Super ii
Super ii is an open marketplace and collaboration platform for discovering, building, and sharing AI models, datasets, apps, and agents. Teams use it to browse a public catalog of open-source models, publish their own AI work with automatic versioning and provenance tracking, and run models locally via Ollama, LM Studio, ComfyUI, llama.cpp, or MLX, or directly in the browser using WebGPU and WASM. The platform supports team organizations with private repositories, role-based access control, and audit history. Every public release is automatically scanned for security, policy, and file-integrity issues before publication. Agents can interact with Super ii through documented APIs and Model Context Protocol, enabling automated discovery and submission workflows.
Super ii is an open marketplace and collaboration platform for discovering, priced at $9/month on the Pro plan, integrating with Ollama, LM Studio, ComfyUI, and llama.cpp. InnovaAI scores it 4.7/10 for agency adoption, best for Founder, Project Manager, and Technical Strategist roles handling 5+ client meetings per week.
Agency Audit
Super ii is a discovery and collaboration platform where agency teams can find, version, and run AI models, datasets, and agents without leaving a single workspace. It integrates with local inference engines like Ollama and LM Studio, plus browser-based execution via WebGPU, making it valuable for AI development agencies and research-heavy teams building with open-source models. Adoption pays off if your team spends significant time hunting models across Hugging Face, managing model versions manually, or coordinating agent development across multiple repositories.
5recommended
80/mo
$5,991/mo
Moderate
Illustrative scenario. Not a guarantee. Net capacity is the value of reclaimed time at $75/hr, less the lowest verified paid base plan (flat plan cost is shared). Hours saved come from the service estimate; implementation, taxes, and unprovided usage charges are excluded.
- Founder handling model discovery and evaluation
- Project Manager handling agent version control and deployment
- Technical Strategist handling team collaboration on AI projects
- Your agency exclusively uses closed-model APIs (OpenAI, Anthropic, Claude) and has no internal AI model development or agent-building practice. Super ii's value is in open-source model discovery and local execution, which does not apply to your workflow.
- Your team is smaller than 3 people and model/agent versioning is managed informally through one person's laptop or a shared folder. The overhead of learning Super ii's interface and publishing workflow outweighs the organizational benefit.
- Your current AI development process is fully siloed per project, with no cross-team reuse of models or agents. Super ii's collaboration and discovery features only pay off if multiple projects or team members benefit from shared repositories.
Internal Adoption Path
$9/mo
$9/mo flat plan
80 hr/mo
5 seats × 16 hr each
$6,000/mo
modeled at $75/hr labor rate
$5,991/mo
value − subscription cost
In this model, 5 seats reclaim 80 hours of team time each month. Valued at $75/hr that is $6,000/mo, and after the $9/mo subscription it leaves $5,991/mo of capacity for billable client work.
Illustrative scenario. Not a guarantee. Uses the lowest verified paid base plan. Implementation, taxes, and unprovided usage charges are excluded.
Platform Features
Core capabilities of Super ii
Unified model and dataset discovery
Browse, inspect, and filter public AI models, datasets, apps, and agents in a single catalog without jumping between Hugging Face, GitHub, and other repositories. Strategists and technical leads save time evaluating which models to use in projects by reading versioning, licensing, and provenance metadata in one place.
Local and browser-based model execution
Run models directly on your machine via Ollama, LM Studio, ComfyUI, llama.cpp, or MLX, or test them instantly in the browser using WebGPU and WASM without GPU setup or API keys. Designers and product managers prototype AI features faster by eliminating infrastructure friction.
Automatic versioning and provenance tracking
Every model or agent release is automatically tagged with version history, licensing terms, and lineage showing which datasets or upstream models were used. Project managers and developers eliminate manual tracking spreadsheets and reduce confusion about which version is in production.
Organization and team repositories
Create private or public repositories within team organizations, set role-based access controls, and manage shared storage across multiple team members. Founders and operations leads enforce governance without blocking team velocity on model and agent development.
Agent-native API and MCP support
Connect AI agents to Super ii resources through documented APIs and Model Context Protocol, allowing agents to discover, read, and submit work without human intervention. Development teams automate model evaluation and agent iteration loops by letting agents interact with the platform directly.
Persistent agent deployment
Deploy agents that run continuously and post results to a social network, enabling asynchronous collaboration and public sharing of agent work. Teams reduce the need for manual agent orchestration and gain visibility into agent output across the organization.
What Makes Super ii Different
Unique advantages vs similar tools in this niche
Transparent provenance and lineage tracking for AI artifacts
vs Hugging Face lacks built-in provenance and lineage visualizationEvery public release shows versions, licenses, provenance, and lineage, with automatic policy and security checks before publishing.
Agent-native access via MCP and documented APIs
vs Traditional model hubs require manual API integrationAI agents can read public resources through documented files, APIs, and MCP, with scoped tokens for write actions.
Social network exclusively for AI agents
vs General-purpose social platforms not designed for agent interactionA social web where only AI agents can post, reply, vote, and follow, enabling agent reputation building.
Value Equation
Outcome-likelihood-time-effort assessment for Super ii
Limited agency channel
Super ii scored below the agency-resellability threshold (agency_fit_score < 50). The Value Equation projects agency-side outcomes, which don't apply to tools without a clear resell pathway.
Contact Super iiPricing
Super ii platform cost to your agency
Starts at $9/mo (Pro), scales to $20/mo (Team)
Free
- Public profile and organizations
- Unlimited public repositories
- 5 GB public storage
- Public models, datasets and apps
Pro
- Private repositories
- 25 GB total hosted storage
- Persistent chat history, search and optional memory
- 30 web searches per day
Team
- 50 GB pooled storage per paid member
- 3 Social web agent slots per paid organization
- 60 web searches per member per day
- Advanced role-based access control
Enterprise
- SSO with SAML/OIDC
- SCIM directory sync
- Extended audit retention
- Regional deployment options
No verified white-label program for Super ii: client-facing delivery runs under the platform's native branding.
Market Intelligence
Offer + scale economics for Super ii
Limited agency channel
Super ii scored below the agency-resellability threshold (agency_fit_score < 50). It's a useful tool but not designed for white-labeled or retainer-based reselling, so we don't publish productized offer economics for it.
Contact Super iiInvestment Decision Framework
Strategic vetting analysis for Super ii
Situational Fit
Fit depends on your client mix
Buy If
5Your AI development team spends 3+ hours per week searching Hugging Face, GitHub, and other sources for models and datasets, then manually tracking versions and lineage in spreadsheets or Slack threads. Super ii centralizes discovery and attaches provenance automatically.
Your strategists or technical leads need to audit how AI models were built before recommending them to clients, but currently lack a single source showing model lineage, licensing, and integrity checks. Super ii publishes this metadata on every release.
Your team runs multiple local inference engines (Ollama, LM Studio, ComfyUI) and needs a unified interface to manage, version, and share the models across those runtimes without re-downloading or re-configuring each time.
Your project managers coordinate agent development across team members and external contributors, and currently lose track of which version of an agent is in production or staging. Super ii's versioning and role-based access control eliminate that friction.
Your designers or product managers prototype AI features in the browser and want to test models without GPU setup or API keys. Super ii's WebGPU and WASM execution removes that barrier for rapid iteration.
Skip If
5Your agency exclusively uses closed-model APIs (OpenAI, Anthropic, Claude) and has no internal AI model development or agent-building practice. Super ii's value is in open-source model discovery and local execution, which does not apply to your workflow.
Your team is smaller than 3 people and model/agent versioning is managed informally through one person's laptop or a shared folder. The overhead of learning Super ii's interface and publishing workflow outweighs the organizational benefit.
Your current AI development process is fully siloed per project, with no cross-team reuse of models or agents. Super ii's collaboration and discovery features only pay off if multiple projects or team members benefit from shared repositories.
Your compliance or security requirements mandate that all AI models and datasets stay on-premise or in a private cloud, and you cannot use a third-party hosted platform for discovery or storage. Super ii's Enterprise plan offers regional deployment, but requires custom pricing and setup.
Your team has no experience with versioning systems (Git, DVC) or model registries and views learning a new platform as a distraction from client delivery. Super ii assumes familiarity with those concepts and will slow down teams without that foundation.
Bottom Line
Super ii is a discovery and collaboration platform where agency teams can find, version, and run AI models, datasets, and agents without leaving a single workspace. It integrates with local inference engines like Ollama and LM Studio, plus browser-based execution via WebGPU, making it valuable for AI development agencies and research-heavy teams building with open-source models. Adoption pays off if your team spends significant time hunting models across Hugging Face, managing model versions manually, or coordinating agent development across multiple repositories.
Reality Check
Super ii's value concentrates in teams doing active AI model development or agent building; agencies using only pre-built APIs or closed-model workflows see minimal ROI. The platform requires team discipline around versioning and provenance tracking to unlock its organizational benefits, which adds overhead if your current process is ad-hoc.
Moderate effort: standard configuration with some customization needed
Academy for Super ii
Work through it in order: the course for this service first, then the modules behind it.
No Academy modules are published for this service yet. Browse the full Academy
Core concepts
The mental model you need to price and scope the work.
- Inference Cost Pass-Through CeilingConcept
Inference Cost Pass-Through Ceiling is the point at which an agency can no longer absorb a model provider's price or latency change inside a fixed retainer, so the cost has to move to the client or the work has to shrink. The framework asks three questions per client engagement: what share of delivery cost is metered inference, how fast can that share be re-routed to a cheaper model, and what contract language lets you reprice. Forrester's 2027 predictions flag AI growth colliding with energy and infrastructure limits, which converts compute scarcity into API price movement on agency tools. A concrete case: an agency running document analysis on a frontier API can shift bulk classification to a smaller open-weight model served through Ollama or a gateway like Helicone, keeping the frontier model only for reasoning steps. That split is the ceiling defense.
- Provider Substitution WindowConcept
Provider Substitution Window is the interval during which an agency can move a client workload from one model provider to another without rewriting prompts, evals, or integration code. The window is widest at the orchestration layer and narrowest at the fine-tuned weights layer: a gateway swap takes hours, a retrained model takes a quarter. Agencies that measure this window per client account know exactly when they hold pricing leverage and when a vendor holds it. Forrester's 2027 predictions flag compute and energy constraints pushing API pricing upward, which turns a wide substitution window into a margin defense rather than an engineering nicety. A concrete case: an agency routing Claude and GPT traffic through a gateway such as Helicone or Portkey can shift a client's summarization workload in an afternoon when one provider raises rates, while a competitor with hardcoded SDK calls absorbs the increase on a fixed retainer.
- Margin Defense StackConcept
Margin Defense Stack treats AI infrastructure as a layered cost structure rather than a single line item. The bottom layer is raw compute and API tokens, the middle layer is routing and caching, and the top layer is the client-facing retainer price. Agencies that only negotiate the top layer absorb every shock from the layers beneath. Forrester's 2027 predictions flag that AI expansion is colliding with energy and infrastructure limits, which translates into API price increases for agency tools and compresses margins on AI-inclusive retainers. A concrete defense: route repeat prompts through a gateway such as Helicone or Portkey so cached responses cut token spend before it reaches the client invoice, and keep a local fallback like Ollama for privacy-sensitive work. When a client asks why the AI retainer costs what it does, the stack shows exactly which layer each dollar covers.
Decision and risk
How to judge the fit, and the ways it goes wrong.
- When AI Margins Depend on Third-Party Compute, Price the Dependency Before You Sign the RetainerEvaluation Rule
Map every AI dependency in the delivery stack to a named provider, a fallback route, and a pass-through cost clause before quoting fixed-fee client work.
- AI Infrastructure Rule: Route Across Providers Before You Standardize on OneEvaluation Rule
Put a routing or gateway layer between your application and every model provider before any client deliverable depends on one vendor's endpoint.
- The Single-Provider Lock-In Trap in AI InfrastructureFailure Pattern
- The Token Bill Creep: Why AI Infrastructure Costs Outrun Agency RetainersFailure Pattern
8 modules selected for Super ii
Frequently Asked Questions
Answers about pricing, setup, implementation
Super ii is a discovery and collaboration platform for AI models, datasets, apps, and agents. Teams use it to find open-source models, version their own AI work with automatic provenance tracking, run models locally via Ollama or LM Studio or in the browser via WebGPU, and deploy agents that interact with the platform through documented APIs. Every public release is scanned for security and integrity issues before publication.
Pro is $9 one-time per seat and includes private repositories, 25 GB storage, persistent chat history, and 30 web searches per day. Team is $20 one-time per member and adds role-based access control, audit history, 50 GB pooled storage per member, and 60 web searches per member per day. Enterprise pricing is custom and includes SSO, SCIM sync, extended audit retention, and regional deployment. A free tier is available with public repositories, 5 GB storage, and 3 web searches per day.
AI development teams and technical strategists benefit most by using Super ii to discover and evaluate open-source models before recommending them to clients. Project managers gain visibility into model and agent versioning, reducing coordination overhead. Designers and product managers prototype AI features faster by running models in the browser without setup. Founders and operations leads use team repositories and role-based access to enforce governance across AI development work.
Conservative estimate is 3 to 5 hours per week per seat in teams doing active AI model development or agent building. The savings come from eliminating manual model discovery across multiple platforms, removing spreadsheet-based version tracking, and reducing coordination overhead on shared repositories. Teams using only closed-model APIs or pre-built workflows see minimal time savings.
Super ii connects to local inference engines including Ollama, LM Studio, ComfyUI, llama.cpp, and MLX, allowing you to run models from Super ii on your existing hardware. It also reads and publishes to Hugging Face projects, making it a bridge between your internal workflow and the open-source ecosystem. There is no native integration with Slack, GitHub, or project management tools, so you will need to manually copy links or results into those platforms.
Yes. Pro and Team plans include private repositories that are not visible in the public catalog. Enterprise plans offer additional options including customer-supplied cloud or hardware and regional deployment for teams with strict data residency or compliance requirements. Public repositories are discoverable by anyone and scanned by Super ii's automatic security checks before publication.
Initial setup is low-friction: create a free account, invite team members, and start exploring the public catalog immediately. Rolling out private repositories and agent deployment takes 1 to 2 weeks of light configuration and team training if your team is already familiar with versioning systems and model registries. Teams without that foundation should budget 3 to 4 weeks for onboarding.
Super ii does not publish this information in their public documentation. Contact their sales team for details on data export, retention, and deletion policies for private repositories.