Weights scattered across S3 buckets, laptops, and forgotten repos? Model gives every model on your team a versioned, documented, discoverable address — and keeps it there.
The numbers behind production-grade model management
The first model lives in a home directory. The second in a shared bucket with a README nobody updates. By the sixth, weights are scattered across so many places that a simple compliance question — which models are in use? — has no clean answer.
Model treats weights as first-class objects — not generic blobs. Every feature is shaped by what teams actually need to know about a model.
Every model and version gets a permanent URL and a canonical pull command. The path from finding a model to running it is short by design.
Versions are content-addressed and signed. The model running in production today is provably the same one reviewed last quarter — no silent swaps.
Fine-tunes point back to base models. Quantized versions point to their originals. The whole ancestry is browsable and queryable — no archaeology required.
Built around the realities of how ML teams actually produce, deploy, and govern AI models at scale.
First-class documentation for every model. Purpose, limitations, training data, metrics, and license — authored in markdown, rendered cleanly in the dashboard.
GOVERNANCETwo fine-tunes that share 90% of their weights don't pay for that storage twice. Content-addressed deduplication runs automatically across all versions.
STORAGESearch by name, task, license, framework, or free-form text. Public and private models in one catalog, with permissions clearly drawn between them.
DISCOVERYContent served from a distribution layer close to major cloud regions. Pulling a 50 GB checkpoint takes minutes — not the better part of an hour.
PERFORMANCEService accounts scoped to specific models and actions. The API key your inference cluster uses can't accidentally publish a new version it's only supposed to read.
SECURITYBenchmark results flow back from connected evaluation platforms and attach to the version that produced them — enriching metadata without duplicating it.
EVALSEvery read and every write captured. The trail of who used which model when is always available — ready for compliance review without extra tooling.
COMPLIANCEA successful training run publishes itself. A fine-tune that finishes pushes its version automatically with lineage, metadata, and metrics already attached.
AUTOMATIONSame API, same SDKs, same dashboard — running entirely on your infrastructure. Full data residency for environments where it's non-negotiable.
ENTERPRISEIf you've used a code host or package registry, Model will feel immediately familiar — because the ergonomic problems have already been solved well by source control.
Give it a name, owner, and license. A model card is generated automatically from the initial metadata.
Push weights, tokenizer, config, and evaluation results in a single command. Each push is content-addressed and returns a stable identifier.
Fine-tune, quantize, merge — each result is a new immutable version with lineage automatically recorded. Old versions are never modified.
Use the stable model identifier from anywhere in your stack — training code, inference servers, evaluation harnesses.
Files already cached locally aren't redownloaded. CDN acceleration serves the rest from the closest available region.
Inference platforms that integrate directly fetch weights once on boot — no extra configuration on the application side.
Lineage is captured when one model is derived from another — no one has to remember to write it down.
The full ancestry graph is browsable in the dashboard and queryable through the API — base models, fine-tunes, quantizations, and merges.
Which production models descend from a given base? Which fine-tunes share a common ancestor? What changed between last quarter's model and today's?
Private models visible only to their owner org. Fine-grained controls decide who can read, write, and publish within that org.
Inference cluster keys read only. Training pipeline keys can write but not delete. Each service account is scoped to exactly what it needs.
Require approval before a new version is promoted to production. Enterprises can enforce this for every model that touches customer data.
Every feature is designed around what teams actually ask about a model — not what you'd ask about a generic blob. That orientation shows up everywhere, from search to model cards.
Content-addressed, signed versions. Lineage from base models to fine-tunes. Evaluation results attached to the exact version that earned them. The answer to what's in production is always trustworthy.
Public community models alongside private fine-tunes, in the same catalog. Pulling a model out and using it elsewhere is one command. No lock-in, because there's no need for one.
Storage deduplication, CDN-served downloads, and audit logs that cover every read and write — the parts most teams notice only when they break, built right from the start.
Public · MIT · 14.2 GB · Meta
Private · run_20240601 · F1: 0.82
Private · run_20241102 · F1: 0.91 · PROD
Private · INT4 · 3.6 GB · serving-only
From research labs to enterprise compliance teams, Model scales with the complexity of your model lifecycle.
Canonical home for the artifacts of research work. Public visibility for the parts worth sharing, private visibility for everything else. Every checkpoint from every experiment has a stable address — six months later, reproduction is one pull command away.
Source of truth for inference and training infrastructure. Replace the tangle of S3 buckets and ad-hoc scripts with a single registry that feeds both. Service accounts scoped exactly to what each system needs — nothing more.
Enforce review and approval workflows for every model that touches customer data. Audit trails that hold up to regulatory review. Know exactly which models are in use, who approved them, and when — without archaeology.
Every fine-tune produces a new version with a clear pointer to its base model, attached metrics, and an automatic record of the training run. Six months later, when someone asks why the production model behaves the way it does, the answer is in the registry — not buried in Slack threads.
Clean HTTP API, Git-like CLI, and SDKs in every language your team uses. Integrates with the wider AI tooling landscape rather than competing with it.
The operational features that make a registry safe to run inside a real enterprise — present from day one, not bolted on later.
Every version is cryptographically signed and content-addressed. The model in production is provably the one that was reviewed.
Every read and write logged. Who pulled which model, when, from where. Exportable to your SIEM or compliance platform.
Per-model, per-action service accounts. Inference keys can't publish. Training keys can't delete. Least-privilege by default.
Require review before any model version is promoted to production. Enforced at the registry level — not an honor system.
Same API, same SDKs, same dashboard on your infrastructure. Docker, Kubernetes, and air-gapped deployments supported.
License metadata is structured and queryable. Know the license of every model in use — and block pulls of ones that don't comply.
We went from a compliance team question we couldn't answer — "which models are in production?" — to having that be a one-API-call question. Model paid for itself in the first week.
The lineage graph is what sold us. When a support fine-tune started behaving strangely, we traced it back to a base model checkpoint with a data issue in under ten minutes. Previously that would have taken days.
Deduplication alone made the switch worthwhile. We had six fine-tunes of the same base model stored separately. Model collapsed that to a single base plus diffs. Storage bill dropped immediately.
Weights are stored as content-addressed objects using a rolling hash of the file contents. Two versions that share blocks — even if the files have different names — automatically share the underlying storage. A fine-tune that modifies 10% of a model's weights stores only that 10% as new data. Deduplication runs automatically at push time with no configuration needed.
Yes. Model is designed to integrate with the wider ecosystem rather than replace it. Public models from HuggingFace can be mirrored into your Model registry, and the CLI and SDK support pulling directly from HuggingFace identifiers. Teams typically use HuggingFace for community model discovery and Model for managing their own private fine-tunes and production artifacts.
Versions are immutable once published — they cannot be silently modified. You can archive a version, which removes it from the default search surface while preserving its artifacts and audit trail. Hard deletion is available for administrators on Enterprise plans and requires a two-person approval workflow by default. This is deliberate: a registry where versions can disappear without a trace is not a reliable source of truth.
When you push a fine-tune, you specify the base model identifier in the push command (or it's inferred automatically by connected training platforms). Model records this pointer as part of the version metadata and builds the lineage graph incrementally as new versions are pushed. Lineage is queryable through the API and browsable through the dashboard — you can traverse the full ancestry of any model and find all descendants of any base.
Yes. The self-hosted edition exposes the same API surface as the hosted service and works with the same SDKs, CLI, and dashboard. Weights never leave your infrastructure. Supported deployment targets include Docker, Kubernetes, and fully air-gapped environments. Self-hosted is available on Enterprise plans with deployment support included. The same deduplication, lineage, and access control features are available in both editions.
Downloads are served from a content-distribution layer placed close to the major cloud regions (AWS, GCP, Azure, and their equivalents). A 50 GB checkpoint typically pulls in 3–8 minutes from within the same cloud region, and 15–25 minutes cross-region. The client is content-addressed, so files already cached locally are never re-downloaded. Enterprise plans can add reserved capacity for predictable latency on inference cluster boot.
Create an account, push your first model, and have a stable identifier in minutes. No configuration required to get started.