightforward. Developers shouldn’t have to build custom integrations from scratch just to connect their applications to the models they need. This update will focus on four areas: → Agent Quickstart: A practical guide from API-key setup to making agent requests, handling errors, and tracking usage. → Framework Integrations: Working examples for LangChain, LlamaIndex, CrewAI, and other supported workflows. → Model Discovery: A programmatic way to discover available models, pricing, context windows, capabilities, and availability. → Documented Model Capabilities: A consistent schema describing supported model inputs, outputs, and features so developers can make informed integration decisions. The goal goes beyond exposing another API endpoint. It’s about reducing integration friction and giving developers the tools and documentation to build agent-powered applications on top of Avenro. Phase 2: Agent Infrastructure. More than access easier integration.

Avenro Dashboard Update — Shipped Phase 1 of our dashboard improvements is now complete. All six areas are done, bringing a more accessible, personalized, and user-friendly experience to Avenro. Here’s what’s now available: → Navigation by task: A clearer dashboard structure across Overview, Inference, Developer Tools, Account, and Token. → Mobile and tablet layouts: A responsive experience across screen sizes, including tables, charts, forms, menus, and dialogs. → Profile pictures: Upload, replace, or remove your avatar to personalize your account. → Light and dark themes: Choose your preferred appearance and save it to your account. → Spanish localization: Access dashboard pages and settings in Spanish, with localized dates, numbers, and monetary amounts. → Notifications and security: In-app alerts for balances, spending, API-key and wallet changes, and credits, alongside centralized sign-in, session, and API-key change records. These improvements make everyday interactions with Avenro simpler, give users more control over their experience, and make the dashboard more practical across devices. Phase 1: complete.

A closer look at Avenro’s upcoming dashboard improvements. This update goes beyond visual changes. The focus is on making everyday interactions with Avenro simpler and giving users better control over their experience. Navigation & Accessibility Task-based navigation will make it easier to find the tools you need, from inference and API keys to billing and account settings. Improved mobile and tablet layouts will make those same workflows more practical beyond the desktop. Personalization Profile pictures will let users personalize their accounts, while saved light and dark themes will make the dashboard more comfortable to use. Spanish localization will extend across dashboard pages and settings, including dates, numbers, and monetary amounts. Account Control In-app notifications will provide visibility into balances, spending, credits, and relevant API-key or wallet changes. Centralized security settings will bring sign-in sessions and API-key change records into one place, making account activity easier to review. Each improvement addresses a different part of the experience, but the goal is the same: less friction, clearer information, and a dashboard that works better for the people using it.

Next up on Avenro: Dashboard Update — Phase 1. We’re working on improving the dashboard across six key areas, focusing on usability, accessibility, personalization, and account management. Here’s what’s coming: Navigation by task: A clearer structure for Overview, Inference, Developer Tools, Account, and Token. Mobile and tablet layouts: A smoother experience across screen sizes, including tables, charts, forms, menus, and dialogs. Profile pictures: The ability to upload, replace, or remove your avatar. Light and dark themes: Choose your preferred appearance and save it to your account. Spanish localization: Dashboard pages and settings in Spanish, including localized dates, numbers, and amounts. Notifications and security: In-app alerts for balances, spending, API keys, wallet changes, and credits, alongside centralized sign-in, session, and API-key change records. The goal is to make Avenro’s dashboard more intuitive, more personal, and easier to manage across devices. That’s what’s next for the Avenro dashboard. — Avenro

The roadmap is set. Now it’s time to build. We’ll be sprinting through the next phase of Avenro, moving fast and turning the roadmap into real updates, one by one. From here on, expect our posts to focus on two things: what we’re working on next and what we’ve shipped. Less talk, more building. Thank you to everyone who contributed ideas and helped shape this roadmap. We’re here for the long run, and we’re ready to move fast. Time to build.

The new Avenro roadmap is live. After asking the community what we should build next, we’ve turned your feedback into a clearer plan covering dashboard improvements, developer tools, and agent-ready infrastructure. This is just the beginning. We’ll keep building, listening, and improving alongside the community. Take a look at the updated roadmap: avenro.tech/roadmap More to build. Avenro

Yesterday, we asked the Avenro community what we should build next. The response has been great, and we’re happy to see so many thoughtful suggestions and technical ideas. We’ve taken your feedback and shaped it into a new roadmap focused on improving the dashboard, developer experience, and agent-ready infrastructure. Your input is helping shape what comes next for Avenro. The roadmap will be live soon. More to build, together.

Good morning We’re exploring new possibilities, discovering new ways to build, and thinking about what comes next for Avenro. More soon. Stay tuned.

Avenro is here for the long run. Now, let’s build together. We’ve seen people talk about what robinhood:0x1d08d2e96e7712b754048dd1c2e7a6e878ed4913 could become if we keep building at this pace. We appreciate the support, but we want the community to be part of what comes next. What should we build next? New features, developer tools, integrations, infrastructure improvements — if you understand the technical side of AI and see something we could do better, we want to hear from you. To the developers, engineers, and people who have been talking about Avenro: bring us your ideas. Challenge us, suggest improvements, and help shape the product. Since launching robinhood:0x1d08d2e96e7712b754048dd1c2e7a6e878ed4913, we’ve continued building. We’re here for the long run, and we believe the work speaks for itself. Don’t just watch us build. Help us decide what to build next. Drop your ideas below. We’re listening.

Avenro: From Discounted AI Credits to AI Inference Infrastructure Avenro started with a simple idea: make AI credits more affordable for everyone. The original focus was straightforward. Give developers access to leading AI models at more competitive prices, without unnecessary subscriptions or complicated commitments. But as we built, the vision expanded. A lower price is a good starting point. Developers also need to understand what they’re paying for, choose the right models, manage their spending, and know how their requests are actually executed. That’s what Avenro has been growing into. What started as discounted AI credits has evolved across several layers: → Unified model access — an OpenAI-compatible API designed to make working with models across providers simpler. → Cost transparency — clearer pricing comparisons, request-level cost metadata, and visibility into inference spending. → Developer observability — API-key usage analytics and better insight into how applications consume AI resources. → Model discovery and comparison — bringing pricing, context windows, availability, and capabilities into the model-selection process. → Execution control — moving beyond basic model access toward configurable routing, compatible fallback preferences, and visibility into request execution. → A native robinhood:0x1d08d2e96e7712b754048dd1c2e7a6e878ed4913 utility layer — extending the ecosystem around token-based access and utility while keeping the core inference experience accessible without requiring robinhood:0x1d08d2e96e7712b754048dd1c2e7a6e878ed4913. Each addition addresses a different part of the same problem: making AI infrastructure easier to access, understand, and control. The original idea hasn’t changed. AI should cost less to use. What’s changed is our understanding of what it takes to deliver that value properly. Avenro is becoming more than a cheaper way to access AI models. It’s an inference platform focused on competitive pricing, a familiar developer experience, cost visibility, and greater control over how AI applications run. We started with the price of intelligence. We’re building the infrastructure around using it.

From Model Choice to Execution Choosing an AI model is only one part of building a reliable AI application. What happens after that decision matters just as much. A developer might compare several models, weigh their pricing, context windows, capabilities, and availability, and select the one that best fits their workload. But that choice doesn’t exist in isolation. What happens when the preferred model becomes unavailable? Which alternatives are acceptable? Can a fallback meet the same requirements without exceeding the application’s cost limits? And once a request completes, can the developer understand which execution path was taken and what it cost? These are connected decisions, but they solve different problems. Model Comparison helps developers evaluate their options before choosing a model. Developer-Controlled Routing focuses on how requests should be handled after those choices are made, including primary-model preferences, compatible fallbacks, routing constraints, and execution visibility. One is about making a more informed choice. The other is about having greater control over how that choice is carried out. At Avenro, we’re approaching AI infrastructure as more than access to models through a single API. The goal is to give developers a clearer view of their options, more deliberate control over execution, and better information about what happens when their applications make requests. Because good infrastructure shouldn’t make every decision for the developer. It should give them the tools to make the decisions that matter.

Avenro Update — Developer-Controlled Routing Automatic failover helps keep requests running. Developer-controlled routing takes the next step: giving developers a say in what happens when the preferred model cannot handle a request. The goal is to make routing decisions more deliberate, configurable, and visible. The planned capabilities cover: • Primary model selection — define your preferred model for a request. • Ordered fallbacks — specify compatible alternatives in the order you prefer. • Routing constraints — keep fallback choices within supported compatibility requirements. • Spending controls — account for cost limits when configuring execution. • Execution visibility — understand which path was selected and whether failover occurred, alongside request usage and cost metadata. The principle is straightforward: a fallback shouldn’t be treated as an interchangeable replacement. The right alternative depends on your application’s requirements. Developers should have more control over those decisions, rather than relying entirely on hidden routing behavior.

Failover is useful. But it isn’t the same as routing control. If a request fails and your infrastructure automatically switches to another model, that’s one problem handled. But what if the alternative isn’t suitable for your application? Maybe it doesn’t support the capability you need, its context window is too small, or its cost doesn’t fit your limits. A successful fallback isn’t necessarily the right fallback. Developers should be able to define not just what happens when something goes wrong, but which alternatives are acceptable in the first place. Reliability is about keeping requests running. Control is about deciding how they should run.

Avenro Update Model Comparison Choosing an AI model shouldn’t mean jumping between provider documentation, pricing pages and capability lists just to understand your options. We’re introducing Model Comparison to make that evaluation more straightforward. The goal is to give developers a clearer, side by side view of supported models across providers, including: • Provider and context window • Input and output pricing • Avenro pricing alongside reference pricing • Model availability • Supported capabilities Instead of evaluating each model in isolation, developers can compare the details that matter to their application in one place. The objective isn’t to label one model as universally better than another. It’s to make the trade-offs easier to understand so developers can make more informed decisions about cost, capability and fit.