Which Model Registry Fits
Your Enterprise?
A side-by-side comparison of security, governance, deployment, and packaging for teams choosing an ML model management platform.
MLflow is the go-to open-source tool for experiment tracking and lightweight model management. Jozu picks up where MLflow stops — adding enterprise-grade security scanning, tamper-proof OCI packaging, governance and audit trails, and on-prem deployment. Many teams use both together: MLflow for experiments, Jozu for production security and governance.
Different tools, different problems. Often better together.
MLflow and Jozu solve different parts of the ML lifecycle, and they’re not mutually exclusive. MLflow, originally created by Databricks, has become the default open-source platform for experiment tracking and model versioning. Jozu, built around the CNCF-backed KitOps project, focuses on what happens after experimentation: securing, governing, and deploying models to production — especially in on-premises or air-gapped environments.
The question isn’t always “which one?” — it’s “do I need both?” If your team operates in a regulated industry, deploys on-prem, or needs tamper-proof model packaging, the answer is usually yes.
Feature Comparison
| Capability | Jozu | MLflow (Open Source) |
|---|---|---|
| Experiment tracking | ◉ Via MLflow integration | ✓ Core feature |
| Model registry | ✓ Private, on-prem registry | ✓ Basic versioning & stages |
| Security scanning | ✓ AI-specific: backdoors, data poisoning, code injection | ✗ Not available |
| Tamper-proof packaging | ✓ SHA attestation, signed provenance | ✗ No integrity verification |
| Software bill of materials (SBOM) | ✓ SPDX 3 built-in | ✗ Not available |
| Audit trails | ✓ Per-version scan results, downloadable logs | ✗ Not in open source |
| On-prem deployment | ✓ Behind-firewall install in ~1 hour | ◉ Self-hosted but no enterprise features |
| Air-gapped support | ✓ Full support, no external connectivity | ✗ Not designed for air-gapped |
| Kubernetes deployment | ✓ Auto-generates manifests + inference containers | ◉ Requires manual Docker + K8s setup |
| Model packaging format | ✓ OCI-standard ModelKit (vendor-neutral) | ◉ MLmodel format (MLflow-specific) |
| Authentication & RBAC | ✓ Integrates with existing auth systems | ✗ No auth by default in OSS |
| Regulatory compliance | ✓ EU AI Act, ISO 42001, NIST AI RMF | ✗ No compliance features |
| Hugging Face import | ✓ One-click import with auto-scanning | ◉ Manual download required |
| Open source | ✓ KitOps (CNCF Sandbox) | ✓ Apache 2.0 |
| Pricing | Free trial; enterprise pricing | Free (self-hosted); Databricks managed $$$ |
Experiment-first vs. security-first.
The core architectural difference is scope. MLflow is an experiment-first platform: it tracks runs, logs metrics, and versions models. It’s optimized for the iterative work data scientists do day-to-day. But MLflow was not designed to be a production security layer. Open-source MLflow ships without authentication, has had multiple critical security vulnerabilities (including four CVSS 10.0 scores in a 50-day span), and lacks model-specific threat scanning.
Jozu approaches the problem from the opposite end. It’s a production-security-first platform built around the OCI standard. Models are packaged as tamper-proof ModelKits — OCI artifacts that include the model, datasets, code, configs, and metadata in a single, signed, verifiable package. When a ModelKit is pushed to Jozu Hub, it’s automatically scanned for AI-specific threats: code injection, backdoors, data poisoning, and adversarial attack vectors. Every version gets a cryptographic attestation and an SPDX 3 software bill of materials.
This means Jozu integrates with MLflow rather than replacing it. A common pattern: data scientists track experiments in MLflow, then the PyKitOps SDK packages the winning model into a ModelKit. From there, Jozu handles scanning, governance, signing, and deployment to Kubernetes.
Packaging: MLmodel vs ModelKit
MLflow’s MLmodel format is a directory containing model files plus a YAML descriptor. It’s straightforward but proprietary to MLflow’s ecosystem and doesn’t include security metadata, integrity verification, or supply-chain provenance.
Jozu’s ModelKit is built on the OCI Image Manifest Specification — the same standard used by Docker containers. This means ModelKits work with any OCI registry (Docker Hub, ECR, GCR, Harbor, Artifactory) and include SHA256 digests for every artifact, signed provenance attestations, and SPDX 3 SBOMs. Because they’re OCI-native, your DevOps team already knows how to work with them.
The widest gap between the two platforms.
| Security Dimension | Jozu | MLflow OSS |
|---|---|---|
| Default authentication | ✓ Built-in, integrates with LDAP/SSO | ✗ None — open by default |
| AI threat scanning | ✓ Backdoors, poisoning, injection | ✗ Not available |
| Supply chain integrity | ✓ SHA attestation + signed provenance | ✗ No integrity checking |
| SBOM generation | ✓ Automatic SPDX 3 | ✗ Not available |
| Vulnerability history | No reported critical CVEs | 4 CVSS 10.0 vulns in 50 days (RCE, file overwrite) |
| Policy enforcement | ✓ Block unscanned/unsigned deployments | ✗ No deployment gates |
For teams in finance, healthcare, government, or any industry subject to AI regulation, this gap is a dealbreaker for using standalone MLflow in production.
When to Choose Each
Choose MLflow if you need:
- → Lightweight experiment tracking for a small team
- → A free, self-hosted solution for R&D
- → Quick model versioning without governance requirements
- → Integration with Databricks-managed infrastructure
- → Hyperparameter tuning and metric comparison
Choose Jozu when:
- → On-prem or air-gapped model deployment
- → AI-specific security scanning before production
- → Regulatory compliance (EU AI Act, NIST AI RMF, ISO 42001)
- → Tamper-proof model packaging with audit trails
- → Kubernetes-native deployment with governance gates
- → Vendor-neutral OCI packaging (no lock-in)
Use both together when:
From experiment to production.
Getting MLflow into production typically means combining it with Airflow for orchestration, Docker for containerization, and Kubernetes for deployment — a pattern the MLOps community has nicknamed the “makeshift monster.” Each piece needs separate configuration, monitoring, and security hardening. MLflow itself has no opinion on how models reach production.
Jozu takes a different approach. When you push a ModelKit to Jozu Hub, it auto-generates deployment artifacts: inference containers and Kubernetes manifests. The Rapid Inference Container (RIC) technology uses layer deduplication to deliver models to clusters up to 10x faster than standard container builds. CI/CD pipelines pull signed ModelKits, validate SHA digests, confirm security scans pass, and deploy — all in a standard pipeline you’d build with Jenkins, GitHub Actions, or GitLab CI.
For on-prem teams, Jozu Hub installs behind the firewall in about an hour via Helm chart. No data leaves your environment. Jozu has zero visibility into your models or data — a critical requirement for organizations with strict data residency policies.
Free doesn’t mean free when you add what’s missing.
MLflow is free and open source. However, enterprise features (authentication, governance, Unity Catalog integration) require Databricks, where pricing is consumption-based and can escalate quickly based on compute, storage, and support tiers.
Jozu’s open-source component, KitOps, is free (CNCF Sandbox project). Jozu Hub is available as a free trial with enterprise pricing for on-prem and SaaS deployments. The key cost consideration is what MLflow doesn’t include: if you need to bolt on authentication, scanning, governance, and deployment orchestration separately, the total cost of a DIY MLflow stack can exceed a purpose-built solution.
Frequently Asked Questions
Can I use Jozu and MLflow together?
Yes — they’re complementary. MLflow handles experiment tracking, and Jozu handles production security, governance, and deployment. The PyKitOps SDK integrates directly with MLflow to package trained models into ModelKits automatically.
Does Jozu replace MLflow’s model registry?
It can, but most teams use both. MLflow’s registry is great for R&D versioning. Jozu’s registry adds security scanning, signed attestations, audit trails, and policy enforcement — the governance layer that MLflow’s open-source registry doesn’t provide.
Is Jozu only for on-prem deployments?
No. Jozu offers SaaS, on-premises, and private-environment deployment options. However, the on-prem and air-gapped capabilities are a core differentiator that MLflow and cloud-only platforms don’t match.
What is a ModelKit?
A ModelKit is an OCI-compliant artifact that bundles a model, datasets, code, configs, and metadata into a single, signed, tamper-proof package. It works with any OCI registry (Docker Hub, ECR, Harbor, etc.) and includes SHA256 digests for every component.
How does Jozu handle security compared to Databricks managed MLflow?
Databricks adds enterprise features like Unity Catalog and LDAP. But even managed MLflow doesn’t offer AI-specific threat scanning (backdoors, data poisoning, prompt injection), SPDX 3 SBOMs, or tamper-proof packaging. Jozu was built security-first from day one for these use cases.
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Your ML Pipeline?
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