Unified API
One base URL, one key, one request format. Fully compatible with the OpenAI SDKs you already use, so migration is a config change rather than a rewrite.
Model-as-a-Service
One API. Every major model.
Infoloop gives AI teams a single, OpenAI-compatible endpoint to the world's leading language models — with routing, failover, usage visibility, and unit economics better than going direct.
Drop-in compatible with the OpenAI SDK, and with the Anthropic Messages API. Change two lines, keep your code.
# Point the OpenAI SDK at Infoloop — nothing else changes from openai import OpenAI client = OpenAI( base_url="https://linz-cloud.com/v1", api_key="INFOLOOP_API_KEY", ) resp = client.chat.completions.create( model="claude-sonnet-5", messages=[{"role": "user", "content": "Hello"}], )
Coverage
The models your product needs, behind one key
Western and Chinese frontier models behind one key, delivered through enterprise cloud platforms. Switch providers by changing a model string — no second contract, no second integration, no second invoice.
Global frontier models
Anthropic Claude
OpenAI GPT
Chinese models
DeepSeek
Alibaba Qwen
Moonshot Kimi
Zhipu GLM
MiniMax
ByteDance Doubao
Tencent Hunyuan
Baidu ERNIE
Delivered through enterprise cloud platforms
Amazon Bedrock
Microsoft Azure
Text, vision and long-context models, plus embeddings. Ask us for the current model list, regional availability and per-model pricing.
Our gateway
Linz Cloud
Speaks the OpenAI Chat Completions API and the Anthropic Messages API, so most existing clients and SDKs work against it unchanged.
Platform
Built for teams shipping AI products, not managing vendors
Every hour your team spends on procurement, quota tickets and provider outages is an hour not spent on your product.
Routing & failover
Requests are routed across redundant upstream channels, including enterprise cloud platforms. When one throttles or degrades, traffic moves automatically instead of surfacing as errors in your product.
Usage & spend visibility
Token counts and cost broken down by model, by key and by period — so you can attribute spend to features and customers instead of guessing at a single monthly figure.
Better unit economics
We buy at volume and pass the benefit on. Teams typically pay less through us than at published list prices, with no minimum commitment to get there.
Capacity that keeps up
Quota is planned ahead of your growth rather than raised after you hit a wall. Tell us where your traffic is going and we provision for it in advance.
Engineers, not ticket queues
You get a direct channel to people who know your workload. Integration help, model selection advice and incident response come from the same team.
Why Infoloop
Fewer vendors. Fewer surprises.
Going direct to four model providers means four contracts, four billing relationships, four sets of rate limits and four different failure modes. We collapse that into one.
- One contract and one invoice covering every model you use, in a single currency.
- No procurement cycle — you can be making production calls the same week you talk to us.
- Provider-agnostic by design. When a better or cheaper model ships, you evaluate it by changing a string.
- Commercial terms that fit a growing company, including payment terms rather than prepayment.
- Multi-region coverage so you can serve traffic close to your users.
- Western and Chinese models side by side under one key — useful if you serve both markets, or want to route cheaper traffic to a lower-cost model.
How it works
From first conversation to production
Most teams are running real traffic through us within days, not procurement quarters.
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Tell us your workload
Which models, roughly what volume, which regions, and what your latency and context requirements look like.
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Get keys and quota
We provision your access with quota sized to your projected traffic, and confirm pricing for the models you will actually use.
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Point your SDK at us
Change the base URL and the key. Your existing OpenAI-compatible code keeps working — run it side by side until you are satisfied.
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Scale with support
We watch capacity against your growth, raise limits before you hit them, and stay reachable when something needs a human.
Use cases
What teams build on us
AI agents
Long-running agent loops with heavy tool use, where token volume is large and a provider outage is a product outage.
RAG & search
Retrieval pipelines that need embeddings and generation from one place, with predictable cost per query.
Content & document work
Summarisation, extraction, translation and drafting at volume, where cost per million tokens decides the margin.
Developer tools
Code assistants and review tools that need frontier reasoning models with low latency and generous context.
Get started
Tell us what you're building
Send us the models you use and roughly what you spend today. We will come back with pricing, availability and a key you can test against.
We reply to every enquiry within one business day.