# Hybrid routing is the default: why managed open weights beat self-hosting and closed APIs
The debate over whether open weights can power mission-critical software has effectively ended. Two major ecosystem releases last week marked the definitive inflection point: Mistral Large 4 shipped under open weights on October 6, and GLM-5.3 entered general availability on AWS Bedrock on October 5.
When frontier weights deploy directly into hyperscaler catalogs, technical leadership no longer needs to defend *why* an application should run open models. The remaining architectural question is purely economic: how much does the business actually save, and where should that infrastructure live?
SOVEREIGN EUROPEAN INFERENCE
### Run DeepSeek, Qwen, and GLM in Europe with Zero Data Retention
Get 600 Million tokens on the Regolo Core plan (€39/mo flat, ~€0.065/1M). Switch endpoints in 1 line of code with full OpenAI SDK compatibility on 100% green datacenters.
[ Start 30-day trial → ](https://regolo.ai/pricing/?utm_source=blog&utm_medium=bento_cta&utm_campaign=deepseek-flash-mid) Free credits included · Live in 60s
✓ 100% EU Green Datacenters ✓ Certified ZDR
---
## The 57% dividend: why single-model architectures are a legacy pattern
Treating an AI application as an indivisible call to a single frontier endpoint is the primary driver of cloud budget overruns. In multi-agent pipelines, 70% to 80% of total token volume is consumed by deterministic, bounded tasks:
- intent classification and ticket categorization.
- regex-adjacent JSON entity extraction from documents.
- factual summarization of grounded RAG context chunks.
- unit test scaffolding and syntax verification.
Dispatching these routine stages to premium flagships charging $10.00 to $50.00 per million tokens burns capital on problems that open models solve with identical accuracy.
```
[Inbound user prompt]
│
▼
┌─────────────────────────────────────────────────────────┐
│ Brick dynamic router (analyzes complexity & capability) │
└─────────────────────────────────────────────────────────┘
│
┌─────┴───────────────────────────────────────┐
▼ ▼
[80% Routine & bounded volume] [20% Deep frontier logic]
- structured JSON extraction - contradictory clause synthesis
- triage & semantic search summarization - multi-file strategic reasoning
│ │
▼ ▼
┌─────────────────────────────────┐ ┌───────────────────────────────┐
│ Managed open-weight pool │ │ Frontier reasoning tier │
│ (Regolo Core flat subscription) │ │ (OpenAI Astra / Claude Fable) │
│ ~€0.065 / 1m tokens blended │ │ pay-as-you-go fallback only │
└─────────────────────────────────┘ └───────────────────────────────┘Code language: JavaScript (javascript)
```
Dynamic mixture-of-models gateways like Brick evaluate request complexity and capability requirements on the fly. By routing routine workloads to open models while reserving frontier reasoning for ambiguous multi-step deduction, engineering organizations routinely capture an immediate **57% reduction in aggregate monthly inference spend**.
---
## The break-even reality: self-hosting versus managed inference APIs
When teams recognize the quality of models like Mistral Large 4 and GLM-5.3, the immediate impulse is often to spin up private GPU instances on hyperscaler infrastructure.
However, detailed break-even analyses from infrastructure benchmarks demonstrate that self-hosting introduces a fundamentally different cost structure:
```
Effective cost per 1M tokens ($) based on daily token volume
$3.00 ┼
│ ● Self-hosted 8x H100 cluster (low utilization penalty)
$2.00 ┼───╲
│ ╲
$1.00 ┼─────● Break-even zone:
│ ╲ 10M - 30M tokens/day at >80% utilization
$0.20 ┼───────╲───────────────────────────────────┼────────────────────────────────────────
│ ╲ │
$0.07 ┼─────────● Managed open API / Regolo flat pool (predictable baseline)
└─────────┬─────────────────────────────────┬────────────────────────────────────────▶
1M tokens/day 15M tokens/day 50M tokens/dayCode language: PHP (php)
```
Self-hosting economics are governed by three unforgiving operational constraints:
### 1. The idle capacity penalty
Cloud GPU rentals (such as 8x H100 or H200 nodes required to serve a 70B+ or MoE architecture with acceptable time-to-first-token) incur a flat hourly rate regardless of traffic. If production traffic drops during nights or weekends, the effective cost per generated token spikes dramatically.
### 2. The 80% utilization barrier
To reach cost parity with managed endpoints, a self-hosted cluster must maintain a sustained utilization rate above 80%. In real-world enterprise applications with peak-to-trough ratios of 4:1, maintaining 80% average utilization without dropping requests during spikes requires sophisticated dynamic scaling infrastructure that adds substantial engineering overhead.
### 3. The 10M to 30M tokens per day threshold
Below 10 to 30 million tokens per day, the fully burdened cost of self-hosting (including GPU rental, networking egress, model serving maintenance, and SRE time) averages **roughly 10 times higher** than consuming the identical model via a managed inference API.
| Operational metric | Self-hosted GPU cluster | Managed open-weight API | Regolo flat subscription pool |
|---|---|---|---|
| Monthly cost structure | fixed hardware commitment ($4,000 to $12,000/mo) | variable metered consumption | fixed flat quota (€39 or €89/mo) |
| Break-even volume | >10M to 30M tokens/day sustained | 0 to 10M tokens/day | predictable savings from day one |
| Utilization dependency | requires >80% constant saturation | zero idle capacity cost | zero idle penalty within quota |
| Operational burden | serving engines, vLLM tuning, cluster ops | zero infrastructure ops | zero infrastructure ops |
| Unit cost per 1M tokens | $0.80 to $2.50 at realistic load | $0.20 to $1.50 metered | **~€0.065 blended** |
| Data sovereignty & ZDR | customer responsibility | vendor dependent | **certified EU Zero Data Retention** |
---
## Why managed open weights beat proprietary closed endpoints
For organizations running below 30 million tokens daily, managed open weights occupy the operational sweet spot:
```
[Closed APIs: OpenAI / Claude] ──▶ Cost: $2.00 to $50.00 / 1M ──▶ High vendor lock-in
│
▼ (10x cost reduction)
[Managed open APIs: Regolo] ──▶ Cost: ~€0.065 / 1M blended ──▶ Zero lock-in, EU sovereign
│
▲ (Eliminates devops & idle hardware waste)
[Self-hosted GPU clusters] ──▶ Cost: $0.80 to $2.50 / 1M ──▶ High ops overhead below 30M tokens/dayCode language: PHP (php)
```
Managed inference providers eliminate the infrastructure maintenance burden while delivering open-source weights at roughly **one-tenth the price of closed APIs like Claude Sonnet or OpenAI GPT-6 Sol**.
### The unit economics of a production SaaS chatbot: 50 daily conversations
To ground this cost difference in a real-world scenario, consider the customer support or assistant chatbot of an active B2B SaaS application handling a moderate baseline volume:
- **traffic volume**: 50 user conversations per day (1,500 conversations per month).
- **conversation profile**: average 6 conversational turns per session, retrieving grounded documentation context chunks (roughly 2,500 input tokens and 500 output tokens per turn). For a complete walkthrough on assembling this retrieval architecture, see our hands-on guide to [building an agentic RAG system that reasons](https://regolo.ai/agentic-rag-tutorial-build-retrieval-that-reasons/) and our tutorial on [building a controlled assistant with LangChain, Qdrant, and Regolo](https://regolo.ai/build-a-controlled-analytics-assistant-with-langchain-qdrant-and-regolo/).
- **monthly token volume**: 22.5 million input tokens and 4.5 million output tokens (27 million total tokens per month).
Under conventional pay-as-you-go metered pricing from closed API providers, output tokens and multi-turn context accumulation create an expensive recurring bill. Under Regolo, this entire workload is absorbed well within the monthly Core subscription quota:
```
Monthly API bill for a SaaS chatbot (50 conversations/day, 27M tokens/mo)
──────────────────────────────────────────────────────────────────────────────────
Anthropic Claude Sonnet 5.5 ($2 in / $10 out) : $90.00 ████████████████████████
OpenAI GPT-6 Sol ($2 in / $10 out) : $90.00 ████████████████████████
Anthropic Claude Opus 5.5 ($4 in / $20 out) : $180.00 ████████████████████████████████████████
OpenAI GPT-6 Astra ($10 in / $50 out) : $450.00 ██████████████████████████████████████████████████████████████████████████
Hosted open-weight API (pay-as-you-go) : $32.40 ████████▌
Regolo Core flat subscription (600M pool) : €39.00 ██████████ (Fixed predictable tier, 95% capacity headroom left)Code language: JavaScript (javascript)
```
| Serving architecture & Provider | Pricing model | Monthly cost (27M tokens) | Marginal cost per conversation | Effective runway / capacity |
|---|---|---|---|---|
| OpenAI GPT-6 Astra | metered pay-as-you-go | $450.00 | $0.30 per conversation | bills expand linearly with usage |
| Anthropic Claude Opus 5.5 | metered pay-as-you-go | $180.00 | $0.12 per conversation | bills expand linearly with usage |
| Anthropic Claude Sonnet 5.5 | metered pay-as-you-go | $90.00 | $0.06 per conversation | bills expand linearly with usage |
| OpenAI GPT-6 Sol | metered pay-as-you-go | $90.00 | $0.06 per conversation | bills expand linearly with usage |
| Hosted open-weight pay-as-you-go | metered usage ($0.60 in / $1.80 out) | $32.40 | ~$0.021 per conversation | variable monthly volatility |
| **Regolo Core** **Subscription** | **flat quota pool (600M tokens)** | **€39.00 flat** | **€0.026 per conversation** | **absorbs up to 1,100 conversations/day without paying an extra cent** |
The difference is not merely the initial invoice saving: it is headroom and predictability. On metered APIs, a doubling of chatbot usage or an unexpected spike in conversational turns doubles the monthly invoice. Under the Regolo Core plan, the same €39 monthly fee covers up to 600 million tokens: allowing that same SaaS chatbot to scale from 50 to more than 1,000 conversations per day without incurring a single euro in overage charges. (To examine how real-world public assistants deploy Regolo under bounded requirements, review our case study on [developing a citizen-facing chatbot on sovereign infrastructure](https://regolo.ai/when-research-meets-artificial-intelligence-a-bachelors-thesis-chooses-regolo-to-develop-a-chatbot-for-citizens/)).
Furthermore, moving from proprietary models to open weights removes commercial platform risk:
- weights are downloadable and reproducible, eliminating abrupt vendor deprecations.
- wire formats adhere to open standards like OpenAI-compatible endpoints and MCP.
- European sovereign hosting guarantees GDPR compliance without cross-border telemetry.
---
## Solving the agentic cost surge with Regolo flat subscription pools
While managed open APIs cut costs by 90% compared to closed flagships, standard pay-as-you-go metering introduces a secondary risk in agentic systems: token volume expansion.
Autonomous agents generate unexpected token surges through multi-turn tool loops, chain-of-thought scratchpads, and automated self-correction steps. Under pure usage-based billing, a looping agent can rapidly consume thousands of dollars in unbudgeted inference.
Regolo solves this economic volatility by pairing intelligent model routing with predictable token volume quotas:
- **Core Plan**: €39 per month for a pool of 600 million tokens (**€0.065 per million tokens**).
- **Boost Plan**: €89 per month for a pool of 1.5 billion tokens.
```
import os
from openai import OpenAI
# Connect to Regolo sovereign inference using the standard OpenAI client
client = OpenAI(
base_url="https://api.regolo.ai/v1",
api_key=os.environ.get("REGOLO_API_KEY")
)
# Route requests to high-performance open weights under your flat pool
response = client.chat.completions.create(
model="glm5.2",
messages=[
{"role": "system", "content": "you are an autonomous extraction agent. output valid json only."},
{"role": "user", "content": "parse transaction log: id=8924 status=completed latency=12ms amount=142.50"}
],
temperature=0.1
)
print(response.choices[0].message.content)Code language: Python (python)
```
Under this architecture, your routing layer dispatches high-volume extraction, parsing, and coding tasks to models like GLM-5.2 or Qwen3.8-27B running inside your Regolo subscription pool.
---
## Two production implementation patterns for a SaaS chatbot
When moving an enterprise assistant off metered closed endpoints, engineering organizations typically select between two validated architectural patterns depending on workflow diversity and team operational maturity. Both patterns assume a standard SaaS support volume of **50 conversations per day** (1,500 conversations per month, consuming approximately 27 million total tokens).
### Pattern 1: single-model dedicated flow with Regolo Core (Qwen3.8-27B)
For applications where queries remain largely within bounded product workflows (such as account management, standard onboarding, documentation search, and routine ticket triage), deploying a single high-efficiency model under a flat quota pool provides maximum architectural simplicity:
- **workhorse engine**: `qwen3.8-27b`, delivering 1,679 Elo on frontend and UI generation, rapid time-to-first-token, and low memory footprint.
- **operational advantage**: zero router latency, single client configuration, and deterministic response profiles across all user sessions.
- **cost for 50 daily conversations**: **€39.00/month flat** (consuming only 27M tokens out of the 600M token monthly pool, leaving 95.5% capacity headroom).
```
Pattern 1: Dedicated Qwen3.8-27B Architecture
──────────────────────────────────────────────────────────────────────────────────
[User Query: 50 conv/day]
│
▼
┌───────────────────────────────────────────────┐
│ SaaS Application & RAG Context Assembly │
└───────────────────────────────────────────────┘
│
▼ (100% of queries dispatched directly)
┌───────────────────────────────────────────────┐
│ Regolo Sovereign European Inference │
│ Model: qwen3.8-27b │
│ Quota pool: 600M tokens / month (€39/mo) │
└───────────────────────────────────────────────┘
│
▼ (Latency: 5-12ms TTFT)
[Generated Assistant Response]
Cost for 50 conv/day: €39.00 / month (vs $90.00 on Claude Sonnet, $450.00 on Astra)
```
For teams with relatively uniform ticket distribution, Qwen3.8-27B under [Regolo Core](/pricing) provides immediate 90%+ cost relief compared to Claude Sonnet ($90/mo) or GPT-6 Sol ($90/mo) without adding architectural moving parts.
---
### Pattern 2: multi-tier dynamic routing with Brick Complexity Pro
For complex SaaS platforms where user queries range from trivial UI questions to intricate API debugging, multi-tenant database permissions, or custom webhook configuration, dynamic routing unlocks maximum quality at minimum unit cost:
- **routing proxy**: `brick-complexity-pro` (hosted on Regolo or self-deployed), which inspects query ambiguity, reasoning depth, and context size in under 15 milliseconds.
- **low-to-medium complexity tier (80% of volume = 40 conv/day)**: dispatched to `qwen3.8-27b` under the Regolo Core subscription pool.
- **high complexity tier (20% of volume = 10 conv/day)**: dispatched dynamically to `glm-5.2` (753B MoE reasoning) only when multi-step synthesis or cross-document reasoning is detected.
- **measured efficiency gain from Brick routing**: as documented in Brick's evaluation benchmarks on 5,504 queries, capability-and-complexity routing achieves 76.98% accuracy at a normalized compute cost of 1.5x the cheapest baseline model, compared to 6.0x the baseline cost for unrouted large frontier backends (a ~4x normalized cost reduction). On the 50 conversations/day workload, this prevents the application from over-allocating heavy reasoning compute to simple queries, preserving maximum quota headroom.
- **cost for 50 daily conversations**: **€39.00/month flat** (both Qwen3.8-27B and GLM-5.2 draw directly from the Regolo sovereign subscription quota, requiring €0.00 in variable overages).
```
Pattern 2: Dynamic Brick Routing Architecture
──────────────────────────────────────────────────────────────────────────────────
[User Query: 50 conv/day]
│
▼
┌───────────────────────────────────────────────┐
│ SaaS Application & RAG Context Assembly │
└───────────────────────────────────────────────┘
│
▼
┌───────────────────────────────────────────────┐
│ Brick Complexity Pro Classifier (<15ms) │
│ Single forward routing pass (no cascade waste)│
└───────────────────────────────────────────────┘
│
┌─────┴─────────────────────────────────────┐
▼ (80% volume: 40 conv/day) ▼ (20% volume: 10 conv/day)
[Routine & Product Queries] [Complex Code & Multi-Doc Reasoning]
│ │
▼ ▼
┌───────────────────────────────┐ ┌────────────────────────────────┐
│ Regolo Sovereign Pool │ │ Regolo Sovereign Pool │
│ Model: qwen3.8-27b │ │ Model: glm-5.2 (753B MoE) │
│ Fast extraction & support │ │ Deep reasoning & edge cases │
└───────────────────────────────┘ └────────────────────────────────┘
│ │
└─────────────────────┬─────────────────────┘
▼
[Generated Assistant Response]
Cost for 50 conv/day: €39.00 / month flat (vs $180.00 on Opus 5.5, $450.00 on Astra)Code language: JavaScript (javascript)
```
### Consumption comparison: Dedicated Qwen3.8-27B vs Brick Complexity Pro vs Origin APIs
To understand how the two patterns consume quota compared to unmanaged alternatives under a 50 conversations/day baseline (27 million total tokens/month):
| Architectural setup | Monthly billing model | Measured accuracy profile | Effective resource consumption & efficiency | Monthly invoice |
|---|---|---|---|---|
| **Origin Closed APIs** (e.g. GPT-6 Astra / Claude Sonnet) | metered pay-as-you-go | ~75% to 80% accuracy | 27M tokens billed at $2.00–$50.00/1M (zero headroom, linear cost growth) | **$90.00 to $450.00** |
| **Pattern 1: Dedicated Qwen3.8-27B on Regolo** | flat subscription (€39/mo pool) | 71.2% in-domain accuracy | 27M tokens drawn from 600M quota (4.5% quota consumed, 95.5% headroom) | **€39.00 flat** |
| **Pattern 2: Brick + Regolo Pool** (Qwen 80% / GLM 20%) | flat subscription (€39/mo pool) | 76.98% accuracy (matches top frontier) | 27M tokens drawn from 600M quota; 4x lower normalized compute vs all-frontier | **€39.00 flat** |
Under Pattern 1, the entire workload is served by Qwen3.8-27B, consuming only 4.5% of your 600M monthly token quota with lowest possible latency. Under Pattern 2, adding Brick routes the 20% most difficult queries to GLM-5.2: lifting accuracy from ~71% to 77% while still fitting comfortably within the exact same €39 monthly subscription pool. In both cases, the SaaS business gains predictable cost control and European data sovereignty.
| Dimension | Pattern 1: Qwen3.8-27B | Pattern 2: Brick Routing | Metered Closed API (Sonnet / Astra) |
|---|---|---|---|
| Primary model backend | `qwen3.8-27b` exclusively | `qwen3.8-27b` (80%) + `glm5.2` (20%) | Claude Sonnet 5.5 / GPT-6 Astra |
| Architecture complexity | minimal: direct single endpoint | moderate: dynamic complexity gateway | single vendor lock-in endpoint |
| Router latency overhead | 0 ms | 10–15 ms classifier pass | 0 ms |
| Handling of complex edge cases | good on standard support flows | optimal: escalates to 753B MoE reasoning | high accuracy at extreme token pricing |
| Monthly cost (50 conv/day) | **€39.00 / month flat** | **€39.00 / month flat** | **$90.00 to $450.00 / month variable** |
| Cost per conversation | **€0.026** | **€0.026** | **$0.06 to $0.30** |
| Scaling headroom without extra cost | absorbs up to 1,100 conv/day | absorbs up to 1,100 conv/day | zero headroom: bills grow linearly |
Both patterns dismantle the legacy assumption that capable business assistants require unbudgeted per-token premium fees.
---
## Extensibility and control: custom model pools and harness integration with Brick
With Brick open-source release of [on our GitHub account](https://github.com/regolo-ai/brick-SR1), you can configure bespoke model pools across any OpenAI-compatible provider, or connect the router directly into terminal coding harnesses like Claude Code and OpenAI Codex.
### Defining a customized model pool
Brick evaluates each inbound query across six canonical capability vectors: coding, creative synthesis, instruction following, mathematical reasoning, agentic planning, and world knowledge. You can construct custom pools in `config.yaml`, mixing sovereign European endpoints, local vLLM instances, and proprietary fallback APIs:
```
Brick Custom Pool Topology
──────────────────────────────────────────────────────────────────────────────────
┌─────────────────────────────────────────────────┐
│ Client Application / Coding Agent Harness │
└─────────────────────────────────────────────────┘
│
▼ (model: "brick")
┌─────────────────────────────────────────────────┐
│ Brick MoM Gateway (Local or Docker container) │
│ - ModernBERT capability scoring │
│ - Qwen-based complexity classifier │
└─────────────────────────────────────────────────┘
│
┌──────────────────────────────────────┼──────────────────────────────────────┐
▼ ▼ ▼
┌───────────────────────────────┐ ┌───────────────────────────────┐ ┌───────────────────────────────┐
│ Sovereign EU Endpoint (Regolo)│ │ Local / Private Inference │ │ Proprietary Frontier Fallback │
│ - Qwen3.8-27B (fast support) │ │ - vLLM or Ollama instance │ │ - OpenAI GPT-6 Astra │
│ - GLM-5.2 (MoE reasoning). │ │ - Private on-prem models │ │ - Claude Opus 5.5 │
└───────────────────────────────┘ └───────────────────────────────┘ └───────────────────────────────┘Code language: PHP (php)
```
The continuous cost-versus-quality trade-off slider (`math.routing_preference` from `-1` for maximum economy to `+1` for maximum quality) allows operators to dynamically adjust how aggressively the gateway downshifts queries to lightweight backends.
### Plug-and-play integration with Claude Code and Codex
For developers building autonomous software systems, Brick provides dedicated profiles that integrate directly with modern terminal harnesses:
- **Claude Code integration**: running `brick start claude` wires Brick into Claude Code's CLI session. Developers select `brick-claude` in the `/model` picker, routing each prompt turn dynamically between Haiku, Sonnet, and Opus depending on coding complexity.
- **OpenAI Codex integration**: running `brick start codex` materializes an OpenAI-compatible bridge, allowing Codex to draw from open-weight models on Regolo or local backends without altering workflow habits.
- **Unified single endpoint**: regardless of how many providers sit behind the gateway, your codebase communicates with a single standard endpoint using `model: "brick"`.
To explore configuration options, review benchmark grading scripts, or self-host the gateway, inspect the [Brick GitHub repository](https://github.com/regolo-ai/brick-SR1).
---
SOVEREIGN EUROPEAN INFERENCE
### Run DeepSeek, Qwen, and GLM in Europe with Zero Data Retention
Get 600 Million tokens on the Regolo Core plan (€39/mo flat, ~€0.065/1M). Switch endpoints in 1 line of code with full OpenAI SDK compatibility on 100% green datacenters.
[ Start 30-day trial → ](https://regolo.ai/pricing/?utm_source=blog&utm_medium=bento_cta&utm_campaign=deepseek-flash-mid) Free credits included · Live in 60s
✓ 100% EU Green Datacenters ✓ Certified ZDR
---
## Frequently Asked Questions (FAQ)
### How much can enterprises save by implementing dynamic hybrid model routing?
Engineering teams that deploy dynamic mixture-of-models routing gateways report an average operating cost reduction of 57%. By analyzing prompt complexity and required capabilities in real time, the router offloads deterministic tasks like extraction and summarization to cost-effective open weights while reserving expensive frontier models strictly for complex multi-step reasoning.
### What is the economic break-even threshold between self-hosting LLMs and managed open APIs?
Self-hosting dedicated GPU hardware only breaks even at sustained workloads above 10 to 30 million tokens per day with continuous hardware utilization exceeding 80%. Below that threshold, the combined burden of idle GPU hourly rentals, cluster engineering, and maintenance makes self-hosting approximately 10x more expensive than consuming identical models through managed inference APIs.
### Why do managed open-weight inference providers beat closed APIs for production SaaS chatbots?
Managed open-weight providers deliver enterprise-grade accuracy at approximately one-tenth the cost of proprietary closed endpoints. Furthermore, flat subscription pools like Regolo Core (€39/month for 600M tokens) protect operating margins against token volume surges caused by autonomous agent loops and deep chain-of-thought scratchpads, allowing a 50 conversations/day assistant to scale past 1,000 conversations/day with zero incremental bill increases.
### How does Brick evaluate query complexity without introducing latency bottlenecks?
Brick executes a single forward routing pass in under 15 milliseconds using a compact classifier running on standard CPU infrastructure. Unlike speculative cascade architectures (such as FrugalGPT) that call models sequentially until an answer passes verification, Brick predicts capability and difficulty upfront, completely avoiding cascade retry token waste and latency degradation.
### Can developers customize model pools or use Brick with developer coding tools?
Yes. Brick is open-source under the Apache 2.0 license on GitHub. Developers can define custom model pools combining sovereign European endpoints on Regolo, local vLLM/Ollama nodes, and proprietary fallbacks. Brick also includes native plug-and-play profiles for terminal coding harnesses, enabling dynamic multi-model routing inside Claude Code and OpenAI Codex through a single endpoint.
---
### Resources and documentation
- [Regolo flat subscription pools and pricing tiers](https://regolo.ai/pricing/)
- [Brick mixture-of-models routing gateway repository on GitHub](https://github.com/regolo-ai/brick-SR1)
- [Regolo engineering labs and architectural guides](https://regolo.ai/labs/)
---
## Ship Private AI. Not Infrastructure.
You have the private AI App architecture, bow give it an inference layer built for production.
**Regolo** gives European teams fast, OpenAI-compatible access to Mistral, Llama, Qwen, DeepSeek, GLM, and more — with zero data retention, EU data residency, and no new SDK to learn.
Change your `base_url`. Keep your LangChain code. Start shipping.
### 🚀 [Start your 30-day free trial →](https://regolo.ai/?utm_source=blog&utm_medium=cta&utm_campaign=private-rag)
Build, test, and deploy with no infrastructure to maintain.
**No credit card. No migration project. No compromise on data control.**
### 💬 [Join the Regolo Discord →](https://discord.gg/bqGrVJHeF)
Meet builders working on private RAG, local LLMs, LangChain, Ollama, and production AI systems. Share your setup, get feedback from the community, and speak directly with the Regolo team.
### 🤝 [Talk to an AI Infrastructure Engineer →](https://regolo.ai/contact?utm_source=blog&utm_medium=cta&utm_campaign=private-rag)
Running a sensitive workload, scaling beyond a proof of concept, or assessing a managed EU inference provider? Get a tailored architecture and commercial proposal for your team.
### 📂 [Clone the GitHub repository →](https://github.com/regolo-ai/tutorials/)
Get the full implementation from this guide: ingestion scripts, ChromaDB setup, hybrid retrieval, the **30-Question RAG Floor**, evaluation examples, and deployment configuration.
> **Private AI should not require a private data center.**
> Regolo gives your team an EU-native path from local experimentation to production-grade inference.
---
### Build with Regolo
- **Discord:** [Join the community →](https://discord.gg/bqGrVJHeF)
- **GitHub:** [Explore open-source workflows →](https://github.com/regolo-ai/tutorials/)
- **X / Twitter:** [Follow @regolo\_ai →](https://x.com/regolo_ai)
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- **Contact:** [Talk to the team →](https://regolo.ai/contact)
---
*Built with ❤️ by the Regolo team. Questions? [regolo.ai/contact](https://regolo.ai/contact)* or chat with us on [Discord](https://discord.gg/bqGrVJHeF)