Zama vs. Duality Technologies vs. Enveil: Choosing a Homomorphic Encryption Platform for Privacy-Preserving Analytics

Zama's architecture is a development stack, not a finished application. TFHE-rs implements the underlying TFHE scheme, Concrete provides an MLIR-based compiler that turns ordinary code into its homomorphic equivalent, and Concrete ML sits on top as a Python API that automatically converts quantized machine learning models into FHE-executable versions, with accuracy reported to stay within a few points of plaintext at 4-bit quantization. Zama also runs the Zama Protocol, live on Ethereum mainnet since late December 2025, which applies the same TFHE foundation plus a threshold-MPC key service to confidential token balances and transfers. The throughline is that Zama gives you cryptographic primitives and compilers, and your team (or a system integrator) still builds the actual encrypted application on top.

Duality's architecture starts from the opposite direction: a finished platform, SecurePlus, aimed at analysts and data scientists who should not need to understand FHE to use it. Per Duality's own CEO in TechCrunch's coverage of its 2021 funding round, the goal is that "any fraud analyst can click a mouse button and not care" about the cryptography underneath. In practice, Duality blends homomorphic encryption with other privacy-enhancing techniques (including federated learning) rather than running every computation under pure FHE, choosing whichever technique fits a given step in the analytics pipeline. Its most concrete published result is a genome-wide association study run jointly with Dana-Farber Cancer Institute and academic partners across more than 25,000 individuals' encrypted genetic and phenotype data, reported as roughly 30 times faster than a comparable multi-party computation approach. That is a specific, checkable benchmark for one workload, not a general performance claim across all analytics.

Enveil's architecture is narrower and more specialized: encrypted search and matching, branded ZeroReveal, designed so an organization can query, cross-match, or watchlist data that lives in an environment it does not control, without revealing the search terms, the results, or its own interest in the data to whoever holds it. Enveil states that ZeroReveal Search 6.0 (released July 2025) processes more than a gigabyte of data per second per thread. That throughput figure matters specifically because encrypted search and matching workloads are latency-sensitive in a way that batch analytics are not; a fraud investigator or intelligence analyst running a query expects a usable response time, not an overnight batch job.

Zama's open-source libraries deploy however you deploy any dependency: pulled into your own build, run on your own infrastructure or CI pipeline, with no vendor-hosted service required for the core Concrete ML and TFHE-rs stack. The Zama Protocol is the exception, since confidential transactions running on Ethereum mainnet depend on Zama's threshold-MPC key service alongside the onchain contracts, which introduces an external dependency that a purely offline FHE application would not have. Documentation describes both CPU and GPU coprocessor paths, so hardware planning is part of the deployment decision, not an afterthought.

Duality and Enveil are both sold as enterprise platforms through a sales-assisted deployment process rather than a self-serve download. Neither vendor publishes a self-hosted-versus-managed breakdown in the materials reviewed for this comparison, so confirm directly during a proof of concept whether the platform runs inside your own environment, in the vendor's cloud, or in a hybrid arrangement, since that materially changes both your compliance posture and your integration timeline. For Duality specifically, expect deployment to involve every data partner in the collaboration, not just your own organization, since the entire point of the platform is joint analysis across parties who do not otherwise share raw data.

Zama's integration surface is whatever you build it to be: Concrete ML ships Python bindings to traditional ML frameworks so a data scientist can convert an existing scikit-learn-style model rather than starting from cryptographic primitives, and fhEVM targets Solidity smart contract developers directly. That flexibility is also the cost: there is no vendor-managed integration layer connecting your SIEM, data warehouse, or MLOps pipeline to Zama's stack. Your engineering team owns that work.

Duality's public materials describe partnerships aimed at embedding HE into systems enterprises already run, including named work with Oracle and IBM, positioned as reducing the need for customers to become encryption experts themselves. For a specific deployment, confirm which of your own data platforms Duality actually has a built integration for versus which would require custom connector work, since a competitive-analytics or healthcare-research partnership can span several unrelated systems across multiple organizations.

Enveil's integration story centers on connecting to data sources you need to search or match against without moving or exposing that data, plus what the vendor describes as advanced server integrations optimized for edge deployments in its ZeroReveal 5.0 and 6.0 releases. As with Duality, get a specific answer during evaluation about which of your source systems (a watchlist database, a partner's customer records, an internal case-management system) Enveil has integrated before, rather than assuming general encrypted-search capability implies a ready-made connector to your particular data source.

Zama demands the most from your own team, and that is the direct tradeoff for owning the stack outright with no vendor lock-in and no per-seat licensing on the open-source libraries themselves. Someone on your team needs to understand quantization tradeoffs for Concrete ML, or Solidity and threshold-MPC concepts for fhEVM, and building a production encrypted application is a development project, not a configuration exercise. This fits organizations with in-house ML or blockchain engineering capacity and a specific application to build, not organizations looking for a turnkey analytics platform.

Duality's stated design goal is to push operational complexity onto the platform and initial integration, so that the analyst running a day-to-day query does not need cryptography expertise. That shifts effort toward the setup phase: establishing the collaboration framework with each data partner, agreeing on what computations are permitted, and integrating each party's data environment. Once that is in place, ongoing operational effort for the analyst is intended to be low, though you should validate that claim against your own partners' technical readiness rather than assuming it holds automatically.

Enveil sits in between: an encrypted search deployment is typically narrower in scope than a multi-party data-collaboration platform, since you are usually integrating your own query and matching workflows against a bounded set of external or restricted data sources rather than standing up a joint analytics environment across several organizations at once. That narrower scope generally means a shorter initial integration than Duality's model, but confirm the specifics against your own source systems before assuming that holds for your deployment.

None of the three vendors reviewed here publishes a flat, comparable enterprise price list. Zama's core libraries (TFHE-rs, Concrete, Concrete ML) are open source under a BSD-3 license per Zama's own GitHub repository, so there is no license fee for the software itself, though building and operating a production application still carries engineering and infrastructure cost, and the separate Zama Protocol's economics (gas costs, key-service fees) are an evolving, protocol-level matter distinct from the open-source libraries. Duality and Enveil are both sold through a sales-assisted enterprise process with quote-based pricing; neither publishes a public price list in the materials reviewed for this comparison. Do not treat the absence of a published number as a signal that either platform is more or less expensive than the other. It means you need a like-for-like quote from each, scoped to the same data volume, number of parties (for Duality) or search throughput requirement (for Enveil), and support tier, before comparing total cost of ownership.

Zama. Strength: open-source, no licensing cost for the core libraries, an active development ecosystem around TFHE-rs and Concrete ML, and a credible funding and valuation trajectory (per the Wavect review, the field's first FHE unicorn after a June 2025 Series B). Limit: it is a toolkit, not an application. If your organization needs a working analytics or search platform rather than a development stack, Zama is the wrong layer to evaluate first; you would be building on top of it, likely with a systems integrator or in-house team.

Duality Technologies. Strength: purpose-built for cross-organizational data collaboration in regulated industries, with a published, specific benchmark (the Dana-Farber genome-wide association study) rather than only marketing language, and named enterprise integration partnerships. Limit: pricing is opaque, deployment inherently involves every data partner in a collaboration (not just your own organization), and Duality's own materials describe blending HE with other privacy-enhancing techniques rather than running everything under pure FHE, so verify exactly which computations in your workload would actually run homomorphically versus through a different technique.

Enveil. Strength: a narrow, well-defined specialization in encrypted search and matching, with a concrete, vendor-stated throughput figure (over 1GB/s per thread for ZeroReveal Search 6.0) that is at least a checkable claim rather than a vague one, and government and financial-sector roots (Enveil is frequently described as CIA-backed in third-party coverage, reflecting its origin serving intelligence and financial-crime use cases). Limit: it is not a general-purpose analytics or ML platform; if your use case is joint statistical analysis or model training across organizations rather than search and matching, Enveil is solving a different problem than the one you have.

There is no universal winner among these three, because they are not really substitutes for each other. Match the platform to the workload.

A development team building a custom encrypted ML inference service, a confidential smart contract, or an FHE-based feature inside an existing product, with in-house engineering capacity to work directly with cryptographic libraries, fits Zama's open-source stack. This is the right starting point when the deliverable is an application you own and operate, not a vendor relationship.

A data or analytics team in a regulated industry (finance, healthcare, government) that needs joint statistical analysis, machine learning, or research collaboration across two or more organizations that cannot share raw data under existing agreements, is the clearest fit for Duality, particularly when the use case resembles the kind of work currently done through a data clean room or a bilateral data-sharing agreement and the volume of parties or datasets makes ad hoc arrangements unmanageable.

A security, fraud, or intelligence team that needs to search, cross-match, or watchlist data sitting in an environment it does not control, across organizational, jurisdictional, or security boundaries, without exposing the query or the result to the data holder, fits Enveil's encrypted search specialization better than either general alternative.

A small team evaluating FHE for the first time on a single, well-defined use case should generally start with whichever of Duality or Enveil matches that specific workload (collaboration versus search) rather than starting with Zama's open-source stack, unless the team already has dedicated cryptography or ML engineering capacity and a clear application to build rather than a platform to buy.

Skip all three, at least for now, if your privacy-sensitive workload can already be solved adequately by a narrower, cheaper control: role-based access control and encryption at rest for internal data, a standard data processing agreement for a single bilateral data share, or differential privacy for aggregate statistical release. FHE and encrypted-search platforms solve a specific problem (computing on data that must stay encrypted through the entire computation, often because a third party or counterparty must never see it), and reaching for that solution when a simpler control would satisfy your actual regulatory or contractual requirement adds cost and complexity without a corresponding benefit.

Also hold off if your workload's sensitivity is really about the compute environment rather than the mathematics of the computation itself, meaning you need to run an unmodified existing application on sensitive data inside a verifiably isolated environment rather than rewriting it to run homomorphically. That is the problem hardware-backed confidential computing solves, and our comparison of confidential computing for AI model protection across Nitro, Azure, and NVIDIA and our Fortanix vs. Opaque vs. Anjuna confidential computing comparison cover that adjacent but distinct category. And if your team ultimately wants to build directly on an open cryptographic library rather than adopt any commercial platform, including Zama's own tooling, our Microsoft SEAL and OpenFHE implementation guide walks through that path separately.

Before committing budget or engineering time to any of the three, validate the following directly rather than relying on a vendor's marketing materials:

1. Run one real query, model inference, or search against your actual data (or a representative synthetic equivalent) end to end, and measure the real latency and accuracy, not a primitive-level cryptographic benchmark, since the Wavect review specifically notes that primitive benchmarks cannot predict a complete application's performance.
2. For Zama, confirm which quantization level your specific ML model tolerates before assuming Concrete ML's reported near-plaintext accuracy applies to your workload, since accuracy tradeoffs vary by model type and bit depth.
3. For Duality, get a straight answer on which parts of your specific pipeline run under pure homomorphic encryption versus another privacy-enhancing technique blended into SecurePlus, and what that means for your compliance documentation.
4. For Enveil, validate the stated throughput (over 1GB/s per thread) against your own data format and query complexity, not just the vendor's benchmark conditions.
5. Ask every vendor for a specific, named reference customer or published case study in your industry, and treat the absence of one as a data point, not a disqualifier, given how young this market still is.
6. Confirm exactly what happens at the boundary: for Duality and Enveil, who else (the vendor, the counterparty, a cloud provider) could theoretically access key material or intermediate computation state, and get that answer in writing before signing.
7. Price each option against the same defined scope (data volume, number of parties or data sources, and expected query or inference rate), since none of the three publishes comparable list pricing and an apples-to-oranges quote comparison will mislead you.