Zama is a privacy-first AI and cryptography company focused on making Fully Homomorphic Encryption (FHE) practical for real-world applications. The platform enables companies to run machine learning models and process data in encrypted form without ever exposing the raw data, even during computation.
FHE is a revolutionary cryptographic technique that allows computations on encrypted data as if it were unencrypted. Zama is leading the charge to bring this technology into production by developing tools, SDKs, and libraries that make it accessible for developers and organizations seeking to protect user privacy while leveraging powerful AI models.
Whether you’re in healthcare, finance, or any regulated industry, Zama helps you build AI applications where sensitive data never has to be decrypted—solving one of the biggest challenges in secure computing.
Features of Zama
Fully Homomorphic Encryption (FHE)
Zama’s core innovation is its ability to perform computations on encrypted data without needing to decrypt it first. This ensures complete data privacy during processing.
Open-Source TFHE-rs Library
Zama maintains TFHE-rs, an open-source Rust library that implements the TFHE (Torus Fully Homomorphic Encryption) scheme. It offers fast and efficient FHE operations suitable for real-world applications.
Concrete ML
Concrete ML is Zama’s privacy-preserving machine learning framework. It allows developers to train and deploy ML models that can make inferences on encrypted data, maintaining compliance and confidentiality.
Concrete Numpy
This tool lets data scientists convert Python/Numpy-based inference scripts into encrypted versions that can run without revealing input data. It’s especially useful for those familiar with the Python ML ecosystem.
Developer SDKs and APIs
Zama provides SDKs and APIs to make integration of FHE into existing applications seamless, even for teams without deep cryptography expertise.
FHE-as-a-Service (Coming Soon)
Zama is developing a managed service offering that will allow enterprises to deploy FHE workflows in the cloud without maintaining complex infrastructure.
Security and Compliance
By enabling computation on encrypted data, Zama inherently supports compliance with privacy regulations such as GDPR, HIPAA, and CCPA.
Active Community and Documentation
With comprehensive documentation, tutorials, and an open-source ecosystem, Zama makes it easier for developers and researchers to experiment and build with FHE.
How Zama Works
Zama enables privacy-preserving computation through Fully Homomorphic Encryption. Here’s how it works at a high level:
When data is generated—say, a patient’s health record or a user’s banking transaction—it is encrypted using Zama’s FHE tools. This encrypted data can then be sent to a server or cloud where computations are performed.
Unlike traditional encryption, where data must be decrypted before processing, FHE allows Zama to run AI models directly on this encrypted data. The output of the computation is also encrypted, ensuring that sensitive information is never exposed during transit, at rest, or even during processing.
Developers use Zama’s tools such as Concrete ML or Concrete Numpy to create and convert models capable of operating in encrypted space. Once deployed, these models accept encrypted inputs, return encrypted results, and only the data owner can decrypt the final outputs.
This architecture provides end-to-end security and is especially valuable in multi-party environments where different stakeholders need to collaborate on sensitive data without sharing raw information.
Use Cases of Zama
Healthcare AI
Hospitals and research institutions can run diagnostic models on encrypted medical data without exposing patient records, enabling HIPAA-compliant AI inferences.
Financial Services
Banks and fintech platforms can analyze user transactions or credit scores without ever decrypting customer data, supporting GDPR compliance and reducing data breach risk.
Cross-Enterprise Collaboration
Organizations can jointly analyze shared datasets (e.g., for fraud detection or risk modeling) without revealing proprietary or sensitive data to each other.
Biometrics and Identity
Zama enables verification of biometric data (e.g., facial recognition or fingerprints) in encrypted form, protecting user identity during processing.
Government and Defense
Sensitive national datasets can be processed securely without leaking confidential information, supporting defense applications and secure intelligence processing.
Cloud AI Services
Cloud service providers can offer AI-as-a-service without requiring access to customer data in plaintext, increasing trust and adoption.
Pricing of Zama
As of now, Zama does not provide a commercial pricing structure directly on its website. The platform is currently focused on open-source development and building enterprise-grade tools. Several components of Zama’s technology stack, such as the TFHE-rs library and Concrete ML, are available for free and can be accessed on GitHub.
Zama’s future offerings, including FHE-as-a-Service, are likely to follow an enterprise or usage-based pricing model. Companies or researchers interested in early access, custom integrations, or commercial support are encouraged to contact the Zama team directly via their website.
Strengths of Zama
End-to-End Privacy
Zama ensures data privacy during every stage of processing, which is a game-changer for industries dealing with highly sensitive data.
Cutting-Edge Cryptography
The platform brings practical FHE into the hands of developers, something that has traditionally been limited to academia and niche use cases.
Open-Source and Transparent
Zama maintains a strong open-source ethos, with transparent development and accessible tools that foster community collaboration.
Developer Accessibility
Tools like Concrete ML and Concrete Numpy make it easier for machine learning and data science teams to adopt FHE without needing to learn cryptography from scratch.
Future-Proof for Regulation
Zama positions businesses to stay ahead of privacy regulations by offering secure-by-design solutions that reduce exposure to compliance risk.
Ecosystem and Documentation
Zama supports its user base with high-quality documentation, sample projects, and an active developer community.
Drawbacks of Zama
Early-Stage Product Maturity
While innovative, some parts of the Zama stack are still in early release stages and may not yet be production-ready for every enterprise use case.
Performance Overhead
FHE is computationally intensive. Even though Zama optimizes for speed, running encrypted computations can still be slower than traditional unencrypted processing.
Limited Commercial Offerings
As of now, Zama’s tools are mostly open-source, and enterprise-level managed services are still under development.
Requires Specialized Knowledge
Even with user-friendly SDKs, integrating FHE may still require understanding cryptographic concepts and adapting models for encrypted computation.
Evolving Ecosystem
As FHE is a cutting-edge domain, libraries and APIs are actively evolving, which may introduce occasional instability or changes in implementation.
Comparison with Other Tools
Compared to data privacy platforms like Duality, Inpher, or Cape Privacy, Zama stands out for its commitment to open-source development and deep focus on making FHE usable for developers at scale.
While some platforms offer secure multi-party computation or secure enclaves (e.g., using Intel SGX), Zama takes a pure FHE approach, which does not rely on hardware-based trust. This makes Zama more flexible and broadly applicable, especially in cloud environments.
In contrast to confidential computing frameworks, which protect data during computation through isolated environments, Zama protects data throughout the entire lifecycle—even while it’s being used.
Zama also differs from platforms like OpenMined and Microsoft SEAL by focusing more heavily on developer usability and offering ML-specific tools like Concrete ML that reduce barriers to practical deployment.
Customer Reviews and Testimonials
While detailed customer reviews are not yet widely available due to the emerging nature of the product, Zama is actively collaborating with a number of enterprise and academic partners. Feedback from the developer community highlights:
Strong documentation and ease of onboarding
Impressive performance benchmarks for FHE in real-world use cases
Active support and rapid iteration from the Zama team
Valuable GitHub repositories and practical examples for machine learning use cases
High potential for production use in sectors requiring maximum data confidentiality
As the platform matures, formal case studies and enterprise testimonials are expected to be published.
Conclusion
Zama is at the forefront of privacy-preserving AI, pioneering the use of Fully Homomorphic Encryption to unlock new possibilities for secure, compliant data processing. By enabling machine learning on encrypted data without ever exposing the raw information, Zama empowers organizations to maintain trust, meet regulatory demands, and innovate responsibly.
Its open-source tools, accessible SDKs, and cutting-edge cryptographic foundations make Zama one of the most promising players in the growing field of secure computation. Although still evolving, the platform is already attracting attention from data-intensive sectors such as healthcare, finance, and government.
For organizations looking to future-proof their AI infrastructure while putting privacy first, Zama represents a powerful and forward-thinking solution.
