# regolo.ai > > European LLM Inference API — Zero Data Retention, 100% EU Infrastructure, Green Energy Regolo.ai is a serverless GPU inference provider built and hosted entirely in Italy, within the DHH Group infrastructure. It gives developers and enterprises access to state-of-the-art open-weight models via an OpenAI-compatible API, without managing GPUs or worrying about data sovereignty. All requests are processed with zero data retention: no logs, no training use, no data shared outside the EU. Powered entirely by renewable energy. ## Posts - [GLM-5.1 vs Laguna M.1 vs MiniMax M3: A Practical Benchmark](http://regolo.ai/glm-5-1-vs-laguna-m-1-vs-minimax-m3-a-practical-benchmark/): MiniMax M3 leads the published coding comparison, while GLM-5. 1 is strongest for long-running text agents and Laguna M. 1... - [When research meets artificial intelligence: a bachelor’s thesis chooses Regolo to develop a chatbot for citizens](http://regolo.ai/when-research-meets-artificial-intelligence-a-bachelors-thesis-chooses-regolo-to-develop-a-chatbot-for-citizens/): A university thesis explores the development of Oracolo, an AI assistant based on Regolo and RAG technology to improve access... - [Brick + Claude Code CLI: A Practical Guide to Smarter AI Routing and Cost Optimization](http://regolo.ai/brick-claude-code-cli-guide/): If you’re running Claude Code CLI in production—or even just heavily in your daily workflow—you’ve probably noticed your API bills... - [Inkling: Thinking Machines Lab Introduces a 975B Open-Weights Multimodal MoE Model](http://regolo.ai/inkling-thinking-machines-lab-introduces-a-975b-open-weights-multimodal-moe-model/): Thinking Machines Lab has released Inkling, its first foundation model trained from scratch and designed for developers who need more... - [LangChain Self-Verification Loop: Python + pytest Demo for a CI Repair Agent](https://regolo.ai/agent-harness-evaluate-efficiency-production/): If you want to make a coding LLM more reliable, start by upgrading the harness instead of swapping models. LangChain’s... - [Bonsai 27B Explained: How a 27B Model Runs Locally](http://regolo.ai/bonsai-27b-explained-how-a-27b-model-runs-locally/): Announced on July 14, 2026, the model supports a 262K token context window while delivering impressive local inference performance: 163... - [Common Pitfalls in Agent Harness Design and How to Avoid Them](http://regolo.ai/common-pitfalls-in-agent-harness-design-and-how-to-avoid-them/): An agent harness is the layer around a model that manages context, tools, permissions, memory, execution flow, and monitoring. Many... - [How to Build a Closed-Loop AI Agent That Catches Its Own Hallucinations](https://regolo.ai/how-to-build-a-closed-loop-ai-agent-that-catches-its-own-hallucinations/): On June 15, 2026, Boris Cherny, head of Claude Code at Anthropic, shifted the industry perspective on AI systems with... - [Opencode + Brick for Multi Agent Coding and optimize costs up to 80%](https://regolo.ai/opencode-brick-for-multi-agent-coding-and-optimize-costs-up-to-80/): Step-by-step guide to setting up opencode with Brick-V1-Beta for automatic model routing across 6 specialized subagents via Regolo API. - [OpenWiki — Setup a Privacy-First for Enterprises](https://regolo.ai/openwiki-setup-a-privacy-first-for-enterprises/): OpenWiki is a CLI tool built by LangChain that writes and maintains documentation for your codebase using an LLM agent... - [Introducing Brick: The Open-Source Semantic Router That Slashes AI Costs by 22x While Hitting World-Record Performance on Hugging Face](https://regolo.ai/introducing-brick-the-open-source-semantic-router-that-slashes-ai-costs-by-22x-while-hitting-world-record-performance-on-hugging-face/): Brick is an open-source routing gateway (Mixture-of-Models or MoM) that automatically forwards each user query to the optimal model from... - [Give your AI agents persistent, GPDR compliance and memory](https://regolo.ai/give-your-ai-agents-persistent-private-memory/): A step-by-step guide to installing agentmemory with OpenCode and Regolo. ai — keeping all session data on-premise, fully GDPR-compliant. Every... - [MiniMax M3 vs GLM-5.2: When to Use Each Open-Weight Giant](https://regolo.ai/minimax-m3-vs-glm-5-2-when-to-use-each-open-weight-giant/): Both models were released in June 2026, both carry a 1M-token context window, and both target the same enterprise buyer:... - [How to Make RAG 32x Memory Efficient with Binary Quantization](https://regolo.ai/how-to-make-rag-32x-memory-efficient-with-binary-quantization/): This pattern is for teams building RAG over millions of chunks, where float32 embeddings start to dominate RAM, SSD, and... - [Token Cost Optimization in 2026: Why AI Spend Is Rising—and How to Cut It by Up to 80%](https://regolo.ai/token-cost-optimization-in-2026-why-ai-spend-is-rising-and-how-to-cut-it-by-up-to-80/): Token cost optimization is no longer a side concern. In 2026, it is the control lever that separates scalable AI... - [AI Act, August 2, 2026: what CTOs must actually do](https://regolo.ai/ai-act-august-2-2026-what-ctos-must-actually-do/): On that date, the AI Act’s general application phase starts: most obligations for high‑risk AI systems and transparency duties kick... - [Beyond the proxy: exploring LLM cost control with Bifrost, Requesty, and Portkey](https://regolo.ai/beyond-the-proxy-exploring-llm-cost-control-with-bifrost-requesty-and-portkey/): As generative AI applications move from fragile prototypes to high-scale production systems, the operational costs of LLM API calls can... - [Building compliant AI agents: a guide for teams preparing for the EU AI Act](https://regolo.ai/what-is-an-inference-provider-a-european-privacy-first-take/): The decision of where to execute your large language models is no longer just an infrastructure line item; it is... - [How to Build Self-Improving Agents That Actually Get Better Over Time](https://regolo.ai/how-to-build-self-improving-agents-that-actually-get-better-over-time/): Many teams building production AI applications quickly realize that single-turn prompting inevitably falls apart when faced with intricate, open-ended tasks.... - [From Prompting to Loop Engineering: Building Autonomous Agents in VS Code](https://regolo.ai/from-prompting-to-loop-engineering-building-autonomous-agents-in-vs-code/): Stop acting as the feedback mechanism for your AI. Instead of traditional prompting (You → Prompt → Agent → Output... - [LLM Architectures for Business: Which Model Fits Which Job?](https://regolo.ai/llm-architectures-for-business-which-model-fits-which-job/): If you are comparing LLM architectures for business, the smart move is not to chase the model with the flashiest... - [GLM 5.2 vs Kimi K2.7 Code: The Definitive Guide for Coding](https://regolo.ai/glm-5-2-vs-kimi-k2-7-code-the-definitive-guide-for-coding/): These are two open-weight models released in June 2026 just one day apart, both Mixture-of-Experts systems and both aimed at... - [AutoRound Quantization Guide: From Local GPU to Private API endpoint](https://regolo.ai/autoround-quantization-gguf-guide/): Your RTX 4090 just became a 70B-model machine. Intel’s AutoRound makes it possible — and this guide shows exactly how... - [AI Coding Pipeline in VS Code: 4-Stage Orchestrated Workflow with Zoo Code and Regolo](https://regolo.ai/ai-coding-pipeline-in-vs-code-4-stage-orchestrated-workflow-with-roo-code-and-regolo/): Most teams use AI coding agents wrong. They throw a massive prompt at a single LLM, hit the context window... - [Secure Multi-Agent Orchestration for Beginners: CrewAI, AutoGen & MetaGPT](https://regolo.ai/multi-agent-orchestration-on-a-european-inference-layer-crewai-autogen-and-metagpt/): Many teams eagerly wire up a multi-agent framework to automate their workflows and point it at a default US-based API,... - [n8n, Flowise, and Langflow: Build AI Workflows without Sending Data Outside Europe](https://regolo.ai/n8n-flowise-and-langflow-build-ai-workflows-without-sending-data-outside-europe/): Low-code AI orchestration platforms like n8n, Flowise, and Langflow have made it incredibly easy to build complex AI agents. However,... - [Practical RAG with Sensitive Documents on EU Infra (LangChain & LlamaIndex)](https://regolo.ai/practical-rag-with-sensitive-documents-on-eu-infra-langchain-llamaindex/): Building Retrieval-Augmented Generation (RAG) applications on sensitive documents requires strict control over where data flows. By combining a private vector... - [Build a healthcare assistant with n8n and Regolo: a step-by-step guide](https://regolo.ai/healthcare-literature-assistant-n8n-regolo/): If your agency wants to offer agentic services in healthcare without building from scratch, n8n is the fastest path: a... - [3 concrete ways to fix accuracy, hallucinations and bias in your LLM agents](https://regolo.ai/fix-errors-hallucinations-bias-llm-agents/): If you ship LLMs and agents into real workflows, you’ve already seen it: the model sounds confident, but dates are... - [DFlash: x3 LLM inference speed – guide and codes](https://regolo.ai/dflash-x3-llm-inference-speed-guide-and-codes/): DFlash is a new block-diffusion based speculative decoding technique that speeds up large language model (LLM) inference by predicting multiple... - [Drop-In OpenAI Replacement: Swap base_url to EU-Hosted Regolo](https://regolo.ai/openai-replacement-eu-base-url-regolo/): Replacing OpenAI with a European, GDPR-compliant inference provider does not require rewriting your application because we provide an OpenAI-compatible endpoint,... - [Context Engineering Tutorial: Build Lightweight, Local AI Agents in Python](https://regolo.ai/context-engineering-tutorial-build-lightweight-local-ai-agents-in-python/): This tutorial provides a complete guide to implementing the Context Engineering framework for AI agents: the architecture presented is optimized to prevent... - [MiniMax vs DeepSeek: 2-tier benchmark comparison for AI agents (2026)](https://regolo.ai/minimax-vs-deepseek-benchmark-model-size-comparison-2026/): Choosing between MiniMax and DeepSeek is not a single decision — it depends on which size tier you are operating... - [Accelerated LLM Inference with DFlash and vLLM Speculators (2026 guide)](https://regolo.ai/train-run-dflash-speculative-decoding-vllm/): DFlash is an effective speculative decoding algorithm designed to accelerate Large Language Model (LLM) inference without altering the core weights... - [How to build a governance‑ready communication protocol for multi‑agent systems](https://regolo.ai/governance-ready-multi-agent-protocol/): Most multi‑agent systems in production still “just pass prompts around”, which is exactly what breaks as soon as you add... - [What embeddings are and how they help AI understand meaning](https://regolo.ai/what-are-embeddings/): An embedding is a way of turning a piece of text into a list of numbers that captures its meaning,... - [How to Decompose Complex LLM Agents with Open Source Models: A Step-by-Step Tutorial](https://regolo.ai/how-to-decompose-complex-llm-agents-with-open-source-models-a-step-by-step-tutorial/): Is your AI agent becoming unreliable? When an agent’s System Prompt grows to hundreds of lines and handles dozens of... - [Implementing Stateful AI Agents: How to Build Anthropic's Memory Store and Dreaming Architecture in Python](https://regolo.ai/implementing-stateful-ai-agents-how-to-build-anthropics-memory-store-and-dreaming-architecture-in-python/): It’s a blueprint production-ready for implementing a stateful, three-layer memory architecture for AI agents. Inspired by Anthropic’s managed agent memory... - [Zero data retention in AI: what it really means and how enterprises can enforce it with Regolo](https://regolo.ai/zero-data-retention-vs-provider-policies-what-teams-need-to-verify/): A direct comparison between true zero-retention inference and softer provider retention policies that still leave teams exposed. - [Understanding multimodal AI models - What a multimodal model is and when to use one](https://regolo.ai/what-is-multimodal-ai/): What does “multimodal” really mean? Most AI models are built for a single kind of input. A text model reads... - [How to build a custom RAG chatbot with Flowise and Regolo](https://regolo.ai/build-rag-chatbot-flowise-regolo/): How to migrate an existing Flowise chatflow to an EU-hosted OpenAI-compatible backend without rebuilding the graph. - [ZAYA1-8B vs DeepSeek-R1-0528: which open model enterprises should use, and how to run it with Regolo](https://regolo.ai/zaya1-8b-vs-deepseek-r1-0528-which-open-model-enterprises-should-use-and-how-to-run-it-with-regolo/): For most companies, ZAYA1-8B is the better open-weight choice when coding, reasoning efficiency, and serving cost matter more than raw... - [Open models and scale-to-zero: managing cold starts and cost](https://regolo.ai/open-models-scale-to-zero-cold-starts/): Which open model families still make sense when a deployment really scales to zero and cold starts start hurting product... - [EU AI: data residency isn’t enough](https://regolo.ai/eu-data-residency-not-enough-ai-compliance/): Why EU data residency solves only part of the enterprise AI review and what teams still need to verify after... - [How Zyphra Achieved Frontier Performance with ZAYA1-8B (A Tiny but Mighty Reasoning MoE)](https://regolo.ai/how-zyphra-achieved-frontier-performance-with-zaya1-8b-a-tiny-but-mighty-reasoning-moe/): Zyphra made ZAYA1-8B strong not by making it huge, but by making it efficient at every layer of the stack.... - [Regolo.ai + Botpress — Integration via Execute Code](https://regolo.ai/regolo-ai-botpress-integration-via-execute-code-card/): The simplest way to connect Regolo Core Models API to Botpress is to use Execute Code Card and call Regolo’s... - [MiniMax M2.7 vs Kimi K2.5: when to use which](https://regolo.ai/minimax-m2-7-vs-kimi-k2-5-when-to-use-which/): Both MiniMax M2. 7 and Kimi K2. 5 are open-weight Mixture-of-Experts models released in early 2026 that punch well above... - [AI Salon Rome and the Shape of the European AI Stack](https://regolo.ai/ai-salon-rome-and-the-shape-of-the-european-ai-stack/): At AI Salon Rome, the conversation was not about AI in the abstract, It was about what it actually takes... - [Using Petri to audit open-source LLMs with Regolo](https://regolo.ai/using-petri-to-audit-open-source-llms-with-regolo/): A practical guide to choosing between Qwen, Mistral, and Gemma based on workload, cost, serving profile, and model family tradeoffs. - [Privacy-first AI in Europe: zero retention, sovereignty, and the new risks we cannot ignore](https://regolo.ai/privacy-first-ai-in-europe-zero-retention-sovereignty-and-the-new-risks-we-cannot-ignore/): Keep inference inside EU jurisdiction, minimize retention, and design the stack so prompts, outputs, and tool payloads do not persist... - [How to use CodeGraphContext with Regolo and RooCode to cut token costs and ship smarter code](https://regolo.ai/how-to-use-codegraphcontext-with-regolo-and-roocode-to-cut-token-costs-and-ship-smarter-code/): Most AI coding agents understand your repository the same way a junior developer would on their first day — by... - [AI, privacy and compliance in 2026: what changes for LLM providers](https://regolo.ai/ai-privacy-and-compliance-in-2026-what-changes-for-llm-providers/): The 2026 wave of EU regulation makes AI governance and data protection a single, continuous compliance problem: if we ship... - [Why TurboQuant matters for real-world LLM inference](https://regolo.ai/why-turboquant-matters-for-real-world-llm-inference/): TurboQuant is a KV-cache compression method from Google Research that was presented at ICLR 2026. In the reported results, it... - [Sustainable inference is now an AI infrastructure decision](https://regolo.ai/sustainable-inference-is-now-an-ai-infrastructure-decision/): Artificial intelligence is simultaneously our most promising tool for fighting climate change and one of its fastest-growing contributors. As AI... - [The green AI paradox: balancing innovation with planetary health](https://regolo.ai/the-green-ai-paradox-balancing-innovation-with-planetary-health/): Artificial intelligence is getting better fast, but the systems behind it are consuming more power, water, and hardware than most... - [TurboQuant benchmark: what to measure, what matters, and how to read the results](https://regolo.ai/turboquant-benchmark-what-to-measure-what-matters-and-how-to-read-the-results/): TurboQuant is a two-stage online vector quantization algorithm from Google Research (presented at ICLR 2026) that compresses LLM key-value caches... - [Privacy‑first AI tools vs big platforms: what “not sending data to OpenAI/Claude really means](https://regolo.ai/privacy-first-ai-tools-vs-big-platforms-what-not-sending-data-to-openai-claude-really-means/): Privacy‑first AI tools trade some convenience for clear, technical guarantees that your data is not quietly fuelling someone else’s models.... - [Multi-agent AI for compliance in banks and insurers: a practical playbook](https://regolo.ai/multi-agent-ai-for-compliance-in-banks-and-insurers-a-practical-playbook/): When we talk about multi-agent AI in banking and insurance, we are really talking about splitting a heavy, regulated workflow... - [How to benchmark memory usage between Hermes Agent and OpenClaw](https://regolo.ai/how-to-benchmark-memory-usage-between-hermes-agent-and-openclaw/): The cleanest way to compare Hermes Agent and OpenClaw is to keep both agents local, send the same workload to... - [Using Regolo models with OpenCode](https://regolo.ai/using-regolo-models-with-opencode/): OpenCode is an open-source, terminal-native AI coding agent that supports 75+ LLM providers through an extensible configuration system. Because Regolo... - [Gemma 4 31B vs Qwen3.6 35B-A3B: When to use which](https://regolo.ai/gemma-4-31b-vs-qwen3-6-35b-a3b-when-to-use-which/): A benchmark-grounded guide for teams choosing between two of the strongest open models of 2026. What these two models actually... - [Inference efficiency and GPU cost optimization in 2026: how to cut LLM serving waste](https://regolo.ai/inference-efficiency-and-gpu-cost-optimization-in-2026-how-to-cut-llm-serving-waste/): Inference efficiency in 2026 is about lowering cost per million tokens by improving utilization, reducing repeated work, and matching infrastructure... - [How to choose a privacy‑first LLM API (without stalling your roadmap)](https://regolo.ai/how-to-choose-a-privacy-first-llm-api-without-stalling-your-roadmap/): Privacy‑focused LLM APIs put control over prompts and outputs back in your hands by minimizing logging, enforcing strict retention, and... - [Open-source LLMs and local / decentralized alternatives in 2026](https://regolo.ai/open-source-llms-and-local-decentralized-alternatives-in-2026/): Open-source / open-weight models are no longer “second tier”: GLM-5. 1 and Gemma 4 compete with or surpass closed LLMs... - [8 out of 10 Europeans do not trust US and Chinese companies with their data](https://regolo.ai/8-out-of-10-europeans-do-not-trust-us-and-chinese-companies-with-their-data/): A March 2026 Politico survey put a number on something many people in European tech had already felt: 84% of... - [Using MiniMax skills with Regolo.ai: a practical guide](https://regolo.ai/using-minimax-skills-with-regolo-ai-a-practical-guide/): We can treat MiniMax “skills” as reusable, high-level instruction blocks or toolkits that specialize an AI agent for tasks like... - [GLM-5.1 vs MiniMax M2.7: which open model looks better on public benchmarks?](https://regolo.ai/glm-5-1-vs-minimax-m2-7-which-open-model-looks-better-on-public-benchmarks/): Based on the public benchmarks we reviewed, GLM-5. 1 has the stronger benchmark profile for long-horizon coding and agentic work,... - [EU data residency and GDPR for AI vendors: from checkbox to product feature](https://regolo.ai/eu-data-residency-and-gdpr-for-ai-vendors-from-checkbox-to-product-feature/): EU data residency has moved from a legal afterthought to a core product requirement for anyone buying or selling AI... - [Zero data retention: from buzzword to enterprise buying criterion](https://regolo.ai/zero-data-retention-from-buzzword-to-enterprise-buying-criterion/): Zero data retention (ZDR) has become a standard line item in AI RFPs because it directly shrinks breach impact, compliance... - [The privacy line AI platforms are crossing with user data](https://regolo.ai/the-privacy-line-ai-platforms-are-crossing-with-user-data/): Recent investigations and policy debates show a clear pattern: major platforms are quietly stretching the boundaries of consent by repurposing... - [AI cost reduction in 2026: how to stop GPUs eating your entire budget](https://regolo.ai/ai-cost-reduction-in-2026-how-to-stop-gpus-eating-your-entire-budget/): AI costs are no longer dominated by model training; for most teams, continuous inference on GPUs is the real bill.... - [AI-Native Software Development: How to Build a PR Review Assistant with Regolo Instead of Another Generic Copilot](https://regolo.ai/ai-native-software-development-how-to-build-a-pr-review-assistant-with-regolo-instead-of-another-generic-copilot/): The next phase of AI in software development is workflow-native, not chat-tab-native. Claude 4 was launched with a strong emphasis... - [Zero Data Retention LLMs: Why It Matters](https://regolo.ai/zero-data-retention-llms-why-it-matters/): Every time you send a prompt to an LLM API, ask yourself: where does that text go after you get... - [Small Language Models Are Growing Up: How to Build a Hybrid Inference Stack Without Sacrificing Quality](https://regolo.ai/small-language-models-are-growing-up-how-to-build-a-hybrid-inference-stack-without-sacrificing-quality/): Small language models are becoming strategically useful because they lower latency, reduce cost, and make hybrid on-device or edge-first architectures... - [AI Agents and Tool Chaining in 2026: How to Build Workflows That Actually Finish the Job](https://regolo.ai/ai-agents-and-tool-chaining-in-2026-how-to-build-workflows-that-actually-finish-the-job/): AI agents are useful when they complete bounded business tasks with reliable tool use, not when they simply produce long... - [Run MiroFish with regolo.ai: A Complete Integration Guide](https://regolo.ai/run-mirofish-with-regolo-ai-a-complete-integration-guide/): Are you tired of using outdated simulation tools that can’t handle the complexity of your projects? Do you want to... - [New LLM Architectures in 2026: What CTOs Should Optimize For Instead of Chasing Benchmarks](https://regolo.ai/new-llm-architectures-in-2026-what-ctos-should-optimize-for-instead-of-chasing-benchmarks/): The important shift in 2026 is architectural, not cosmetic. Llama 4 is positioned around native multimodality, Claude 4 around coding... - [AI Governance, Copyright, and Enterprise Risk: How to Build a Policy Gateway Before You Ship](https://regolo.ai/ai-governance-copyright-and-enterprise-risk-how-to-build-a-policy-gateway-before-you-ship/): AI governance is not a legal appendix; it is an application-layer control plane. Regolo’s public positioning emphasizes EU-hosted APIs, Zero... - [Build ToneCraft: an AI-powered Thunderbird extension for more professional emails (on Regolo GPUs)](https://regolo.ai/build-tonecraft-an-ai-powered-thunderbird-extension-for-more-professional-emails-on-regolo-gpus/): AI email assistants are everywhere, but most tools still feel like generic text generators bolted on top of your inbox.... - [Qwen3.5 122B, Qwen3.5 9B, and Mistral Small 4 119B Are Now Available on Regolo](https://regolo.ai/qwen3-5-122b-qwen3-5-9b-and-mistral-small-4-119b-are-now-available-on-regolo/): We have added three new open models to Regolo: Qwen3. 5 122B, Qwen3. 5 9B, and Mistral Small 4 119B.... - [Programmatic Tool Calling: How to Build Smarter LLM Agents on Regolo GPUs](https://regolo.ai/programmatic-tool-calling-how-to-build-smarter-llm-agents-on-regolo-gpus/): Programmatic tool calling is quickly becoming one of the hottest topics among LLM builders on Reddit, X and dev forums.... - [How to Implement GraphRAG with Regolo.ai Models](https://regolo.ai/how-to-implement-graphrag-with-regolo-ai-models/): GraphRAG improves retrieval‑augmented generation by converting raw text into a knowledge graph, enabling multi‑hop reasoning and corpus‑wide synthesis that standard... - [Build Faster: LLM as a Service for Developers with Qwen 3.5 122b](https://regolo.ai/build-faster-llm-as-a-service-for-developers-with-qwen-3-5-122b/): LLM as a Service (LLMaaS) is changing how developers work. Rather than deploying and managing models yourself, you call a... - [AI Footprint: How to Measure and Reduce LLM Inference Impact](https://regolo.ai/ai-footprint-how-to-measure-and-reduce-llm-inference-impact/): AI is becoming part of everyday products, but every prompt, completion, and agent workflow has a real infrastructure cost. The... - [Cloud Bill Shock: How Hidden Egress Fees Crush Startup Budgets & How to Escape](https://regolo.ai/cloud-bill-shock-how-hidden-egress-fees-crush-startup-budgets-how-to-escape/): Your AI feature goes viral. Traffic spikes. Users love it. Then your AWS invoice arrives — $47,000 for one month,... - [How to Implement GDPR-Compliant AI Inference: A Pragmatic Framework](https://regolo.ai/how-to-implement-gdpr-compliant-ai-inference-a-pragmatic-framework/): Review AI inference under GDPR with five concrete checks: geography, retention, transfers, persistence, and whether the architecture is defensible for... - [Building a Document Analysis System with Regolo AI API](https://regolo.ai/building-a-document-analysis-system-with-regolo-ai-api/): Co-authored by Matteo Mendula – AI VENTURE BUILDER LDT – http://aivb. ai/ Processing tenders and contracts manually is a bottleneck... - [From Inbox to Automated CRM: Privacy-First Email RAG with LlamaIndex for EU Developers](https://regolo.ai/from-inbox-to-automated-crm-privacy-first-email-rag-with-llamaindex-for-eu-developers/): 👉 UNLIMITED tokens for 30 days If you are a software developer, data scientist, or AI engineer building solutions for... - [OpenClaw AI Agent: Build a 24/7 Business Automation Machine](https://regolo.ai/openclaw-ai-agent-build-a-24-7-business-automation-machine/): What if your business could run a full marketing pipeline, manage CRM leads, monitor competitors, draft content, and open pull... - [Building a Reliable Multi-Agent Competitor Intelligence System with CrewAI](https://regolo.ai/building-a-reliable-multi-agent-competitor-intelligence-system-with-crewai/): Single-agent LLMs hallucinate, miss context, or produce shallow analysis. Multi-agent systems fix this by giving specialized roles, opposing incentives (especially... - [Scale-to-Zero Cold Start Latency: Why Serverless GPU Breaks Real-Time AI (And How to Fix It)](https://regolo.ai/scale-to-zero-cold-start-latency-why-serverless-gpu-breaks-real-time-ai-and-how-to-fix-it/): Real-time AI applications live and die by latency. When a user sends a message to your chatbot or speaks to... - [Multi-Agent AI Systems: Why the “Single Copilot” Is Dying](https://regolo.ai/multi-agent-ai-systems-why-the-single-copilot-is-dying/): The “single copilot” era is ending—not because AI got worse, but because enterprises finally tried to scale it. When one... - [AI Data Sovereignty in Europe for LLMs](https://regolo.ai/ai-data-sovereignty-gdpr-compliant-llm-inference-europe/): If you are deploying LLMs in Europe, “Does it work? ” is no longer the question that slows you down.... - [Human-in-the-Loop: Phase or Permanent Fixture for Agentic AI?](https://regolo.ai/human-in-the-loop-autonomous-ai-agents-governance/): What happens when your AI agent autonomously spends $50,000 of company budget on cloud resources — and gets it wrong?... - [The Overkill Hardware Trap: Stop Paying H100 Prices for Inference](https://regolo.ai/overkill-hardware-trap-inference-vs-training-gpu-costs/): Here’s a scenario playing out in engineering organizations right now: your team spent three weeks fine-tuning a Llama 3 70B... - [GPU Inference Costs Exploded by 2026: The Big Challenge for Italian Startups and Enterprises (and How to Control Them)](https://regolo.ai/gpu-inference-costs-exploded-by-2026-the-big-challenge-for-italian-startups-and-enterprises-and-how-to-control-them/): The Italian cloud computing market hit €8. 13 billion in 2025, surging 20% year-over-year, fueled by AI workloads and sovereign... - [Testing, Transparency, and Carbon Awareness: Inside the Regolo Playground](https://regolo.ai/testing-transparency-and-carbon-awareness-inside-the-regolo-playground/): The Regolo Playground is a focused environment for experimenting with open models, designed to make prompt iteration, model selection, and... - [Dictation Tool on Linux: hyprwhspr + Regolo (Whisper)](https://regolo.ai/dictation-tool-on-linux-hyprwhspr-regolo-whisper/): Use a dictation tool on Linux with hyprwhspr and Regolo’s Whisper API. Set up shortcuts, dual-language dictation, and audio ducking... - [DeepSeek-OCR-2 vs GLM-OCR vs PaddleOCR: Which OCR Model Should You Use and When?](https://regolo.ai/deepseek-ocr-vs-glm-ocr-vs-paddleocr-benchmark-2026/): Choose between DeepSeek-OCR-2, GLM-OCR, and PaddleOCR based on document type, benchmark context, throughput, and deployment reality instead of headline scores... - [GitHub Copilot Alternative: Copilot vs Custom LLM Assistant (cheaper and faster)](https://regolo.ai/github-copilot-alternative-copilot-vs-custom-llm-assistant-cheaper-and-faster/): When you use GitHub Copilot, your code snippets are processed through Microsoft Azure infrastructure. While GitHub states that code from... - [Private AI Coding: Deploy Without Giving Away Your Code](https://regolo.ai/private-ai-coding-deploy-without-giving-away-your-code/): In 2025, the market for AI coding tools is valued at around $6. 5 billion. Coding is becoming increasingly widespread,... # # Detailed Content ## Posts A university thesis explores the development of Oracolo, an AI assistant based on Regolo and RAG technology to improve access to public information. Step-by-step guide to setting up opencode with Brick-V1-Beta for automatic model routing across 6 specialized subagents via Regolo API. A direct comparison between true zero-retention inference and softer provider retention policies that still leave teams exposed. How to migrate an existing Flowise chatflow to an EU-hosted OpenAI-compatible backend without rebuilding the graph. Which open model families still make sense when a deployment really scales to zero and cold starts start hurting product experience. Why EU data residency solves only part of the enterprise AI review and what teams still need to verify after geography. A practical guide to choosing between Qwen, Mistral, and Gemma based on workload, cost, serving profile, and model family tradeoffs. Review AI inference under GDPR with five concrete checks: geography, retention, transfers, persistence, and whether the architecture is defensible for the actual use case. Use a dictation tool on Linux with hyprwhspr and Regolo’s Whisper API. Set up shortcuts, dual-language dictation, and audio ducking in minutes. Choose between DeepSeek-OCR-2, GLM-OCR, and PaddleOCR based on document type, benchmark context, throughput, and deployment reality instead of headline scores alone. > --- ## Core Product - [Home](https://regolo.ai/): European AI inference API. OpenAI-compatible endpoints for chat, embeddings, speech-to-text, image generation, OCR, and reranking. Zero data retention, GDPR-compliant, 100% renewable energy. - [Models Library](https://regolo.ai/models-library/): Full list of available models across all modalities — LLMs, embedding, STT, image, OCR, rerank — with specs, context windows, quantization, and token pricing. - [Pricing](https://regolo.ai/pricing/): Pay-as-you-go (no monthly commitment) and Enterprise plans. 30-day free trial with full access, no credit card required. - [Dashboard](https://dashboard.regolo.ai/): API key management, usage monitoring, spending caps, and model access. - [Models Status](https://obs.regolo.ai/): Real-time availability and latency status for all deployed models. - [Docs](https://docs.regolo.ai/): Full API reference, quickstart guides, and integration examples. - [Labs](https://regolo.ai/labs/): Experimental features and early-access models. - [European Inference](https://regolo.ai/european-inference/): Regolo's commitment to EU data residency, GDPR compliance, EU AI Act alignment, and transparency. - [GPU Cloud Infrastructure](https://regolo.ai/gpu-cloud-infrastructure/): How Regolo manages serverless GPU infrastructure for autoscaling, cold starts, and multi-tenant isolation. - [Sustainable AI](https://regolo.ai/the-environmental-impact-of-artificial-intelligence-why-building-a-sustainable-ai-matters/): Environmental impact of AI inference and Regolo's 100% renewable energy commitment. - [DHH Group](https://regolo.ai/dhh-group/): Regolo is developed within DHH Group, a European cloud infrastructure company (Seeweb S.r.l., Italy). - [Case Studies](https://regolo.ai/case-studies/): Customer stories and production use cases. - [Contacts](https://regolo.ai/contacts/): Discord is the primary support and community channel. - [Terms and Conditions](https://regolo.ai/terms-and-conditions/): Regolo Inference Service Agreement. - [Data Processor Agreement](https://regolo.ai/terms-and-conditions/#data-processor-agreement): DPA for enterprise and regulated-sector customers. - [Privacy Policy](https://regolo.ai/privacy-policy/): Data handling, GDPR compliance, and cookie policy. --- ## API Integration Regolo exposes an OpenAI-compatible REST API. The base URL is `https://api.regolo.ai/v1`. Authentication uses Bearer tokens (API key from dashboard). All major OpenAI SDK client libraries work without modification by changing the `base_url` parameter. Supported endpoints: `/chat/completions`, `/embeddings`, `/audio/transcriptions`, `/images/generations`, `/rerank`. Key properties of all API calls: - Zero data retention: prompts, completions, and audio are not stored or logged after the request completes. - EU-only routing: all traffic stays within Italian data centers. - Autoscaling: no cold-start penalty for Core Models. --- ## Models ### Large Language Models (Chat / Text) | Model | Parameters | Quantization | Context | Input €/1M | Output €/1M | Capabilities | |---|---|---|---|---|---|---| | apertus-70b | 70B | FP16 | 65K | €0.40 | €2.10 | Chat, Tool Use, 1800+ languages, Apache 2.0 | | gpt-oss-120b | 117B MoE (5.1B active) | MXFP4 | — | €1.00 | €4.20 | Reasoning, Chain-of-Thought, Tool Use, Fine-tuning | | gpt-oss-20b | 20B | MXFP4 | — | €0.10 | €0.42 | Fast Inference, Tool Use, Edge-friendly | | Llama-3.1-8B-Instruct | 8B | FP16 | — | €0.05 | €0.25 | Multilingual Chat | | Llama-3.3-70B-Instruct | 70B | FP16 | — | €0.60 | €2.70 | Multilingual Chat, Function Calling | | minimax-m2.5 | 229B MoE (46B active) | FP8 | — | €0.60 | €3.80 | Coding, Agentic Tool Use, Search, Reasoning | | mistral-small-4-119b | 119B hybrid | — | — | €0.50 | €2.10 | Reasoning, Coding (Devstral), Vision, Function Calling | | mistral-small3.2 | 24B | Q4_K_M | — | €0.50 | €2.20 | STEM Reasoning, Vision, Function Calling | | qwen3-coder-next | — | — | Long | €0.50 | €2.00 | Coding, Agents, IDE Integration | | qwen3-vl-32b | 32B | FP8 | — | €0.50 | €2.50 | Vision-Language, Function Calling | | qwen3.5-122b | 122B MoE (10B active) | FP8 | 262K | €1.00 | €4.20 | Reasoning, Vision, Function Calling, 201 languages | | qwen3.5-9b | 9B | FP16 | — | €0.07 | €0.35 | Reasoning, Fast | **Notable model details:** - [apertus-70b](https://regolo.ai/pricing/): Fully open 70B multilingual LLM supporting 1800+ languages with 65K context. Trained on 15T tokens of compliant open data. Supports tool use and agentic workflows. Apache 2.0 licensed, EU AI Act compliant. - [gpt-oss-120b](https://regolo.ai/pricing/): OpenAI open-weight 117B MoE model (only 5.1B active parameters per token). Supports reasoning, chain-of-thought, tool use, and fine-tuning. - [gpt-oss-20b](https://regolo.ai/pricing/): 20B open-weight model optimized for fast, low-latency inference. Strong reasoning and tool use with minimal memory footprint. - [Llama-3.3-70B-Instruct](https://regolo.ai/pricing/): Meta's multilingual 70B parameter model, optimized for instruction-following, dialogue, and function calling. - [minimax-m2.5](https://regolo.ai/pricing/): 229B MoE (46B active), native FP8 quantization. Built for coding, agentic tool use, and search tasks. - [mistral-small-4-119b](https://regolo.ai/pricing/): Unified model combining Instruct, Reasoning (Magistral), and Devstral (coding) capabilities in a single endpoint. - [mistral-small3.2](https://regolo.ai/pricing/): 24B multimodal model (Mistral-Small-3.2-24B-Instruct-2506), optimized for STEM reasoning and robust function calling with vision support. - [qwen3-coder-next](https://regolo.ai/pricing/): Open-weight coding model for agents and local development. Long-context reasoning, efficient IDE integration. - [qwen3.5-122b](https://regolo.ai/pricing/): 122B MoE with Gated DeltaNet hybrid attention, 256 experts, 262K context window, 201 language support, FP8. - [qwen3-vl-32b](https://regolo.ai/pricing/): Most capable vision-language model in the Qwen3 series. Supports image understanding and function calling. ### Embedding Models | Model | Pricing | Notes | |---|---|---| | gte-Qwen2 | €0.001/request | Semantic retrieval, clustering, RAG — 32K context, 30+ languages | | Qwen3-Embedding-8B | €0.001/request | Multilingual text embedding, retrieval, classification, clustering | ### Speech-to-Text | Model | Pricing | Notes | |---|---|---| | faster-whisper-large-v3 | €0.00015/second | Multilingual transcription and translation, CTranslate2 optimized | ### Image Generation | Model | Pricing | Notes | |---|---|---| | Qwen-Image | €0.015/megapixel | Complex text rendering, precise image editing | ### OCR | Model | Pricing | Notes | |---|---|---| | deepseek-ocr-2 | €0.02/request | High-accuracy OCR for documents, PDFs, screenshots, receipts — multi-language, multi-layout | ### Reranking | Model | Pricing | Notes | |---|---|---| | Qwen3-Reranker-4B | €0.01/query | 4B cross-encoder, 100+ languages, long-form contexts | --- ## GPU Cloud Instances (Custom Model Deployment) Deploy any Hugging Face model on dedicated GPU instances. Pay by the minute. | GPU | VRAM | vCPU | RAM | Price/hour | |---|---|---|---|---| | NVIDIA RTX 6000 | 24 GB | 8 | 32 GB | €0.29 | | NVIDIA RTX A6000 | 47.3 GB | 8 | — | — | | NVIDIA RTXPRO 6000 | 96 GB | 16 | 120 GB | €1.25 | | 2× NVIDIA RTXPRO 6000 | 192 GB | 32 | 240 GB | €2.50 | | 4× NVIDIA RTXPRO 6000 | 384 GB | 64 | 480 GB | €5.00 | | NVIDIA H200 | 141 GB | 32 | 256 GB | €2.60 | | 2× NVIDIA H200 | 282 GB | 64 | 512 GB | €5.20 | | 4× NVIDIA H200 | 564 GB | 128 | 1024 GB | €10.40 | --- ## Blog: Recent Posts ### Architecture & Engineering - [Small Language Models Are Growing Up: How to Build a Hybrid Inference Stack Without Sacrificing Quality](https://regolo.ai/small-language-models-are-growing-up-how-to-build-a-hybrid-inference-stack-without-sacrificing-quality/): When SLMs are now strategically useful for latency, cost, and edge-first hybrid architectures. - [AI Agents and Tool Chaining in 2026: How to Build Workflows That Actually Finish the Job](https://regolo.ai/ai-agents-and-tool-chaining-in-2026-how-to-build-workflows-that-actually-finish-the-job/): Building reliable bounded AI agent workflows with robust tool use. - [New LLM Architectures in 2026: What CTOs Should Optimize For Instead of Chasing Benchmarks](https://regolo.ai/new-llm-architectures-in-2026-what-ctos-should-optimize-for-instead-of-chasing-benchmarks/): Llama 4, Claude 4, native multimodality, and what actually matters for production. - [Programmatic Tool Calling: How to Build Smarter LLM Agents on Regolo GPUs](https://regolo.ai/programmatic-tool-calling-how-to-build-smarter-llm-agents-on-regolo-gpus/): Practical guide to function calling and tool orchestration with open models. - [Scale-to-Zero Cold Start Latency: Why Serverless GPU Breaks Real-Time AI (And How to Fix It)](https://regolo.ai/scale-to-zero-cold-start-latency-why-serverless-gpu-breaks-real-time-ai-and-how-to-fix-it/): Diagnosing and solving cold start latency in serverless GPU deployments. - [Multi-Agent AI Systems: Why the "Single Copilot" Is Dying](https://regolo.ai/multi-agent-ai-systems-why-the-single-copilot-is-dying/): Why enterprises are moving from single-agent to specialized multi-agent architectures. - [The Overkill Hardware Trap: Stop Paying H100 Prices for Inference](https://regolo.ai/overkill-hardware-trap-inference-vs-training-gpu-costs/): Matching GPU hardware to workload — why H100s are usually wrong for inference. - [Human-in-the-Loop: Phase or Permanent Fixture for Agentic AI?](https://regolo.ai/human-in-the-loop-autonomous-ai-agents-governance/): Governance patterns for autonomous AI agents, oversight, and accountability. ### Compliance & Privacy - [AI Governance, Copyright, and Enterprise Risk: How to Build a Policy Gateway Before You Ship](https://regolo.ai/ai-governance-copyright-and-enterprise-risk-how-to-build-a-policy-gateway-before-you-ship/): Application-layer control planes for AI governance, EU-hosted APIs, and zero retention. - [AI Data Sovereignty in Europe for LLMs](https://regolo.ai/ai-data-sovereignty-gdpr-compliant-llm-inference-europe/): Practical guide to GDPR-compliant LLM inference in Europe. - [How to Implement GDPR-Compliant AI Inference: A Pragmatic Framework](https://regolo.ai/how-to-implement-gdpr-compliant-ai-inference-a-pragmatic-framework/): Step-by-step framework for integrating LLMs in enterprise applications under GDPR. - [Data Privacy First: CTO Guide to AI Act Compliance](https://regolo.ai/data-privacy-first-ai-act-cto-guide/): EU AI Act obligations, fines, and how to architect compliant AI systems. - [Private AI Coding: Deploy Without Giving Away Your Code](https://regolo.ai/private-ai-coding-deploy-without-giving-away-your-code/): How to use AI coding assistants without sending proprietary code to US cloud providers. - [GitHub Copilot Alternative: Copilot vs Custom LLM Assistant (cheaper and faster)](https://regolo.ai/github-copilot-alternative-copilot-vs-custom-llm-assistant-cheaper-and-faster/): Comparing GitHub Copilot with a self-configured open-model assistant on EU infrastructure. ### Tutorials & Integrations - [Run MiroFish with regolo.ai: A Complete Integration Guide](https://regolo.ai/run-mirofish-with-regolo-ai-a-complete-integration-guide/): Connecting MiroFish simulation tools to the Regolo inference API. - [How to Implement GraphRAG with Regolo.ai Models](https://regolo.ai/how-to-implement-graphrag-with-regolo-ai-models/): Converting text into knowledge graphs for multi-hop reasoning in RAG pipelines. - [Build Faster: LLM as a Service for Developers with Qwen 3.5 122b](https://regolo.ai/build-faster-llm-as-a-service-for-developers-with-qwen-3-5-122b/): Quickstart for developers using LLMaaS to skip GPU management. - [Build ToneCraft: an AI-powered Thunderbird extension for more professional emails (on Regolo GPUs)](https://regolo.ai/build-tonecraft-an-ai-powered-thunderbird-extension-for-more-professional-emails-on-regolo-gpus/): Building a real browser extension that calls Regolo for email tone adjustment. - [Building a Reliable Multi-Agent Competitor Intelligence System with CrewAI](https://regolo.ai/building-a-reliable-multi-agent-competitor-intelligence-system-with-crewai/): Multi-agent patterns with specialized roles using CrewAI on open models. - [From Inbox to Automated CRM: Privacy-First Email RAG with LlamaIndex for EU Developers](https://regolo.ai/from-inbox-to-automated-crm-privacy-first-email-rag-with-llamaindex-for-eu-developers/): Building a private email RAG pipeline with LlamaIndex and Regolo. - [Building a Document Analysis System with Regolo AI API](https://regolo.ai/building-a-document-analysis-system-with-regolo-ai-api/): Automating tender and contract analysis with the Regolo API. - [OpenClaw AI Agent: Build a 24/7 Business Automation Machine](https://regolo.ai/openclaw-ai-agent-build-a-24-7-business-automation-machine/): Marketing pipeline, CRM, competitor monitoring, and content automation with OpenClaw. - [Production-Ready RAG on Open Models: Chunking, Retrieval, Reranking & Evaluation](https://regolo.ai/production-ready-rag-on-open-models-chunking-retrieval-reranking-evaluation/): Full RAG pipeline from chunking strategy to evaluation, running on Regolo. - [Supercharge RAG with Qwen3 and LlamaIndex on Regolo](https://regolo.ai/supercharging-retrieval-with-qwen-and-llamaindex-a-hands-on-guide/): Step-by-step RAG pipeline with Qwen3 LLM+embeddings and LlamaIndex. - [Use OpenAI Agents SDK with LLaMA 3.3 and Regolo (Tool Calling)](https://regolo.ai/use-openais-agents-sdk-with-an-open-model/): Connecting OpenAI Agents SDK to Regolo with open models and function calling. - [Qwen3-Reranker-4B on Regolo: Add a "critical brain" to your RAG in minutes](https://regolo.ai/rerank-models-have-landed-on-regolo-%f0%9f%9a%80/): Cross-encoder reranking to improve retrieval quality in RAG systems. - [Agentic RAG with Regolo + Elysia: Build a "Second Brain"](https://regolo.ai/chat-with-all-your-documents-with-regolo-elysia/): Agentic RAG with Weaviate and Elysia for private document reasoning. - [Orchestrating Predictable AI Agents with Parlant and Regolo](https://regolo.ai/orchestrating-predictable-ai-agents-with-parlant-and-regolo/): Using Parlant to build structured, reliable agent workflows on open models. - [Build Multi-Agent Workflows with crewAI](https://regolo.ai/build-multi-agent-workflows-with-crewai-teams/): Getting started with CrewAI and DeepSeek-R1-70B on Regolo. - [Build AI Agents Visually: Langflow + Regolo](https://regolo.ai/langflow-meets-regolo-ai-how-to-integrate-regolo-ai-into-langflow-in-minutes/): No-code agent prototyping with Langflow connected to Regolo. - [Connect Regolo Models on your Dify Workflows](https://regolo.ai/connect-regolo-models-on-your-dify-workflows/): Integrating Regolo into Dify for drag-and-drop AI workflows. - [Boost Your Workflows with Regolo AI + n8n](https://regolo.ai/boost-your-workflows-with-regolo-ai-on-n8n/): Chat, embeddings, and image generation inside n8n automations. - [Wolf of Wall Street with Regolo + OpenBB: Build Your Private AI Trading Analyst](https://regolo.ai/wolf-of-wall-street-with-regolo-openbb-trading-agent-%f0%9f%92%b8/): Custom AI trading copilot using OpenBB Workspace and Regolo. - [Give Your Coding Agent a Memory: Regolo + Cipher Integration](https://regolo.ai/connect-your-coding-agents-with-regolo-and-cipher/): Persistent memory for coding agents using Byterover Cipher and Regolo. - [From Zero to an Enterprise-Ready AI Agent with Cheshire Cat and Regolo](https://regolo.ai/from-zero-to-an-enterprise-ready-ai-agent-with-cheshire-cat-and-regolo-a-practical-guide-using-only-open-source-llms/): Cheshire Cat + Llama-3.3-70B on Regolo for enterprise agent use cases. - [Privacy-First Email Search: Building a RAG System with LlamaIndex](https://regolo.ai/privacy-first-email-search-building-a-rag-system-with-llamaindex/): Semantic email search over thousands of messages using LlamaIndex. - [Secure & Scalable Meeting Transcription with Python and Regolo](https://regolo.ai/secure-and-private-transcribing-meeting-recordings-with-python-and-regolo-ai/): Splitting audio, transcribing with Whisper API, and managing privacy. - [AI-Driven Cybersecurity: Deploy Beelzebub + Regolo to Trap Hackers](https://regolo.ai/ai-based-cybersecurity-with-regolo-beelzebub%f0%9f%90%9d%f0%9f%9b%a1%ef%b8%8f/): AI-powered honeypots that detect and analyze intrusions using Regolo. - [Build a Private Documentation Chatbot with Flowise and Regolo (No-Code)](https://regolo.ai/seamlessly-integrating-regolo-ai-with-flowise/): Setting up a chatflow in Flowise pointing at the Regolo API. - [Handling big projects with AI in your terminal with Plandex](https://regolo.ai/handling-big-projects-with-ai-in-your-terminal-with-plandex/): Self-hosting Plandex and connecting it to Regolo for large-scale coding tasks. - [AI in your terminal with Aider and regolo.ai](https://regolo.ai/ai-in-your-terminal-with-aider-and-regolo-ai/): Aider pair-programming in Git projects using Regolo as the LLM backend. - [Roo-Code: AI-Powered Autonomous Coding in VSCode](https://regolo.ai/roo-code-ai-powered-autonomous-coding-in-vscode/): Autonomous coding agent in VSCode using Regolo as the inference endpoint. - [Theia, the AI integrated VSCode alternative from Eclipse](https://regolo.ai/theia-the-ai-integrated-vscode-alternative-from-eclipse/): Eclipse Theia + Theia AI framework with Regolo for AI-native IDEs. ### Benchmarks & Cost Optimization - [GPU Inference Costs Exploded by 2026: The Big Challenge for Italian Startups](https://regolo.ai/gpu-inference-costs-exploded-by-2026-the-big-challenge-for-italian-startups-and-enterprises-and-how-to-control-them/): Italian cloud market at €8.13B in 2025 (+20% YoY) and how to control GPU costs. - [Cloud Bill Shock: How Hidden Egress Fees Crush Startup Budgets & How to Escape](https://regolo.ai/cloud-bill-shock-how-hidden-egress-fees-crush-startup-budgets-how-to-escape/): Anatomy of unexpected cloud bills and how EU providers eliminate egress fees. - [DeepSeek-OCR-2 vs GLM-OCR vs PaddleOCR: The New Era of Intelligent OCR (2026 Benchmarks)](https://regolo.ai/deepseek-ocr-vs-glm-ocr-vs-paddleocr-benchmark-2026/): Comparative benchmarks for VLM-based OCR on complex documents. - [Guide to High-Performance VLM-OCR Recipes: Unlock DeepSeek-OCR](https://regolo.ai/guide-to-high-performance-vlm-ocr-recipes-unlock-deepseek-ocr/): Practical recipes for high-accuracy OCR using vision-language models. - [AI Footprint: How to Measure and Reduce LLM Inference Impact](https://regolo.ai/ai-footprint-how-to-measure-and-reduce-llm-inference-impact/): Measuring infrastructure cost per request and reducing environmental footprint. - [Testing, Transparency, and Carbon Awareness: Inside the Regolo Playground](https://regolo.ai/testing-transparency-and-carbon-awareness-inside-the-regolo-playground/): How the Regolo Playground exposes model metadata, latency, and carbon estimates. ### Product & Release Notes - [Qwen3.5 122B, Qwen3.5 9B, and Mistral Small 4 119B Are Now Available on Regolo](https://regolo.ai/qwen3-5-122b-qwen3-5-9b-and-mistral-small-4-119b-are-now-available-on-regolo/): Release announcement for three new models. - [Making use of reasoning models with regolo.ai](https://regolo.ai/making-use-of-reasoning-models-with-regolo-ai/): Guide to chain-of-thought and reasoning models via the OpenAI-compatible API. - [Fast Whisper: The Best Open-Source Speech-to-Text Solution](https://regolo.ai/fast-whisper-the-best-open-source-speech-to-text-solution/): Overview of faster-whisper-large-v3 and the Regolo STT API. - [Cloud LLM Hosting: Scalable, Private, and Green Infrastructure](https://regolo.ai/what-is-cloud-llm-hosting-a-european-take-on-scalable-private-and-green-ai-infrastructure/): Analysis of LLM cloud hosting in Europe — sovereignty, sustainability, and transparency. ### Sustainable Computing - [Why Open Source and Green Infrastructure Are at the Core of Regolo.ai's European LLM Platform](https://regolo.ai/why-open-source-and-green-infrastructure-are-at-the-core-of-regolo-ais-european-llm-platform/): 100% renewable energy, open models, EU data centers: why they are design decisions not marketing. ### Audio & Speech - [Dictation Tool on Linux: hyprwhspr + Regolo (Whisper)](https://regolo.ai/dictation-tool-on-linux-hyprwhspr-regolo-whisper/): Keyboard shortcut dictation on Linux using hyprwhspr and the Regolo Whisper API. - [Thunderbird + ThunderAI + Regolo: AI email summaries & replies](https://regolo.ai/thunderbird-meets-ai-how-to-use-thunderai-with-regolo/): Configuring ThunderAI plugin with Regolo for AI-powered email summaries and replies. ### Case Studies & Community - [NEITCUS® Business Intelligence + regolo.ai](https://regolo.ai/neitcus-business-intelligence-regolo-ai/): Financial services automation with AI-powered BI using Regolo. - [What is OpenClaw? Your Open-Source AI Assistant](https://regolo.ai/what-is-openclaw-your-open-source-ai-assistant/): OpenClaw (MIT license) as an open-source AI assistant connected to Regolo. - [Build an Internal Knowledge Bot with Clawdbot: Embeddings + Rerank + Chat in 30 Minutes](https://regolo.ai/build-an-internal-knowledge-bot-with-clawdbot-embeddings-rerank-chat-in-30-minutes/): Building a private internal knowledge assistant combining Regolo embeddings, reranking, and chat. --- ## Blog Categories - [Tutorial & How-to](https://regolo.ai/category/tutorial/): Step-by-step integration guides and code walkthroughs. - [Benchmarks & Cost Optimization](https://regolo.ai/category/benchmarks-cost-optimization/): Model comparisons, performance tests, and cost analysis. - [Compliance and Privacy](https://regolo.ai/category/compliance-and-privacy/): GDPR, EU AI Act, data sovereignty, and enterprise risk. - [Product & Release Notes](https://regolo.ai/category/product-release-notes/): New models, features, and platform updates. - [Self-Hosting & DevOps](https://regolo.ai/category/self-hosting-devops/): Deploying open-source tools with Regolo as the inference backend. - [Sustainable Computing](https://regolo.ai/category/sustainable-computing/): Environmental impact and green AI infrastructure. - [Case Studies & Stories](https://regolo.ai/category/case-studies-community-stories/): Real-world uses from customers and the community. - [Events & Workshops](https://regolo.ai/category/events-workshops/): Meetups, workshops, and developer events. --- ## Community & Support - [Discord](https://discord.gg/myuuVFcfJw): Primary support channel and community hub. - [Reddit](https://www.reddit.com/r/regolo_ai/): Community discussions and feedback. - [LinkedIn](https://www.linkedin.com/company/regolo-ai/): Product updates and announcements. - [X / Twitter](https://x.com/regolo_ai): News and developer threads.