Every European AI chat & assistant reviewed
Tallinn, Estonia
Founded 2023
Free tier of 15 messages / EUR 1 seven-day trial / EUR 12 per month or EUR 97 per year for full access
EUR 1 for seven days
Best for: European organisations that must be able to prove no American processor touches their prompts, and individuals who want the same guarantee
DentroChat is the European AI chat that has gone furthest in taking American infrastructure out of the stack. Its published subprocessor list names eighteen companies across Germany, France, Austria, the Netherlands, Switzerland, Sweden, Slovenia, Luxembourg and Ireland, and not one of them is American. For a product that has to run inference somewhere, that is harder than it sounds, and it is the reason to look at a small Estonian vendor ahead of a larger competitor.
Hosting, inference, the language models, image generation, transactional mail and payments each sit with a named European company. The list is public, so a procurement officer can check it instead of taking a claim on trust, and that is the product as much as the chat window is. Which providers are on it changes as Dentro develops; that none of them is American does not.
The assistant itself is competent rather than remarkable: Fast, Thinking and Creative modes, image generation, web search, file and PDF analysis, voice input, memory across conversations, automatic chat deletion, and a workspace that turns a conversation into documents, decks and spreadsheets.
Sharing works through links, which suits a small team but is not a full multi-user product with roles and audit trails. Pricing is EUR 12 a month or EUR 97 a year for full access, after a free tier of fifteen messages and a EUR 1 seven-day trial.
The limits follow from the architecture. Dentro trains nothing itself, so what it can do is bounded by the European providers it routes to, and a change on their side is a change to the product. DentroInnovation OU is a small company run by its two founders, with far less scale than Proton or Mistral, and there is no self-hosting and no source to inspect.
What DentroChat does well
- Eighteen subprocessors, every one European
- Public, checkable infrastructure page
- Images, web search, voice, files and memory in one product
- Workspace produces documents, decks and spreadsheets
- Cheap next to the American assistants
Where DentroChat falls short
- Trains no models; quality tracks its providers
- Small company; far less scale than Mistral or Proton
- No self-hosting, no open source, web only
Standout feature. The only assistant here whose entire subprocessor chain is European, published and verifiable.
Geneva, Switzerland
Founded 2014
Free tier with limits / Lumo Plus $9.99 per month billed annually / Lumo for Business via Lumo Professional or Proton Workspace
Free tier
Best for: People and teams already inside the Proton suite who want the assistant under the same roof
Lumo is Proton's answer to ChatGPT, and it brings something most privacy-branded assistants cannot: a stack you can inspect. The apps are open source and so are the models behind them. Proton runs those models on its own servers under Swiss law rather than routing prompts to an American provider, which is the distinction that matters if your reason for looking is where your data ends up.
The privacy claim goes past the usual wording. Conversations use zero-access encryption, so Proton cannot read your history even if asked to, and prompts train nothing. Lumo is reachable over Tor, and guest access lets you use it without an account at all, which is unusual in this category. The trade-off is the same as Proton Mail: lose your password and recovery phrase and the history is gone, because nobody can decrypt it for you.
Underneath sit open-weight models that Proton optimises and hosts, currently Qwen 3.5, GLM 5.3 and the Swiss-built Apertus 1.5 for text with Image-Turbo and FireRed-Image-Edit-1.1 for images. Worth knowing and rarely mentioned: Qwen and GLM, the main text models, were trained by Chinese labs, not European ones. The weights are open and the inference is Swiss, so nothing leaves Proton, but if sovereignty means who trained the model rather than who runs it, Le Chat is the stricter answer.
It is not a frontier model, and on hard reasoning, long context and coding it sits behind ChatGPT, Claude and Gemini. There is no self-hosting. The free tier is tight enough that regular use means Plus at $9.99 a month billed annually. What it does offer, that the others do not, is a Swiss jurisdiction, an inspectable stack and integration with Proton Drive.
What Lumo does well
- Apps and models both open source
- Zero-access encryption: Proton cannot read your history
- Swiss jurisdiction, outside the US CLOUD Act
- Tor and guest access, no account needed
- Reads Proton Drive files
Where Lumo falls short
- Behind the frontier models on hard reasoning
- Main text models trained by Chinese labs, not European
- No self-hosting
Standout feature. The only assistant here that is open source from app to model weights.
Paris, France
Founded 2023
Free tier / Vibe (formerly Le Chat) Pro $14.99 per month / Team $24.99 per user per month / pay-per-use API
Free tier
Best for: Anyone wanting a European ChatGPT alternative, an API, or open-weight models to self-host
Mistral AI is the reason "is there a European alternative to OpenAI" now has a real answer.
Mistral Large 3 is a sparse mixture-of-experts model with 41 billion active parameters out of 675 billion total, reasoning across a 256,000-token context window, multimodal and multilingual from the ground up — and available as an open-weight download, which no US frontier lab matches at that tier.
Around it sit Mistral Medium 3.5, the new 128B open-weight default, Mistral Small 4 for cost-sensitive work, Magistral for reasoning, the Ministral 3 family at 3B, 8B and 14B for edge deployment, and Voxtral for audio.
Vibe, the assistant formerly called Le Chat, is the consumer face: web, iOS and Android, with deep research for complex queries, native multilingual reasoning across European languages, image editing and a Memories feature retaining context between conversations.
Free, Pro and Team tiers, Pro at $14.99 per month for more messages, web searches and deep research plus all-day coding, Team at $24.99 per user. Mistral Studio covers the developer side, with API usage from $0.15 per million input tokens for Mistral Small 4.
The European argument is concrete rather than sentimental: Vibe processes data within EU jurisdiction under French law rather than under the CLOUD Act, which matters most for the assistant staff paste anything into. The gaps are maturity ones — Vibe's third-party ecosystem is younger than ChatGPT's despite 100+ connectors, enterprise support is thinner than Microsoft's, and the release cadence is fast enough that keeping current takes attention.
What Mistral AI does well
- Frontier-class models with open weights you can download and run
- Vibe (formerly Le Chat) processes in EU jurisdiction under French law
- 256,000-token context, multimodal and multilingual
- Models from 3B edge to 675B total parameters
- API from $0.15 per million input tokens for Small 4
Where Mistral AI falls short
- Vibe's third-party ecosystem younger than ChatGPT's
- Enterprise support less deep than Microsoft or Google
- Rapid release cadence requires ongoing attention
- Not every model in the lineup is open-weight
Standout feature. The only company here shipping frontier-class models you can download and run on your own hardware.
Heidelberg, Germany
Founded 2019
Enterprise pricing on request
Contact sales
Best for: Government agencies and regulated enterprises with hard data-boundary requirements
Aleph Alpha pivoted from selling access to its Luminous models to shipping PhariaAI, an enterprise operating system for generative AI, and the shift is what makes it relevant. PhariaAI handles the full lifecycle — model hosting, orchestration, retrieval-augmented generation, explainability and compliance — running natively on STACKIT so that compute, models and application layer are all operated by European companies under European law.
Hybrid execution is its most distinctive capability: workloads run across on-premise and cloud at once, with sensitive data processed exclusively on local servers while less sensitive tasks overflow to cloud capacity during peak demand, allocated dynamically by classification rules the organisation defines. That turns an all-or-nothing decision into a policy question — a healthcare institution can keep patient records local while using cloud capacity for general document summarisation.
Explainability is built in rather than added: PhariaAI traces a response back to the source documents that produced it through attention visualisation and source attribution, so decisions informed by AI remain justifiable and reviewable.
That is precisely what the EU AI Act asks of high-risk systems, and it explains the concentration of government, legal and financial services deployments. Aleph Alpha GmbH operates from Heidelberg with enterprise-only pricing quoted on request. In September 2026 it signed a binding agreement to combine with Canada's Cohere; the merged company will operate as Cohere, with headquarters in Berlin and Toronto, subject to regulatory approval.
What Aleph Alpha does well
- Full sovereign stack — compute, models and application under EU law
- Hybrid execution splits workloads by data classification
- Source attribution and attention visualisation for auditability
- Designed against EU AI Act requirements from the start
- On-premise deployment for classified workloads
Where Aleph Alpha falls short
- Enterprise pricing only, nothing published
- Proprietary — no open weights
- Models trail frontier US labs on raw benchmark capability
- Sales-led engagement with no self-service entry
Standout feature. Hybrid execution: sensitive data stays on-premise while everything else uses cloud capacity, decided by your own classification rules.
Berlin, Germany
Founded 2016
Free Developer Edition (one bot, up to 1,000 external conversations per month) / Enterprise on request
Free Developer Edition licence
Best for: Developers building auditable, self-hosted conversational assistants
Rasa is a framework for building conversational assistants that follow your business process rather than improvising, and its CALM architecture is what makes that possible with modern language models. An LLM handles dialogue understanding — the ambiguity and variability of how people actually phrase things — while business logic stays in developer-defined conversation patterns that describe how a process should flow. The assistant is fluent, and it cannot leave an approved workflow.
That separation of understanding from execution replaces the older approach of defining hundreds of intents with training examples each, and it has a second benefit that matters more in regulated sectors: the system is debuggable and auditable, so you can demonstrate what the assistant will and will not do under GDPR, HIPAA or financial services rules. Full platform access, premium support and large-scale deployment sit in the Enterprise tier.
The free Developer Edition licence covers one bot with up to 1,000 external or 100 internal conversations a month, locally or in production, so a self-hosted pilot costs only infrastructure: a modest VM for prototyping, a multi-node Kubernetes cluster in production.
The older Rasa Open Source framework remains Apache 2.0 but is in maintenance mode. Rasa Technologies is based in Berlin with a US entity in San Francisco; EU customers contract with the Berlin GmbH. Enterprise pricing is custom and undisclosed, which is friction for smaller teams, though the free licence gives room to validate a project first.
What Rasa does well
- Free Developer Edition for one bot, fully self-hostable
- CALM keeps business logic in developer rules, not the model
- Auditable and debuggable for regulated deployments
- Data never has to leave your infrastructure
- Broad channel and integration support
Where Rasa falls short
- A framework, not a product — requires developers
- Enterprise pricing undisclosed
- Longer time to a working bot than a hosted builder
- Self-hosting means you operate the infrastructure
Standout feature. CALM gives you an LLM's fluency with hand-written business rules underneath — fluent, and incapable of going off-process.
Paris, France
Free
Free, no payment required
Best for: Developers, researchers and anyone wanting free access to many open models
HuggingChat is free with no payment required, and its distinguishing feature is choice: over 100 open-source models you can select manually, or Omni routing that analyses each query and sends it to whichever model is likeliest to answer well — a coding-specialised model for programming, a reasoning model for analysis, a general model for creative work. The lineup tracks new open releases, currently including Qwen, GLM, DeepSeek, Llama, Cohere Command and OpenAI gpt-oss models.
Switching models mid-conversation or comparing how two answer the same question is something no proprietary chatbot allows, and for anyone evaluating open models before deploying one it is the fastest way to form a view. Around that sit web search, custom assistants, file upload and code execution — enough to be a working daily assistant rather than only a demo.
Worth stating plainly: the service is run by Hugging Face, Inc., whose privacy policy says the company and its servers are in the United States, with Hugging Face SAS in Paris as its EU establishment, so it is not the choice for a strict data-residency requirement. What it is, unambiguously, is the best free route into open models, backed by the company that hosts most of them.
What HuggingChat does well
- Completely free with no payment required
- 100+ open-source models with manual or Omni routing
- Switch models mid-conversation or compare answers
- Web search, custom assistants, file upload, code execution
- Backed by the company hosting most open models
Where HuggingChat falls short
- Run by a US company on US servers
- Open models trail frontier proprietary ones on hard tasks
- Interface less polished than commercial assistants
- No enterprise support or SLA
Standout feature. Over 100 open models in one interface, switchable mid-conversation — the fastest way to find out which one you actually want.
Paris, France
Free (500 credits) / Pro €30 per seat per month or €24 billed yearly / Max €150 per seat per month or €120 billed yearly / Enterprise on request
Free plan with 500 credits
Best for: Teams building custom AI agents over their own company data
Dust turns company knowledge into agents that can act on it, and the depth of its data connections is what distinguishes it from a chatbot with a document upload. It connects to Notion, Google Drive, Confluence, GitHub, Salesforce, HubSpot, BigQuery, Snowflake, Gmail, Google Calendar, Intercom and Zendesk, with admins controlling exactly what is ingested down to individual Slack channels, Drive folders, Notion pages or Confluence spaces — granularity that matters when departments have different access rules.
Agents write as well as read: updating Notion pages and databases, reading and creating HubSpot records, querying Salesforce objects. That makes them participants in workflows rather than a search box.
Organisations build agents per role — support drawing on knowledge base articles and past tickets, sales pulling CRM data and meeting transcripts, engineering working from repositories and incident reports — and Dust Apps extends them with data transformations, API calls and multi-step automation, with agent chaining for processes that span several.
Dust is run by Permutation Labs in Paris, with its platform code published on GitHub under MIT. Business plans cover teams of up to 100: a free tier with 500 credits, Pro at €30 per seat per month (€24 billed yearly) with 8,000 credits per seat, and Max at €150 (€120 yearly) with 40,000, all with 20+ models, custom agents, SSO and US or EU data residency. Enterprise adds unlimited connectors, SCIM, audit logs, single-tenant deployment and an SLA.
What Dust does well
- Deep connections to Notion, Drive, Salesforce, HubSpot and more
- Admin control down to individual channels and folders
- Agents write back to connected systems, not just read
- Agent chaining and Dust Apps for custom workflows
- French company with EU data residency on every plan
Where Dust falls short
- €24 to €30 per seat per month adds up across a company
- Credit-based usage makes heavy agent work harder to budget
- Single-tenant deployment only on the Enterprise tier
- Underlying models are third-party, not European
Standout feature. Agents that update Notion, HubSpot and Salesforce rather than only reading them — the step from knowledge search to actual workflow.
London, United Kingdom
Per-minute usage pricing, quoted on request
Contact sales
Best for: Enterprises automating high-volume inbound customer calls
PolyAI builds voice assistants for inbound customer calls, and the reason it works where adapted chatbots do not is that the stack was built voice-first. Spoken conversation brings background noise, accents, interruptions and hesitation, and PolyAI's proprietary speech recognition handles it while swapping domain-specific vocabularies mid-conversation — recognising UK postcodes one moment and US social security numbers the next, something generic speech-to-text struggles with.
Its Raven LLM combines with retrieval-based AI and patented dialogue policy technology to deliver sub-second responses that stay inside enterprise business rules and guardrails, which is what allows calls like technical troubleshooting, reservation changes or insurance claims to complete end to end rather than deflecting to FAQs. The voice blends human recordings with neural synthesis so it carries brand identity rather than sounding generic.
Deployment has traditionally been a managed engagement, with PolyAI analysing call data to identify the highest-volume automatable call types before designing and building the assistant; the platform is now also open to enterprise builders through Poly Agent Builder and a developer ADK.
Every call is answered instantly, eliminating hold time, and escalation to a human carries the full context — its Microsoft Dynamics 365 Contact Centre integration pushes gathered data into the agent dashboard so nobody asks the customer to repeat themselves.
PolyAI Ltd is a London company under UK adequacy, with offices in New York, San Francisco and Toronto and a US affiliate acting as joint controller for service data; use is priced per minute, quoted on request.
What PolyAI does well
- Built voice-first rather than adapted from a text chatbot
- Domain vocabulary swapping mid-conversation
- Sub-second responses within enterprise guardrails
- Calls answered instantly — hold time eliminated
- Full context passed to humans on escalation
Where PolyAI falls short
- Enterprise-focused; self-build tools are new
- Per-minute pricing, no rates published
- Implementation takes design work before launch
- UK company, so adequacy rather than EU establishment
Standout feature. Speech recognition that swaps vocabulary mid-call — postcodes now, account numbers next — which is where generic voice bots fall apart.
Berlin, Germany
Free open-source tier / from about €99 per month
Free tier
Best for: Teams building custom NLP models on sensitive data
Kern AI works one layer below the assistants in this category, on the training data that makes a custom NLP model work.
Refinery, its open-source flagship, is a data-centric IDE for NLP covering text classification, span extraction and generation tasks, with the notable feature being weak supervision: instead of labelling every data point by hand, you write labelling heuristics in plain Python — regular expressions, keyword lists, domain rules, calls to pre-trained models — and the engine aggregates those noisy labels probabilistically into high-quality training data. Kern AI puts the speed-up at up to 100 times manual annotation.
Refinery adds role-based access control for collaborative annotation, neural search powered by Qdrant for finding similar examples, and data quality monitoring, building on Hugging Face and spaCy so transfer learning is available without assembling infrastructure. Bricks supplies modular enrichment, with Gates and Workflow carrying data through to deployment.
The Confidential AI platform is what makes it usable in regulated sectors: open models like LLaMA and DeepSeek run inside secure enclaves where data stays encrypted and isolated during processing, and LLM Knowledge Agents connect those models to internal data without exposure. Insurers including Markel Insurance SE, HDI Global Specialty and Nürnberger Versicherung are customers. Kern AI is Berlin-based, with a free open-source tier and paid plans from about €99 per month.
What Kern AI does well
- Weak supervision labels data up to 100× faster than by hand
- Confidential computing keeps data encrypted during processing
- Open-source Refinery and Bricks with a real free tier
- Built on Hugging Face and spaCy, no infrastructure to assemble
- Proven with regulated insurance customers
Where Kern AI falls short
- Developer and data-science tool, not an end-user product
- Solves training data, not conversation — different problem
- Paid tiers from about €99/month on top of infrastructure
- Smaller company than the labelling platforms it replaces
Standout feature. Write labelling rules in Python instead of annotating by hand — the claim is a 100× speed-up, and it is the whole product.
Geneva, Switzerland
Founded 1996
Free with my kSuite for individuals (life-long); kSuite Pro from CHF 1.76 per user/month excl. VAT (Standard), with fuller Euria features on Business (CHF 7.34) and Enterprise (CHF 13.83)
Free for life with my kSuite (individuals)
Best for: Individuals and small teams wanting Swiss-hosted AI
Euria is Infomaniak's answer to the same question Lumo asks — can a Swiss company give you ChatGPT without American servers — but it takes a different path to get there.
Besides a standalone web and mobile app, Euria is built into kDrive, the Infomaniak Mail Service and kChat, so the assistant already sees your files, your inbox and your team conversations rather than starting from an empty chat window.
Individuals get it free for life inside my kSuite; organisations get it in kSuite Pro from CHF 1.76 per user per month on the Standard plan, with kDrive file search on Business at CHF 7.34 and the fullest AI features, and up to 6 TB of storage per user, on Enterprise at CHF 13.83.
Everything runs on infrastructure Infomaniak owns and operates itself in Geneva and Zurich, on open-source models rather than a licensed frontier model, with Infomaniak stating that queries are neither stored nor used for training.
The company frames this as sovereignty in the fullest sense: no hyperscaler underneath, no foreign cloud contract, and — a detail few competitors mention — waste heat from the data centres is reused to heat homes nearby. Feature-wise Euria covers web-grounded answers, writing and translation, document summarisation and audio transcription, aimed squarely at daily office work rather than open-ended research.
The trade-off is scope and polish. Euria is a productivity feature riding on top of kSuite rather than a frontier assistant competing on raw model quality, and Infomaniak does not publish which open-source models it runs or benchmark them against GPT or Mistral.
There is no dedicated pricing for Euria alone: it has its own web and mobile app, but the plans that unlock it are kSuite or kSuite Pro. For a company already paying for Infomaniak email or storage, it is close to free; for anyone starting from zero, it is a suite purchase, not a chatbot signup.
What Euria does well
- Hosted exclusively in Switzerland, on Infomaniak-owned infrastructure
- Free for life for individuals inside my kSuite
- kSuite Pro includes it from CHF 1.76 per user per month
- Built into kDrive, Mail and kChat rather than a separate app
- Renewable energy, with waste heat reused to heat homes
Where Euria falls short
- No standalone Euria subscription; only bundled inside kSuite
- Underlying open-source models are not named or benchmarked
- Positioned for office productivity, not open-ended research or coding
- Smaller AI-specific track record than Proton or Mistral
Standout feature. The only assistant in this category whose company also tells you it reuses the AI's waste heat to warm homes.
Berlin, Germany
Founded 2018
Haystack framework free and open source (Apache 2.0) / Studio free tier (1 workspace, 100 pipeline hours) / Enterprise: custom pricing on request
Free Studio tier, no credit card required
Best for: Developers building governed RAG applications and AI agents
deepset is behind Haystack, an open-source framework with more than 26,000 GitHub stars for building retrieval-augmented generation pipelines, AI agents and semantic search — the same territory as LangChain and LlamaIndex, but maintained by a Berlin company rather than a US one.
Where Rasa in this category is a framework for managing a conversation's business logic, Haystack is a framework for grounding an assistant in your own documents and data sources, with explicit control over retrieval, routing, memory and generation rather than a black-box pipeline.
On top of the open-source core sits the Haystack Enterprise Platform (for a time marketed as the deepset AI Platform), a visual pipeline builder with role-based access control, audit logs, guardrails and a playground for testing before production, deployable in the cloud or self-hosted for teams that need to keep infrastructure in-house. deepset markets it explicitly as "the platform for Sovereign AI," and the company holds SOC 2 Type II certification.
Contracts for customers outside the US run through deepset GmbH in Berlin under German law, with Berlin courts, rather than through its New York office.
The free Studio tier covers one workspace, one user, 100 pipeline hours and 50 files — enough to build a prototype, not to run a team on.
Beyond that, deepset publishes no self-serve pricing at all; every Enterprise plan is quoted individually, which is friction for a small team comparing options against Dust's published €30 per seat. And unlike Rasa's dialogue-management focus, Haystack assumes you already know what "retrieval" and "pipeline" mean — this is a tool for a development team, not a business user configuring a bot in an afternoon.
What deepset does well
- Haystack framework is free, open source and Apache 2.0
- More than 26,000 GitHub stars and active development
- Cloud or self-hosted deployment for data sovereignty
- RBAC, audit logs and guardrails built into the Enterprise Platform
- SOC 2 Type II certified
Where deepset falls short
- No published Enterprise pricing; every plan is quoted
- Free Studio tier is a prototype limit, not production-ready
- Requires developers; not a no-code bot builder like Rasa or Dust
- Also operates from New York, so not EU-only
Standout feature. An open-source RAG framework with 26,000+ GitHub stars, built by a Berlin company rather than a Silicon Valley one.
