On-Prem Control vs.
Cloud-Only Convenience
SageMaker is powerful but locks you into AWS. Jozu gives you enterprise AI governance on your infrastructure, with transparent pricing and no vendor lock-in.
AWS SageMaker is a comprehensive, cloud-only ML platform tightly integrated with AWS. It’s powerful but expensive, complex, and creates deep vendor lock-in. Jozu is an on-prem-first AI platform focused on model security, governance, and deployment — with vendor-neutral OCI packaging that runs on any Kubernetes cluster. Choose SageMaker if you’re all-in on AWS and don’t need on-prem. Choose Jozu if you need deployment flexibility, transparent costs, or operate in regulated industries.
Different philosophies. Overlapping use cases.
SageMaker and Jozu serve overlapping but different parts of the ML lifecycle. SageMaker is AWS’s fully managed ML platform — covering everything from notebooks to training to endpoints. It’s a “one cloud to rule them all” approach: powerful, but the price is deep AWS dependency.
Jozu takes a different philosophy. Rather than being an all-in-one platform, Jozu focuses on the critical gap most MLOps tools ignore: securing, governing, and deploying models to production. It integrates with your existing tools (MLflow, Kubeflow, KServe) and runs on any Kubernetes cluster — in the cloud, on-prem, or air-gapped. No cloud account required.
Feature Comparison
| Capability | Jozu | AWS SageMaker |
|---|---|---|
| Deployment model | ✓ On-prem, cloud, air-gapped, SaaS | ◉ Cloud only (AWS) |
| Vendor lock-in | ✓ None — OCI standard, any K8s | ✗ Deep AWS dependency |
| Model registry | ✓ Private, governed, with scanning | ✓ SageMaker Model Registry |
| AI security scanning | ✓ Backdoors, poisoning, injection, adversarial | ✗ No model-specific scanning |
| Tamper-proof packaging | ✓ SHA attestation, signed provenance | ✗ SageMaker-proprietary format |
| SBOM generation | ✓ Automatic SPDX 3 | ✗ Not available |
| Compliance frameworks | ✓ EU AI Act, ISO 42001, NIST AI RMF | ◉ AWS compliance (SOC, HIPAA), not AI-specific |
| Experiment tracking | ◉ Via MLflow integration | ✓ Built-in via Studio |
| Training infrastructure | ✗ Not a training platform | ✓ Managed training, HyperPod |
| Data labeling | ✗ Not a labeling tool | ✓ Ground Truth |
| Model serving | ✓ Rapid Inference Containers (10x faster) | ✓ Real-time, batch, serverless endpoints |
| Multi-cloud support | ✓ Any Kubernetes (EKS, GKE, AKS, bare metal) | ✗ AWS only |
| Packaging format | ✓ OCI-standard ModelKit (portable) | ✗ AWS-proprietary (hard to migrate) |
| Open source component | ✓ KitOps (CNCF Sandbox) | ◉ Some OSS tools, platform is proprietary |
| Pricing | Free trial; transparent enterprise pricing | Complex: instances + storage + endpoints + data transfer |
SageMaker’s biggest strength is also its biggest risk.
SageMaker’s deep AWS integration means models are stored in proprietary formats on S3. Endpoints run on SageMaker-specific instances. Logs live in CloudWatch. Pipelines use SageMaker’s orchestration. Feature stores, model monitors, and data labeling are all AWS-native.
Switching away from SageMaker means rebuilding your entire ML infrastructure. This isn’t a theoretical concern — many enterprises report that the switching cost becomes the primary reason they stay, not the product quality.
SageMaker instances cost 20–40% more than equivalent raw EC2 instances. Real-time endpoints run continuously regardless of traffic. Orphaned EBS volumes accumulate invisible charges. Multiple billing components make month-end cost forecasting unreliable. Teams regularly report “bill shock” after scaling SageMaker workloads.
Jozu takes the opposite approach. ModelKits are OCI-standard artifacts that work with any registry and any Kubernetes cluster. Moving from AWS to GCP, Azure, or bare metal means changing your Kubernetes context — not rebuilding your ML platform. Your models, metadata, security scans, and governance records travel with you.
Pricing: Transparent vs Opaque
AWS SageMaker
- Compute instances (20–40% premium over EC2)
- Always-on endpoint costs
- S3 storage charges
- Data transfer in/out
- Training job per-second billing
- Processing, Feature Store, Monitor fees
- Orphaned resource charges
- Savings Plans require 1–3yr commitment
Jozu
- KitOps is free (CNCF open source)
- Jozu Hub free trial available
- Enterprise pricing for on-prem/SaaS
- Runs on your existing K8s infrastructure
- No per-instance markup
- No data transfer charges
- No surprise endpoint costs
- You control your own compute spend
Cloud security is not AI security.
SageMaker inherits AWS’s security posture: IAM policies, VPC isolation, KMS encryption, and CloudTrail audit logs. These are strong cloud security primitives — but they’re not AI-specific. SageMaker doesn’t scan model weights for backdoors. It doesn’t generate SBOMs. It doesn’t verify model integrity with cryptographic attestations.
Jozu was built security-first for AI. Every model pushed to Jozu Hub is automatically scanned for AI-specific threats: code injection, backdoors, data poisoning, prompt manipulation, and adversarial attacks. Each ModelKit includes SHA256 digests, signed provenance attestations, and SPDX 3 software bills of materials. Deployment policies can block models that haven’t been scanned or signed.
Jozu’s governance features align with the EU AI Act, ISO 42001, and NIST AI Risk Management Framework. These frameworks require model provenance tracking, risk assessment documentation, and supply chain transparency — capabilities that SageMaker doesn’t natively provide at the model level.
Deploy anywhere. Not just AWS.
SageMaker excels at deploying models within AWS. Real-time endpoints with auto-scaling, batch transform jobs, and serverless inference are all well-integrated. But every endpoint runs on AWS infrastructure, and only AWS infrastructure. If your organization needs to deploy models on-premises, in air-gapped environments, or across multiple cloud providers, SageMaker has no answer.
Jozu generates deployment artifacts (inference containers and Kubernetes manifests) directly from ModelKits. The Rapid Inference Container (RIC) technology uses layer deduplication to deliver models to Kubernetes clusters up to 10x faster than rebuilding containers from scratch. This works on EKS, GKE, AKS, OpenShift, bare metal Kubernetes, or air-gapped clusters — wherever your workloads run.
On-Premises & Air-Gapped
Jozu Hub installs behind the firewall in approximately one hour via Helm chart. No data leaves your environment. Jozu has zero visibility into your models or data. This isn’t a workaround — it’s a core design principle for teams in government, defense, healthcare, and financial services where data residency requirements are non-negotiable.
SageMaker offers hybrid workarounds (VPN/Direct Connect to SageMaker endpoints, ECS Anywhere), but these aren’t true on-prem execution. Training and inference still happen in AWS. Data still transits through AWS infrastructure.
When to Choose Each
SageMaker fits if:
- → You’re fully committed to the AWS ecosystem
- → You need managed training infrastructure (GPU clusters, HyperPod)
- → Cloud-only deployment is acceptable for your use case
- → Data labeling (Ground Truth) is part of your workflow
- → Budget is less constrained than operational complexity
Jozu fits if:
- → On-prem or air-gapped deployment is a requirement
- → You need AI-specific security scanning and governance
- → Vendor lock-in is a strategic risk you want to avoid
- → Multi-cloud or hybrid deployment matters
- → Transparent, predictable pricing is important
- → Regulatory compliance (EU AI Act, NIST AI RMF) is required
- → You want to use your existing Kubernetes infrastructure
Different tools for different problems. Some teams use both. Train in SageMaker Studio, then package models as ModelKits for scanning, governance, and deployment to any Kubernetes cluster — including EKS. SageMaker’s training capabilities without SageMaker’s deployment lock-in.
Frequently Asked Questions
Can I use Jozu with SageMaker?
Yes. You can train models in SageMaker and use Jozu for production governance and deployment. The KitOps CLI packages SageMaker-trained models into portable ModelKits that work on any Kubernetes cluster, including EKS.
Is SageMaker available on-premises?
No. SageMaker is a cloud-only service. AWS offers hybrid workarounds through VPN/Direct Connect and ECS Anywhere, but training and inference still run on AWS infrastructure. Jozu Hub installs fully behind your firewall with no external connectivity required.
How does Jozu compare on cost?
SageMaker charges a 20–40% premium over raw EC2 instance costs, plus storage, data transfer, endpoint uptime, and numerous service-specific fees. Jozu runs on your existing Kubernetes infrastructure with no per-instance markup or surprise endpoint costs. KitOps is free as a CNCF open-source project.
Does Jozu offer managed training like SageMaker?
No. Jozu is not a training platform. It focuses on model security, governance, and deployment. Teams typically use existing training tools (SageMaker, Kubeflow, MLflow, custom pipelines) and bring trained models to Jozu for the production lifecycle.
Can I migrate models from SageMaker to Jozu?
Yes. The KitOps CLI can package models from any source — including SageMaker — into OCI-standard ModelKits. Once packaged, models can be deployed to any Kubernetes cluster and stored in any OCI registry, eliminating AWS dependency.
Deploy AI on Your Terms,
Not Your Cloud Provider’s
Try Jozu Hub free. Install on-prem in under an hour, or start with SaaS. No AWS account required.