Ship faster, ship safer, ship sovereign. A fully integrated software supply chain platform that unifies source control, CI/CD, container registry, and ML operations under one identity — so your team builds, deploys, and audits from one place, not five.
Ship faster, ship safer, ship sovereign. A fully integrated software supply chain platform that unifies source control, CI/CD, container registry, and ML operations under one identity — so your team builds, deploys, and audits from one place, not five.
Source control, container registry, ML tracking, code search, and deployment — one platform, one identity, one protocol, one audit trail. No bolting together GitHub, Harbor, and MLflow with separate auth.
No Go services, no Python sidecars, no Node.js microservices. One Rust binary with native Git engine, native OCI registry, and native ML tracking — fast, memory-safe, and deployable anywhere.
Seven deployment classes including air-gapped for classified networks and edge-constrained for industrial sites. Quantum-resistant signing. No competitor offers this range.
Models use the same registry, same identity, same deployment gates as containers. No separate ML platform handoff — model promotion uses the same approval chain as a production deploy.
No dependency on upstream GitHub, GitLab, Harbor, or MLflow release schedules or licensing changes. Haephestus owns the entire surface — every line of code, every protocol, every security decision.
Every commit, every artifact, every model version, every deployment — traceable with deterministic provenance and tamper-evident audit trails. SBOM, vulnerability scanning, and compliance evidence built in.
Most engineering teams run a Frankenstein toolchain — GitHub for source code, Harbor for containers, MLflow for experiments, Jenkins for CI, and a wiki nobody updates. Each has its own login, its own permissions, its own audit trail, and its own bill. When a security audit asks who pushed what image to production and which model version is deployed, the answer requires correlating logs across four systems that were never designed to talk to each other.
Haephestus is one platform that handles the entire software supply chain — source control, code review, CI/CD, container registry, package registry, ML experiment tracking, model registry, code search, and deployment — under one identity system, one permission model, one protocol surface, and one audit trail. Built in Rust. No Go services. No Python sidecars. No separate auth for the registry that the security team set up without telling anyone.
Repository hosting, code review, pull requests, issues, wikis, CI/CD pipelines, webhooks, releases, migrations, mirrors — everything GitHub and GitLab do, but built in Rust with one identity system and one audit trail. Your developers use the same Git workflows they already know.
The Git engine is native Rust — not a wrapper around the C Git binary. Object storage, pack files, refs, diff, merge, rebase, transport, LFS, submodules — all native. No shelling out to external processes. Faster, safer, and debuggable as one system.
One login. Every tool. The developer who pushes code, the DevOps engineer who deploys the image, and the data scientist who promotes the model are the same person in the same system — not three identities across three vendor consoles with three different permission models.
When a new developer joins, you grant access once — not once per tool. When a contractor leaves, you revoke once — not hope you remembered the Harbor admin account they set up on a Tuesday. When a security audit asks who has access to push production images, the answer is one query — not a spreadsheet you maintain by hand.
Run Haephestus on your own infrastructure — bare metal, VM, or Kubernetes. Your code, your containers, your models, your data — all on your network. No outbound telemetry. No phone-home. The same platform that runs in your data center is the same platform that runs in the cloud.
Trains models with full experiment provenance — training data, hyperparameters, code version, validation metrics. Promotes models through the same registry as containers. No separate ML platform with its own auth and its own audit trail.
Deploys containers and models through the same gates — identity, permissions, scanning, signing. One CLI, one API, one audit trail for both. No context switching between GitHub, Harbor, and MLflow.
Traces any artifact from commit to deployment in one query. SBOM, vulnerability history, and approval chain — all in one tamper-evident log. No cross-referencing across four vendor consoles.
Manages one platform instead of four. One deployment, one upgrade cycle, one monitoring dashboard, one bill. Air-gapped, edge, or cloud — the same binary, the same governance.
Model versioning with the same rigor as container images — signed, scanned, provenance-tracked. When a model is promoted from staging to production, it goes through an approval chain with evidence: training data, validation results, bias checks, performance metrics. No model reaches production without a documented, auditable approval.
One dashboard for the entire software supply chain — repositories, registries, ML experiments, deployments, and security. Deployment-class-aware: the UI adapts to your deployment mode — full in self-hosted, governed in managed, bounded in edge, reduced in air-gapped. No features you cannot use, no missing features you need.
One platform replaces 6+ tools. Fewer logins, fewer bills, fewer security gaps. GitHub, Harbor, MLflow, Jenkins, code search, and wiki — all consolidated into one system with one identity and one audit trail.
Your security team reviews one attack surface, not six. Your finance team pays one invoice, not six. Your engineers learn one API, not six.
Seven deployment classes — one binary serves all, from dev laptop to classified network. Self-hosted on your metal, managed in our cloud, bring-your-own-cloud, hybrid, air-gapped, edge, or shared SaaS.
Same code. Same API. Same audit trail. Different deployment boundaries for different security postures.
Your source control is in GitHub. Your container registry is in Harbor. Your ML tracking is in MLflow. Your CI is in Jenkins. Each has its own login, its own permissions, its own API, and its own audit trail. When a security auditor asks who pushed a specific image to production and which model version is running, the answer requires correlating logs across four systems that were never designed to talk to each other. The gaps between your tools are where security incidents hide.
Haephestus is one platform. Source control, registry, ML tracking, code search, and deployment share one identity, one protocol, one audit trail, and one deployment model. The person who pushes code, the person who pushes an image, and the person who promotes a model are the same identity with the same permissions under the same policy. When an auditor asks a question, the answer comes from one system — not from a cross-referencing exercise across four vendor consoles.
That is what Haephestus is. Not another tool to add to your toolchain. A replacement for the toolchain itself.
Operator relevance: A DevOps lead can reduce security surface and audit scope by consolidating source control, registry, ML, and deployment into one platform with one identity.