Data Self vs Pangolin : AI Open Source and Private Cloud Solution Comparison

Comparison between Data Self and Pangolin to choose an open-source AI and private cloud solution. Analysis of performance, compliance, and self-hosting.

The DATALIA team · Published August 2026 · Updated August 2026

Direct answer: Data Self excels in local performance with open-source models optimized for controlled environments, while Pangolin offers a unified AI gateway capable of handling both cloud models (OpenAI, Anthropic, Gemini) and self-hosted models via an identity proxy. For a small-to-medium business seeking a balance between sovereignty and simplicity, Pangolin 1.22 provides a more integrated approach. For an organization demanding maximum performance on local hardware, Data Self is preferable.

Table of Contents

1. Comparison criteria
2. Data Self: local performance and open-source models
3. Pangolin 1.22: the unified AI gateway
4. Criterion-by-criterion comparison
5. Which one to choose based on your profile?
6. Frequently asked questions

Comparison criteria

To compare Data Self and Pangolin, we evaluated six essential criteria for IT decision-makers, CTOs, and compliance teams:

• Data sovereignty: ability to keep data on local or private infrastructure.
• Performance: latency, throughput, and response quality according to deployed models.
• Deployment ease: complexity of installation, configuration, and maintenance.
• Regulatory compliance: GDPR, AI Act, HDS according to sectors.
• Scalability: ability to scale up or integrate new models.
• Total cost of ownership: infrastructure, licenses, staff, and support.

Data Self: local performance and open-source models

General overview

Data Self is an open-source AI platform designed for local deployment of large language models. It focuses on optimizing open-source models such as Llama, Mistral, and other community models, adapting them to controlled environments. The goal is to offer organizations a high-performance alternative to cloud APIs while maintaining full control over data.

Strengths of Data Self

• High local performance: Data Self uses quantization techniques (4-bit, 8-bit) to run models from 7B to 32B on standard hardware, delivering performance comparable to cloud solutions in certain use cases.
• Full control: All data remains on local infrastructure, ideal for regulated sectors such as healthcare or finance.
• Model flexibility: Supports a wide range of open-source models, allowing teams to choose the model most suitable for their use case.

Weaknesses of Data Self

• Deployment complexity: Requires technical expertise for installation, configuration, and model optimization.
• Scalability limitations: Local deployment can become costly in terms of infrastructure for heavy workloads.
• Limited support: As an open-source solution, support depends on the community, which can be a barrier for businesses.

Pricing and target audience

Data Self is typically used by organizations with internal technical teams capable of managing deployment and maintenance. The main cost is related to hardware infrastructure and engineer time.

For whom?

Data Self suits organizations that:

• Require full data sovereignty.
• Have internal technical teams to manage deployment.
• Intensively use open-source models and wish to optimize them locally.

Pangolin 1.22: the unified AI gateway

General overview

Pangolin 1.22 introduces a major feature: the AI Gateway. This gateway acts as an identity proxy in front of public cloud APIs (OpenAI, Anthropic, Gemini) and self-hosted model servers (Ollama, vLLM, Mistral). It enables coding agents and AI clients to call a single Pangolin URL, simplifying access to various AI services.

Strengths of Pangolin 1.22

• Unified access: A single URL to access cloud and self-hosted models, reducing multi-provider management complexity.
• Advanced management: Budgets, session history, and usage analysis for each call, providing complete visibility into AI usage.
• Security and identity: The proxy is identity-aware, integrating existing users, roles, and permissions.
• Deployment flexibility: Can be made public with personal virtual API keys or private via the Pangolin client tunnel.

Weaknesses of Pangolin 1.22

• Cloud dependency: Although it manages self-hosted models, the core of Pangolin relies on cloud services for certain features.
• Adoption cost: Advanced features may require a paid subscription, especially for editions supporting the AI Gateway.
• Learning curve: Initial setup of the AI Gateway may require an adaptation period.

Pricing and target audience

Pangolin offers a free Community edition with public and private SSH, RDP, and VNC resources. The AI Gateway is available in higher editions, with pricing based on usage or subscription.

For whom?

Pangolin suits organizations that:

• Use a combination of cloud and self-hosted AI services.
• Need centralized management and unified security policies.
• Seek a balanced solution between sovereignty and performance, without sacrificing cloud innovation.

Criterion-by-criterion comparison

Data sovereignty

Data Self: Offers maximum data sovereignty by keeping all data on local infrastructure. No risk of leakage to third parties, ideal for strict compliance requirements.

Pangolin: While it supports self-hosting, its advanced features rely on cloud services, potentially creating data leakage points.

Performance

Data Self: Performance heavily depends on hardware and model optimization. Quantization techniques allow running powerful models on standard hardware, but results may fall short of highly optimized cloud services.

Pangolin: Directly accesses high-performance cloud models (GPT-4, Claude 2, Gemini), delivering superior performance for complex tasks. For self-hosted models, performance depends on local configuration.

Deployment ease

Data Self: Deployment requires in-depth expertise in AI model management, hardware setup, and software optimization. This can be a barrier for small organizations.

Pangolin: Pangolin's unified interface simplifies access to AI services, reducing multi-provider management complexity. The AI Gateway provides centralized configuration.

Regulatory compliance

Data Self: Perfect for regulated sectors (healthcare, finance) requiring strict GDPR and AI Act compliance. Data never leaves local infrastructure.

Pangolin: Compliance depends on integrated cloud services. Organizations must evaluate each cloud provider's compliance when using Pangolin.

Scalability

Data Self: Scalability is limited by local hardware capacity. Significant increases in request volume may require additional hardware investments.

Pangolin: Offers excellent scalability through cloud services, enabling quick scaling without local hardware investments.

Total cost of ownership

Data Self: Initial costs can be high due to hardware investment and the technical time required for configuration and maintenance. However, it can be more economical long-term for high query volumes.

Pangolin: The freemium model with premium subscriptions can become costly, but offers lower upfront costs and easy scalability.

Which one to choose based on your profile?

The choice between Data Self and Pangolin depends on your specific priorities:

• For sovereignty and compliance: If you operate in a regulated sector or data confidentiality is paramount, Data Self is the best option due to its fully local deployment.
• For flexibility and innovation: If you use a combination of cloud and self-hosted AI services and need a unified solution, Pangolin 1.22 with its AI Gateway offers a more integrated and scalable approach.
• For small organizations: If you lack internal technical expertise, Pangolin offers a gentler learning curve and more accessible support.
• For large companies with high intensity: Data Self may offer a better cost/performance ratio in the long run, but requires a dedicated technical team.

Advice for choosing

• Assess your sovereignty needs: If compliance is critical, prioritize a fully local solution like Data Self.
• Consider operational complexity: A solution like Pangolin can reduce operational load, but verify cloud dependencies.
• Test before committing: Deploy a pilot on a specific use case to measure performance, cost, and ease of use.
• Plan for skill development: Self-hosting requires technical skills you must either possess or acquire through a partner.

Role of DATALIA

At DATALIA, we help organizations navigate between AI innovation and compliance. Whether you choose Data Self for its sovereignty or Pangolin for its integration, our digital transformation expertise guides you in choosing, deploying, and training your teams on these technologies. We combine consulting, custom integration, and training to ensure your AI adoption remains controlled, compliant, and high-performing.

Conclusion

Data Self and Pangolin represent two distinct approaches to adopting AI in your organization. Data Self excels in data sovereignty and local performance, but requires significant technical expertise. Pangolin 1.22, with its AI Gateway, offers a unified and scalable solution, ideal for organizations navigating multiple cloud environments. The final choice depends on your technical maturity, compliance requirements, and cloud strategy.

Regardless of the solution chosen, it is crucial to consider not only immediate performance but also maintainability, compliance, and future growth. A thorough evaluation of both options, based on a concrete pilot, will help you make an informed decision.

Frequently asked questions

Is Data Self suitable for SMEs?

Data Self is technically suitable for any organization size, but its deployment relies on solid internal expertise. SMEs without a dedicated technical team might find Pangolin more accessible, while SMEs with DevOps skills can take advantage of Data Self's data sovereignty.

How does Pangolin handle GDPR compliance?

Pangolin supports self-hosting for models, but its advanced features sometimes rely on cloud services. Organizations must evaluate the compliance of each integrated cloud service and implement policies to ensure data protection according to the GDPR.

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