Enterprises evaluating “private AI for enterprises” are often trying to solve two problems at once: (1) enable broad internal adoption of generative AI without creating data leakage, compliance, or vendor-risk exposure; and (2) avoid an 12–18 month infrastructure build that stalls business value.

This comparison focuses on two European-built platforms aimed at enterprise adoption:

• Zylon is an enterprise AI platform designed to run inside the customer’s infrastructure—including air-gapped environments—so data can remain on-site. It emphasizes a self-contained stack (local models + vector search + ingestion + API + workspace), fixed-cost usage (no per-token billing), and fast time-to-initial deployment (hours to a week depending on environment and rollout scope).

• Langdock is an enterprise AI platform designed to scale organization-wide AI adoption through chat, agents, workflows, integrations, and a unified API. It emphasizes security certifications (ISO 27001, SOC 2 Type II), EU hosting on Microsoft Azure for its standard SaaS, and flexible deployment options that include bringing your own cloud and on-premise deployment (at enterprise scale).

A practical way to position them for regulated buyers:

• If you need true on-prem / air-gapped operation as a core requirement (not a special case)—for example, strict data sovereignty AI requirements, critical infrastructure constraints, or “no cloud exposure” policies, Zylon’s deployment model aligns more directly with those demands.

• If your primary goal is fast enterprise-wide adoption with strong governance in an EU-hosted SaaS model (and you can accept cloud-hosting or large-scale dedicated deployments), Langdock is designed for that path and has mature admin/security messaging plus published per-seat pricing for the business tier.

The remainder of the article goes deep on deployment, data privacy & sovereignty, compliance & governance, cost, security posture, performance/customizability, integrations, and regulated-industry fit—ending with a decision recommendation for enterprise buyers.

Platform Overviews

What Is Zylon?

Zylon is an enterprise AI platform for regulated industries built to deploy inside enterprise infrastructure without external cloud dependencies.  It is structured as a full stack including:

• AI Core: local LLMs, vector databases, and GPU orchestration—deployable in private cloud VPC, on-prem servers, and fully air-gapped environments.

• Workspace: an internal interface for AI assistance, document creation, knowledge base usage, collaboration, and data sources.

• API Gateway: OpenAI-compatible endpoints with built-in authentication/logging/rate limiting/observability and integration hooks into common tooling such as n8n and LangChain.

Zylon’s operating assumptions are “private AI for regulated industries” first: it highlights 100% on-premise, air-gapped deployment, and “data never touches external servers.”  It also emphasizes fixed cost / unlimited usage (i.e., not charging per token) and “production-ready in under one week” for typical deployments.

From an implementation standpoint, Zylon’s documentation describes installation via a CLI-driven process (including support for semi-airgapped and fully offline installations).  Hardware-wise, the platform requires an NVIDIA CUDA-capable GPU; reference configurations show single-GPU setups supporting hundreds of users in some deployments (depending on models and workload).

Zylon is legally registered in Spain, and its company page describes it as a European company focused on regulated environments.

What Is Langdock?

Langdock is an enterprise AI adoption platform that bundles multiple product surfaces into one environment: chat, custom agents, workflows, integrations, and an API layer meant to unify access to multiple models.

The platform positions itself as:

• Model agnostic (to avoid vendor lock-in),

• Security-first and hosted in Europe for its standard SaaS footprint,

• Enterprise-ready with governance and admin controls,

• Deployable anywhere with options that range from multi-tenant EU SaaS to single-tenant SaaS, bring-your-own-cloud, and on-premise via Helm on Kubernetes (enterprise-scale thresholds are published on its security page).

Langdock also publishes clearer pricing for smaller-to-mid enterprise tiers: its “Business” plan is priced per user/month and includes SSO/SCIM/SAML, while enterprise pricing and dedicated deployments are custom.

From a security posture perspective, Langdock states it is ISO 27001 certified and SOC 2 Type II audited, uses TLS 1.2+ in transit and AES-256 at rest, performs independent penetration testing, and does not train models on customer data.

Deployment, Data, and Compliance

Deployment Model Comparison

For regulated enterprises, “deployment model” is not only a hosting preference—it determines the real boundary of trust: where prompts, retrieved context, embeddings/vector stores, logs, and admin telemetry live.

Below is a deployment-focused “self-hosted AI platform” comparison that highlights the differences that typically matter to CTOs and CISOs.

Data Privacy and Sovereignty Analysis

“Data sovereignty AI” requirements are easiest to satisfy when the architecture keeps sensitive data inside the legal and technical boundary the enterprise already governs (its data center, its private cloud tenant, its classified network, etc.). In the EU context, GDPR principles include integrity/confidentiality and accountability, which materially influence system design choices (logging, access controls, retention, vendor/subprocessor management, and cross-border transfers).

Zylon’s data boundary

Zylon’s core positioning is that data stays on the customer’s servers, including in air-gapped deployments.  Its documentation describes full offline installation and operation for environments with “NO internet connection.”  For regulated buyers, this reduces several common data-sovereignty risks:

• Cross-border transfer risk is minimized when the platform does not require sending prompts/document context outside the customer’s infrastructure.

• Subprocessor surface area can be reduced when critical functions (models, retrieval, and inference) run locally.

A nuance worth capturing for compliance teams: Zylon’s operator documentation describes optional observability tools (Sentry crash reporting and Grafana usage metrics) and explicitly documents how to disable them.  In strict data environments, disabling external telemetry—and validating that no “phone-home” behavior exists in the deployed stack—tends to be part of readiness review.

Langdock’s data boundary

Langdock’s standard SaaS deployment is hosted on Microsoft Azure servers inside the EU and highlights EU hosting, no training on customer data, and encryption at rest/in transit.  It also offers a mix of deployment models (single tenant SaaS, bring your own cloud, on-premise), which means data sovereignty can be improved by moving from shared SaaS to a dedicated environment or into customer infrastructure.

However, enterprise buyers should separate platform hosting from model hosting:

• Langdock states “the application and most models (configurable) are hosted entirely in the EU,” and also supports bringing your own model API keys.

• When using BYOK or adding models, the Langdock docs describe connecting the workspace to model APIs (base URL, API key, region selection, etc.), which can include third-party providers or customer-hosted endpoints.

In practice, Langdock can fit “private AI for enterprises” in multiple ways:

• EU SaaS + strong governance controls, when customer policy allows EU cloud hosting.

• Dedicated deployments (single tenant, own cloud, or on-prem) for tighter requirements.

• BYOK / customer-hosted models to tighten where model inference occurs.

Compliance and Governance

This section addresses GDPR, HIPAA, SOC 2, and the EU AI Act from an enterprise buyer perspective: which platform capabilities map to real controls, auditability, and governance.

Baseline: what these frameworks typically demand

• GDPR establishes principles such as lawfulness/fairness/transparency, purpose limitation, data minimization, integrity/confidentiality, and accountability—meaning governance is not optional in EU enterprise deployments.

• HIPAA Security Rule requires administrative, physical, and technical safeguards for protecting electronic protected health information (ePHI).

• SOC 2 is an attestation report on controls relevant to security, availability, processing integrity, confidentiality, and/or privacy. It is governed by the AICPA.

• EU AI Act (Regulation (EU) 2024/1689) is the EU’s harmonized AI regulation; it is in force and introduces risk-based obligations (especially for high-risk systems) that typically increase requirements for documentation, control, and oversight in enterprise contexts.

Governance feature comparison

A key takeaway for compliance teams: both platforms can support governance, but they do so differently.

• Zylon’s governance posture is strongly tied to where the system runs (on-prem/air-gapped) and configurable features like audit logging and feature toggles (e.g., disabling web search, disabling external observability).

• Langdock’s governance posture is strongly tied to security certifications and enterprise controls (ISO 27001, SOC 2 Type II, SAML/SCIM), plus documented audit log APIs and EU-hosted infrastructure for SaaS.

Economics and Security Posture

Cost Model Comparison

Cost predictability is often the deciding factor for “private AI for banking” and “AI for financial services,” because seat counts can scale faster than usage, and usage can spike unpredictably during rollout.

Zylon and Langdock approach cost in structurally different ways:

• Zylon emphasizes fixed cost and “no per-token pricing” plus “unlimited AI interactions,” positioning the primary variable costs in the customer’s infrastructure (GPU/servers, storage, and ops).

• Langdock publishes per-seat pricing for its Business plan and adds package-based pricing for workflows plus a separate usage-priced API product with a disclosed surcharge model.

Cost comparison table

Security Posture and Threat Model Differences

Security posture for enterprise AI assistants tends to break into two layers:

1. Platform security controls (identity, RBAC, audit logs, encryption, monitoring, vulnerability management).

2. Threat model boundary (what systems must be trusted, what third parties can access data, and what “blast radius” looks like).

Langdock: security posture strengths

Langdock explicitly describes:

• Encryption in transit (TLS 1.2+) and at rest (AES-256),

• ISO 27001 certification and SOC 2 Type II audits,

• Independent penetration testing and subprocessor reviews,

• Audit log export via API with explicit retention, useful for feeding SIEM pipelines.

• SAML SSO and SCIM support for identity and lifecycle management at scale.

• Static IP documentation (useful for firewall allowlisting and network monitoring of outbound integration traffic).

Threat-model implication: In standard SaaS mode, a regulated enterprise is trusting a cloud-hosted control plane, plus any configured model providers (unless models are hosted in a controlled environment). Langdock partially mitigates via EU hosting and deployment flexibility (single tenant / own cloud / on-prem).

Zylon: threat model strengths

Zylon pushes the trust boundary inward:

• Full air-gapped installation is explicitly supported and documented for “zero internet access.”

• Industry pages emphasize “no cloud exposure” and audit trails, including “every query logged,” which matters for financial services oversight and regulated auditability.

• The platform uses local infrastructure, and documentation frames logs as shareable without vendor access to data (a practical detail for sensitive environments).

• Feature toggles allow disabling capabilities that expand external exposure (e.g., web search is disabled by default and requires external API keys).

• External observability can be explicitly disabled, and the docs state how to do it.

Threat-model implication: For regulated enterprises, Zylon aligns to a model where the enterprise retains operational responsibility for patching, infrastructure hardening, and access governance. That is not “free”—but it is often the trade-off chosen for maximum data sovereignty AI requirements.

Performance, Customizability, and Integration

Performance and Customizability

Enterprise buyers should evaluate performance and customizability across two different axes:

• Model capability access (can you run/route to the models you need?)

• Operational performance (throughput, latency, concurrency on your infrastructure and under your security constraints)

Zylon performance profile

Zylon has one strict hardware requirement in its operator guidance: an NVIDIA CUDA-capable GPU; it provides a set of recommended GPU configurations (bare metal and cloud) and notes that some features and models depend on GPU choice.  The docs also describe real-world scaling: examples include customers running on RTX 5090-class hardware supporting 200+ users (workload-dependent).

Zylon’s “customizability” is closely tied to:

• Running local models,

• Running in restricted or air-gapped networks,

• Providing OpenAI-compatible endpoints for custom apps and integrations through its API gateway.

Langdock performance profile

Langdock’s model-agnostic strategy is a major advantage when:

• Different teams need different models,

• You want to route between providers for cost/performance,

• You want BYOK control over model endpoints and regions.

Its docs explicitly support adding your own models, with configuration fields such as base URL, API key, model ID, context size, and region—meaning you can integrate provider-hosted models or potentially customer-hosted endpoints.

For regulated buyers, a performance nuance is cost-control and fairness limits: Langdock documents model usage policies (including “fair usage” categories).  This may not matter for many workflows, but it is relevant for enterprise operational predictability at scale.

Integration and Extensibility

Integration is where most enterprise AI programs either succeed (because the assistant reaches the knowledge and systems employees actually use) or degrade into “generic chat.”

Zylon integration surface

Zylon’s documentation and product pages emphasize connectivity to enterprise tools and data sources without moving data to the cloud. Examples called out include SharePoint, Confluence, PostgreSQL, file systems, and banking cores (Symitar, Corelation, Fiserv).

Extensibility is driven through:

• An API gateway with OpenAI-compatible endpoints, plus built-in authentication/logging/rate limiting.

• Included deployment of n8n for workflows/automation on the same server as the platform, designed to work in air-gapped setups (with external connections constrained in air-gapped modes).

Langdock integration surface

Langdock’s product design includes:

• Integrations, agents, and workflows as first-class primitives,

• A documented approach to building custom integrations (integrations vs actions vs triggers),

• Governance in integrations (mirrored access permissions, optional analytics controls),

• Audit log export capabilities to feed SIEM pipelines,

• Network/security operational tooling such as static IP allowlisting guidance.

From an enterprise “integration & extensibility” viewpoint: Langdock is designed to integrate broadly with your toolstack (and to let you build new integrations explicitly), while Zylon is designed to bring integrations into a controlled on-prem environment (and support automation through embedded workflow tooling).

Enterprise Fit in Regulated Industries

Enterprise Use Cases in Regulated Industries

This is where “enterprise AI assistant comparison” becomes concrete: what workflows can you support without violating policy, and what operational effort is required to keep the assistant safe and auditable?

Financial services and private AI for banking

Zylon’s financial services materials emphasize on-prem use for banks/credit unions and explicitly call out regulatory context (SOC 2, GLBA, FINRA, data residency) plus RBAC and detailed audit logs for each query/response/data access.  This aligns with typical bank risk models where auditability and data locality are non-negotiable.

Langdock can support financial services workflows through governed internal assistants, workflow automations, and audit log export, with EU hosting for SaaS and BYOK for model control.  In banking environments where cloud is permitted (or where dedicated deployment is approved), this can accelerate adoption.

Healthcare (GDPR + HIPAA considerations)

HIPAA security requirements emphasize administrative/physical/technical safeguards around ePHI.  Zylon’s regulated-industry messaging includes healthcare networks and patient information protection under HIPAA, and Zylon’s deployment modes (on-prem/air-gapped) align strongly with “keep PHI local” security patterns.

At the same time, Zylon’s public terms include a generic “not tailored to comply with HIPAA” clause for its “Services,” which healthcare compliance teams should resolve through product-specific contractual commitments and deployment scoping.

Langdock’s public security/compliance emphasis is ISO/SOC/GDPR; healthcare buyers should validate HIPAA-specific contracting requirements if PHI is in scope and clarify model-provider handling if routing to external LLM providers.

Public sector and defense / critical infrastructure

Zylon’s public sector and defense/critical infra pages explicitly emphasize air-gap capability, “no cloud exposure,” and “complete audit trails,” and even reference classified-network patterns (SCIF) in deployment models.  For governments and critical infrastructure operators, this directly matches the constraints that prevent ordinary SaaS adoption.

Langdock’s public deployment options include on-premise and own-cloud at very large scale thresholds, but its standard SaaS positioning is cloud-hosted (EU).  For many public sector buyers, that’s either acceptable (with the right contracts) or disqualifying—depending on classification level and national policies.

Manufacturing and engineering

Zylon’s materials emphasize IP protection, project-level segregation, and keeping proprietary knowledge internal—especially relevant to engineering firms and manufacturers.

Langdock is also used for internal knowledge access and workflow automation, with strong governance tooling and the advantage of model flexibility for varied tasks (technical writing, documentation, internal Q&A, automation).

Industry suitability matrix

The table below summarizes fit for typical regulated-industry requirements. “Best” here means “most structurally aligned assuming standard enterprise procurement constraints,” not “feature-complete for every buyer.”

Strengths and Limitations of Each

Zylon strengths Zylon’s strengths are structural, not cosmetic:

It is built to run on customer infrastructure, including fully offline environments.  It emphasizes fixed-cost usage (no per-token pricing) and unlimited interactions, and its operator docs describe rapid, CLI-driven installation.  It also provides on-prem governance levers such as optional audit logging and feature toggles (including disabling web search).

Zylon limitations / trade-offs The primary trade-off is operational: running self-hosted AI platforms requires capacity planning, security hardening, patching, backup/DR procedures, and model lifecycle management. Zylon provides backup/restore guidance and air-gap update mechanics, but the responsibility still sits with the customer’s IT/security function.

Additionally, compliance teams should align product architecture, governance features, and contractual language—especially where public legal terms contain generic statements about sector-specific compliance.

Langdock strengths Langdock’s strengths center on enabling organization-wide adoption with governance and security controls:

• Published security posture (encryption, ISO 27001, SOC 2 Type II, pen testing, subprocessor governance).

• Multiple deployment options (EU SaaS, dedicated deployments, own cloud, on-prem via Helm).

• Mature platform primitives for adoption (chat, agents, workflows, integrations) and documented audit log export for enterprise monitoring.

• Model flexibility and BYOK/“add your own models” pathways to tune cost/performance and regional routing.

Langdock limitations / trade-offs In SaaS mode, some regulated buyers may still see the cloud control plane as a risk boundary—even with EU hosting—depending on sector and policy.  Moving to on-prem or own cloud may require large enterprise-scale commitments (as the company’s public security page signals).

And because Langdock supports BYOK and multiple model providers, enterprises must govern outbound data flows to AI model endpoints (and ensure vendor risk is managed across the chosen providers).

When Langdock Makes Sense

Langdock is a strong choice in scenarios like:

If your enterprise wants to standardize AI usage quickly across many departments with:

• EU-hosted SaaS acceptable under policy,

• A need for SAML/SCIM, centrally administered workflows, and governance APIs (audit logs),

• A preference for model-agnostic routing or BYOK control to balance cost and capability.

It is also compelling when you want “one interface” that covers chat + agents + workflows + integrations without building those layers internally.

When Zylon Is the Right Choice

Zylon is the stronger fit when “data sovereignty AI” is not a tagline but a hard requirement, for example:

If you must deploy in on-prem or air-gapped environments where there is zero tolerance for external processing.

If your organization wants a private AI platform with fixed-cost usage and no per-token billing exposure during adoption spikes.

If your security team requires auditability and controlled feature exposure, including the ability to disable web search and disable external telemetry.

If your integration strategy depends on connecting to on-prem systems (including file shares, collaboration tools, and regulated systems) while keeping the retrieval and inference boundary internal.

Final Recommendation, FAQ, and Implementation Guidance

Final Recommendation for Enterprise Decision-Makers

For enterprise buyers in regulated industries—finance/banking, healthcare, public sector, manufacturing, engineering—the deciding factor is usually not whether the product has an “AI assistant.” It is whether the platform’s default trust boundary matches regulatory reality.

Zylon’s design center is on-premise and air-gapped deployment with local models and internal control of data flows, backed by published operator documentation for online and offline installs, governance feature toggles, and local operational ownership.

Langdock is a strong enterprise AI adoption platform—especially for EU-hosted SaaS and security-audited governance programs—but it is structurally best when your organization can accept cloud or large-scale dedicated deployments and is comfortable governing model-provider routing across a flexible, multi-model ecosystem.

For regulated enterprises where maximum data sovereignty and on-prem operation are the primary requirements, Zylon is the recommended choice.

FAQ for Enterprise Buyers

What is the best Langdock alternative for air-gapped environments?

If “air-gapped” means zero internet connection and fully offline operations, you need a platform that documents offline installation and offline operation. Zylon’s operator documentation includes a full-airgap installation process built for “zero internet access,” including an offline machine running the platform.  Langdock offers on-premise deployment via Kubernetes/Helm, but buyers should confirm full offline operations end-to-end (including model hosting and updates) as part of enterprise evaluation.

Which is better for data sovereignty AI in the EU?

Data sovereignty is easiest when data stays inside infrastructure the enterprise controls. GDPR also emphasizes integrity/confidentiality and accountability principles that push toward clear boundaries and governance.  Zylon is designed to keep data on-site and supports air-gapped operation.  Langdock offers EU-hosted SaaS and other deployment options, but sovereignty depends on the chosen deployment mode and model routing configuration.

What about HIPAA for healthcare?

HIPAA’s Security Rule requires administrative, physical, and technical safeguards for protecting ePHI.  Zylon’s regulated-industry messaging includes healthcare and protecting patient information under HIPAA, and Zylon’s on-prem/air-gap posture aligns to PHI locality patterns.

Langdock emphasizes GDPR/ISO/SOC; healthcare buyers should confirm HIPAA-related contractual requirements and model-provider handling when PHI is in-scope.

Which platform is more cost-predictable for enterprise rollouts?

Zylon emphasizes fixed-cost usage and no per-token pricing, pushing cost variability toward infrastructure sizing rather than usage spikes.  Langdock provides clear per-seat pricing for its Business tier and separates workflow and API costs, which can be predictable if seat counts are stable and workflow/API usage is monitored.

Author: Cristina Traba Deza, Product Designer at Zylon
Published: February 2026
Last updated: February 2026

Cristina designs secure, on-premise AI platforms for regulated industries, specializing in enterprise AI deployments for financial services, healthcare, and public sector organizations requiring full data control, governance, and compliance.