Internal Model Marketplace: Building a Self-Service AI Model Garden On-Premises

The Model Discovery Problem in Large Organizations

As enterprises scale their on-premises AI infrastructure, a pattern emerges that undermines efficiency: teams across the organization independently download, fine-tune, and deploy overlapping models without knowing what their peers have already built. The legal team fine-tunes a document classification model that the compliance team fine-tuned six months earlier. The customer service division evaluates five different summarization models while the engineering team has already benchmarked and deployed the best option for the same hardware.

This duplication wastes GPU compute on redundant fine-tuning, storage on duplicate model weights, and engineering time on evaluation work that has already been done. Worse, it creates governance blind spots — models deployed by individual teams may not have undergone the security review, bias testing, or compliance checks that the organization requires.

An internal model marketplace — sometimes called a model garden or model catalog — solves this by providing a single, searchable registry of approved models with rich metadata, evaluation results, and one-click deployment capabilities. It functions as an internal app store for AI models: teams browse what is available, read reviews and benchmarks from peers who have already used the model, and deploy with confidence that the model meets organizational standards.

Designing the Model Catalog Schema

The catalog is only as useful as the metadata it contains. A model entry that lists nothing more than a name and a download link provides no more value than a shared file server. The catalog schema must capture the information that teams need to make informed decisions about which model to use.

Each model entry should include a model card — a structured document that describes the model's intended use cases, known limitations, training data summary, and performance characteristics. The ML community has converged on model cards as the standard format for this information, and tools like Hugging Face's model card toolkit can generate them semi-automatically from training metadata.

Beyond the model card, include hardware requirements (minimum GPU memory, recommended GPU type, CPU and RAM requirements for loading), benchmark results on your organization's standard evaluation suite (not just the public benchmarks from the model's original release), and compatibility information (supported inference frameworks, quantization formats available, maximum context length tested on your hardware).

Add organizational metadata: which team owns the model, when it was last updated, its compliance review status, approved use cases, and any usage restrictions. A model approved for internal document summarization may not be approved for customer-facing chat applications — this distinction must be visible in the catalog, not buried in a policy document that nobody reads.

Tag models with a maturity level: experimental (available for evaluation only), validated (passed standard benchmarks and security review), and production (deployed and supported with SLAs). This prevents teams from accidentally building production systems on models that were uploaded for exploratory evaluation.

Curation and Governance Workflows

An ungoverned catalog quickly becomes a dumping ground. Establish clear workflows for how models enter the catalog, how they are maintained, and how they are retired.

The submission workflow should require a model card, benchmark results on at least one standard evaluation suite, a completed security scan (checking for serialization vulnerabilities, excessive permissions, and embedded code), and sign-off from the submitting team's lead. Automate as much of this as possible — the security scan and benchmark execution can run in a CI/CD pipeline triggered by model submission, with human review required only for the final approval.

Periodic review ensures that catalog entries remain accurate. Models that have not been updated in six months should be flagged for the owning team to either refresh or mark as deprecated. Models whose base model has received a security advisory should be automatically flagged for re-evaluation. Establish a quarterly review cadence where the platform team and model owners assess the catalog's health — how many models are actively used, how many are stale, and whether there are coverage gaps in key capability areas.

Deprecation and retirement must be handled gracefully. When a model is deprecated, teams currently using it should receive automated notifications with a migration timeline and recommended replacements. The catalog should continue to list deprecated models (clearly marked) so that teams can plan their migrations, but new deployments of deprecated models should be blocked.

Self-Service Deployment and Evaluation

The marketplace's value proposition is self-service — teams should be able to evaluate and deploy models without filing tickets with the platform team. This requires investing in automation that makes the deployment path frictionless while maintaining governance guardrails.

Provide a one-click evaluation environment that spins up a temporary inference endpoint for any catalog model. Teams can send test requests, measure latency on their specific input patterns, and compare multiple models side by side. This environment should have a fixed lifetime (e.g., 4 hours) and limited GPU allocation to prevent evaluation workloads from impacting production traffic. Most importantly, evaluation should not require any platform team involvement — a developer should be able to evaluate a model during a single afternoon without waiting for provisioning.

For production deployment, implement deployment templates — pre-configured infrastructure-as-code modules that encode your organization's deployment standards (resource limits, health checks, monitoring integration, autoscaling policies). A team deploying a model selects the appropriate template for their use case (low-latency single-request, batch processing, streaming), and the template handles the infrastructure details. This ensures that every deployment meets your operational standards while allowing teams to move quickly.

Integrate with your existing CI/CD platform so that model deployment follows the same workflow as application deployment: version controlled, peer reviewed, and automatically tested. A model deployment pull request should trigger automated tests that verify the model loads correctly, responds within latency SLAs on standard inputs, and passes a smoke test suite specific to its use case.

Usage Analytics and the Network Effect

The most successful internal marketplaces create a network effect — the more teams use the catalog, the more valuable it becomes for every team. Usage analytics are the engine that drives this effect.

Track and display adoption metrics for each model: how many teams have deployed it, how many inference requests it serves daily, and its trend over time. Models with high adoption and stable usage are strong candidates for additional platform investment (dedicated GPU allocation, priority support, custom fine-tuned variants). Models with declining usage may signal that a better alternative has emerged or that the use case has changed.

Enable user reviews and ratings where teams share their experience with a model — what worked well, what limitations they encountered, and any tips for optimal usage (prompt engineering strategies, preprocessing steps, configuration tuning). This institutional knowledge is often more valuable than formal benchmarks because it reflects real-world usage on your organization's data and infrastructure.

Publish a monthly catalog digest that highlights newly added models, popular models, and models that have been significantly updated. Distribute this through your organization's existing communication channels (Slack, email, internal blog). Visibility drives adoption, and adoption drives the contributions that keep the catalog valuable.

Technical Architecture for the Marketplace Platform

The marketplace itself is a software product that needs a deliberate architecture. At minimum, it consists of four components: a metadata store, a model registry, a deployment engine, and a web interface.

The metadata store holds model cards, benchmark results, reviews, and organizational metadata. A PostgreSQL database with a REST API serves this purpose well. Schema versioning is important — as you learn what metadata teams actually need, you will evolve the schema, and existing entries must be migrated gracefully.

The model registry stores the actual model artifacts — weight files, tokenizers, configuration files, and signatures. Your existing model registry (MLflow Model Registry, Harbor for OCI-formatted models, or a dedicated object store with versioning) can serve this role. The marketplace's metadata store references the registry by artifact URI and version, maintaining a clean separation between metadata and binary storage.

The deployment engine translates a deployment request into running infrastructure. Kubernetes operators like KServe or Seldon Core provide model-specific deployment primitives (predictive services, transformers, explainers) that abstract the complexity of GPU scheduling, health checking, and traffic management. The marketplace calls the deployment engine's API to provision, scale, and tear down model endpoints.

The web interface is where teams browse, search, evaluate, and deploy models. Invest in search quality — teams should find relevant models by capability description ("summarize legal documents under 10,000 words"), not just by model name. Faceted search on hardware requirements, maturity level, and use case domain makes the catalog navigable even as it grows to hundreds of entries.

Featured image by Tyler on Unsplash.