Zama

Zama

Verified
4.4
15 saves
Updated September 10, 2026

Encrypt, compute, and protect data without decryption.

Key Takeaways

What it is
Encrypt, compute, and protect data without decryption.
Best for
  • Fully Homomorphic Encryption
  • Concrete Framework
  • Developer-Friendly Tools
Rating
4.4/5

What is Zama?

Zama is a pioneering tool in the field of cryptography, specializing in Fully Homomorphic Encryption (FHE). This technology allows for computations on encrypted data without needing to decrypt it first, providing a significant boost to data privacy and security. Designed for developers, data scientists, and businesses, Zama facilitates the integration of FHE into existing applications, ensuring that sensitive data remains confidential throughout its lifecycle.

Zama is notable for its advanced approach to Fully Homomorphic Encryption, providing a unique capability to perform computations on encrypted data. Its integration with popular programming languages and frameworks, and its focus on broad industry adoption, sets it apart in the field of confidential computing.

Zama excels in providing robust data privacy through its innovative use of Fully Homomorphic Encryption, making it an essential tool for entities handling sensitive data. Its integration with Python and support for machine learning applications offer a unique advantage in the field of confidential computing.

Who is Using Zama?

Financial Institutions

For secure, private financial transactions and analytics.

Healthcare Providers

To process confidential medical records and personal health information securely.

Government Agencies

For protecting state and national data during inter-departmental sharing.

Tech Companies

Developing new privacy-preserving technologies and services.

Uncommon Use Cases

Academic researchers for data-driven studies without accessing raw data; non-profits safeguarding sensitive demographic information.

Key Features

Fully Homomorphic Encryption: Enables operations on encrypted data, maintaining data security throughout the computation process.
Concrete Framework: Provides a robust framework that converts Python code into its homomorphic equivalent, making complex cryptography accessible without deep cryptographic knowledge.
Developer-Friendly Tools: Includes libraries such as TFHE-rs and fhEVM for boolean and integer arithmetic on encrypted data, and for writing confidential smart contracts.
Integration with Machine Learning: The Concrete ML framework works with traditional ML frameworks to preserve privacy in machine learning workflows.
Extensive Documentation and Community Support: Offers comprehensive documentation, an active Discord community, and a repository of research papers for continuous learning and support.

Rating Breakdown

4.4/ 5.0
accuracy
4.6
ease of use
4.2
features
4.8
speed
3.9
support
4.7
value
4.3

Pricing Plans

Free Access

  • A community version is available on GitHub for exploring Zama’s capabilities

Enterprise Solutions

  • Pricing is tailored based on specific needs and scale

Zama Use Cases

Confidential RWA tokenization

Bring real-world assets onchain while protecting investor identities, deal terms, and cap-table structure.

Outcome: Private, compliant asset tokenization.

Confidential payments and payroll

Settle, invoice, and compensate onchain without exposing transaction amounts.

Outcome: Private onchain payments and compensation.

Confidential DeFi and trading

Trade, stake, and earn yield without revealing positions, strategies, or counterparty data.

Outcome: Reduced information leakage in financial activity.

Confidential token distribution

Run vesting, airdrops, and allocations without exposing recipient amounts.

Outcome: Private token distributions.

Confidential smart-contract development

Use Zama libraries and SDKs to build contracts that compute on encrypted values.

Outcome: Deployable confidential dApps and tokens.

Workflow Ideas with Zama

Build a confidential token dApp

  1. 1Initialize the Relayer SDK in a web application
  2. 2Configure encryption and decryption settings
  3. 3Create encrypted inputs and interact with confidential contracts

Wrap an ERC-20 token confidentially

  1. 1Find and validate the token’s confidential wrapper in the registry
  2. 2Approve the wrapper contract
  3. 3Wrap ERC-20 tokens into confidential ERC-7984 tokens

Delegate ZAMA for staking

  1. 1Choose an operator pool
  2. 2Approve the operator staking contract
  3. 3Deposit ZAMA and receive operator staking shares

How Zama fits into your stack

Python Integration

Direct support for Python, enabling use with familiar tools.

Blockchain Compatibility

Supports confidential smart contracts, enhancing security in blockchain applications.

Machine Learning Frameworks

Compatible with existing ML frameworks through Concrete ML.

Extensive Documentation

Provides rich resources for integrating and extending Zama’s functionalities.

Pros

  • +Enhanced Data Privacy: Allows for data analysis and machine learning while preserving user privacy.
  • +Ease of Use: Simplifies FHE deployment with user-friendly libraries and frameworks, reducing the need for deep cryptographic expertise.
  • +Versatile Applications: Suitable for various industries, including finance, healthcare, and the public sector.
  • +Active Community and Support: Provides robust support through documentation, GitHub repositories, and an active community.

Cons

  • −Performance Overhead: FHE can introduce slower performance compared to operations on unencrypted data.
  • −Resource Intensity: Requires significant computational resources, which may be challenging for smaller organizations.
  • −Learning Curve: Initial setup and integration into existing systems can be complex.

Limitations & when to consider alternatives

Known limitations

  • Encrypted control flow constraints. Concrete does not support if or while conditions that depend on encrypted values.
  • Floating-point limitations. Concrete does not support floating-point inputs or outputs; encrypted values use supported integer precision.
  • Encrypted bit-width limits. Encrypted values have a maximum bit width, and exceeding it causes an error.
  • Confidential-token decimal limit. Confidential wrappers currently support a maximum of six decimals.
  • Unsupported token types. Non-standard fee-on-transfer and deflationary tokens are not supported by confidential wrappers.

Look elsewhere when

  • You need a conventional general-purpose blockchain platform.
  • Your application requires unsupported encrypted control flow.
  • Your token requires more than six confidential decimals.

How to Use Zama

  1. 1

    Explore a range of tutorials on Zama's official GitHub page or through their extensive documentation to understand and maximize the tool's potential.

Who is Zama For?

Institutions

Zama targets institutions that need confidentiality and compliance for public-blockchain activities.

Blockchain developers

Developers can build confidential smart contracts, dApps, wallets, and token integrations.

Wallet and dApp developers

The Relayer SDK and wallet guides support confidential-token balances and transfers.

Operators

Operators run protocol infrastructure such as KMS nodes and FHE coprocessors.

Token holders and delegators

Token holders can delegate ZAMA to operator pools and participate in staking.

Frequently Asked Questions

What is Zama?

Zama is an open-source cryptography company building Fully Homomorphic Encryption solutions for blockchain and AI.

What does the Zama Protocol do?

The protocol enables confidential assets and encrypted, verifiable computation on public blockchains.

What is the Zama SDK?

The Zama SDK provides a TypeScript interface for building applications with confidential tokens.

How can developers build with Zama?

Developers can use Solidity guides, the Relayer SDK, code examples, and protocol libraries.

What are Zama Protocol apps?

The official protocol apps support activities such as staking, governance, and confidential-token operations.

What is Fully Homomorphic Encryption?

FHE enables computation on encrypted data without revealing the underlying information.

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