IONOS

What is IONOS?

IONOS is a European cloud and hosting provider whose AI Model Hub serves open-weight LLMs through an OpenAI-compatible API, with inference running in IONOS-operated German data centres. 

For developers, the service provides managed access to model families including Llama, Mistral, Qwen and gpt-oss without requiring teams to deploy or maintain their own inference infrastructure. On Eden AI, IONOS is available for generative text and chat workloads through the text/chat feature.

The core positioning of IONOS AI is European data sovereignty. Inference runs in Germany, the service is GDPR compliant and IONOS is ISO 27001 certified. Customer input data is never used to train models. These characteristics matter to European companies where data location, processing controls and infrastructure governance form part of contractual or internal compliance requirements. Instead of treating residency as an additional routing consideration, teams can select German-hosted inference as a deliberate architectural choice.

IONOS focuses on open-weight models rather than proprietary frontier models. Developers can choose between different model families and parameter tiers while retaining portability at the model level. The underlying models are not exclusive to IONOS, reducing model-level vendor lock-in if infrastructure requirements change. 

Billing is based on input and output tokens per million tokens, making inference costs directly measurable against application usage. This structure also makes it practical to benchmark smaller, lower-cost models against larger alternatives before assigning more expensive inference to workloads that actually require it.

IONOS at a glance

IONOS main AI capabilities

• Text generation and chat completion: Llama 3.3, Llama 3.1, Mistral, Qwen and gpt-oss models support generative text and chat workloads.
• Code generation: Qwen3 Coder Next is the dedicated option for code generation and developer-oriented text workflows.
• Long-form reasoning: Meta Llama 3.1 405B, gpt-oss-120b and Qwen3.5 397B A17B provide the largest parameter tiers for complex reasoning workloads.

When should you choose IONOS?

EU data residency is contractual

IONOS is a strong fit when European data residency is an explicit contractual or architectural requirement. Inference runs in IONOS-operated data centres in Germany, with GDPR compliance and ISO 27001 certification. Customer inputs are not used for model training, giving teams concrete infrastructure and data-handling characteristics to assess against their compliance requirements.

You want open-weight models without running your own GPUs

IONOS provides managed inference when you want access to open-weight Llama, Mistral, Qwen or gpt-oss models without operating GPU infrastructure yourself. The AI Model Hub exposes these models through an OpenAI-compatible API, while the open-weight approach preserves model-level portability if you later decide to change the infrastructure serving the same model family.

Token cost dominates your unit economics

IONOS is worth evaluating when LLM token consumption materially affects your product margins. Eden AI rates start at $0.12 per million input tokens for selected models, while input and output usage are priced separately. Benchmark model quality and token consumption together, particularly where higher output rates on larger models could materially change cost per completed task.

IONOS pros and cons

IONOS models, features and capabilities on Eden AI

IONOS provides open-weight LLM inference for text and chat workloads through Eden AI, with models hosted in IONOS-operated German data centres.

Relevant selected features for IONOS

IONOS is available on Eden AI under the Generative AI / Text category through the text/chat feature ID. Developers can use it for conversational applications and text generation across supported IONOS models. 

Eden AI normalizes the request and response schema across providers, reducing provider-specific integration work. Moving from one IONOS model to another therefore requires changing the model parameter rather than rebuilding the integration. 

The same approach applies when switching between IONOS and another provider available through Eden AI, making model comparison and provider routing easier to implement.

Popular IONOS models on Eden AI

Popular IONOS models you can call through Eden AI include selected Llama, Mistral, gpt-oss and Qwen models across different sizes and price points.

Explore all IONOS models available through Eden AI.

How to choose the right IONOS model

Choosing an IONOS model should account for task complexity, input volume and expected output volume rather than model size alone.

Supported IONOS capabilities

IONOS supports the core generation controls needed for text and conversational applications through Eden AI.

What can you build with IONOS?

IONOS can support text-based applications where European data sovereignty, open-weight model access and predictable token costs are important architectural requirements.

GDPR-compliant internal assistants and document Q&A

IONOS can power internal assistants and document Q&A systems for organizations whose inference must remain in the EU. A retrieval layer first identifies relevant passages from approved internal documents, then sends the question and retrieved context to an IONOS model for response generation in German data centres. 

Llama 3.3 70B Instruct is a practical candidate for this workflow because its 70B parameter tier provides an intermediate option between smaller models and the 405B Llama tier, at $0.75 per million input and output tokens. IONOS is GDPR compliant and does not use customer inputs for model training. 

For sensitive workflows, teams should still evaluate answers against representative documents and require human review where inaccurate responses could affect consequential decisions.

High-volume text classification and enrichment

Low-cost small models make IONOS particularly relevant when classification unit economics matter across millions of requests. Qwen3.5 9B costs $0.12 per million input tokens and $0.17 per million output tokens, making it a candidate for support-ticket routing, tagging and sentiment classification. 

An application sends the source text with a constrained classification instruction, receives the generated label or enrichment, then writes that result into a helpdesk, CRM or analytics pipeline. Keeping outputs short further limits output-token consumption for high-volume workloads. Before production, benchmark the model against a labeled sample from your own data. 

Ambiguous tickets, unusual language and business-specific categories can still produce incorrect classifications, so low-confidence or high-impact cases should have a defined review or fallback path.

Developer tooling and code assistance

IONOS can provide the generation layer for developer tools that review code, suggest refactors and create test scaffolding. Qwen3 Coder Next is the coding-specific option, priced at $0.17 per million input tokens and $0.93 per million output tokens. 

A development tool can collect the relevant code and instructions, submit them through Eden AI's text/chat feature, then return the generated review, refactoring proposal or test code inside the developer workflow. The generated output can subsequently be checked by existing tests and static-analysis tooling before acceptance. 

Code generation should not bypass engineering review: developers should inspect suggested changes, run tests and validate security-sensitive modifications before merging generated code into production.

IONOS use cases by industry

Best IONOS alternatives and comparisons on Eden AI

The best IONOS alternative depends on whether your priority is European sovereignty, direct access to a model developer, infrastructure choice, inference cost, or frontier-model capability.

IONOS vs Mistral AI

IONOS and Mistral AI both fit European AI strategies, but they occupy different positions in the stack. IONOS is a European cloud and hosting provider whose AI Model Hub serves multiple open-weight model families, including Llama, Mistral, Qwen and gpt-oss, from IONOS-operated German data centres. Mistral AI is primarily a model developer, so the comparison is closer to neutral multi-family hosting versus first-party access to one model maker's portfolio.

IONOS is therefore relevant when infrastructure location and the ability to evaluate several open-weight families through one host are priorities. Mistral AI is more relevant when your technical decision is specifically centered on Mistral models and first-party access.

IONOS vs OVHcloud and Scaleway 

Choose IONOS if you prioritize German-hosted inference across multiple open-weight families; choose Mistral AI if your application is specifically built around Mistral's own models.

IONOS, OVHcloud and Scaleway all run inference inside the EU, so the deciding factor between them is rarely sovereignty itself - it is jurisdiction, catalogue composition and modality coverage.

IONOS serves its AI Model Hub from IONOS-operated data centres in Germany. OVHcloud deploys AI Endpoints from its Gravelines site in France, and Scaleway runs Generative APIs from Paris. If your data-residency clause names a specific member state rather than the EU as a whole, that distinction decides the choice on its own.

Catalogue composition differs more sharply. IONOS is deliberately open-weight only: Llama, Mistral, Qwen and gpt-oss. Scaleway mixes open weights with commercial and Chinese-origin models such as Mistral Medium 3.5, GLM-5.2 and DeepSeek V4 Flash. OVHcloud covers the widest range of modalities, adding speech recognition, text-to-speech, image generation and moderation models alongside its LLMs.

IONOS vs OpenAI and Anthropic

IONOS serves a different requirement from OpenAI and Anthropic. Its strongest case is sovereign European inference for open-weight models, with processing in German data centres, GDPR compliance and transparent token pricing. OpenAI and Anthropic are proprietary frontier-model developers whose models should be evaluated when maximum capability on demanding reasoning tasks matters more than using open weights.

The trade-off should not be hidden: open-weight models served through IONOS may trail frontier models on the hardest reasoning workloads. That does not make either approach universally better. Classification, internal assistants and cost-sensitive generation may have different requirements from complex reasoning workloads.

Many teams therefore have reason to use both. Eden AI enables routing between providers rather than forcing a single-provider architecture.

Choose IONOS for sovereign open-weight inference and cost control; evaluate OpenAI or Anthropic when frontier capability is the primary requirement.

Frequently asked questions about IONOS on Eden AI