Don’t choose one AI. Govern all of them.
The best model changes every few months. Your operating model shouldn’t.
Qua is one persistent layer for context, permissions, collaboration, routing, approvals, provenance and economics — across every model you use.
Capability by capability.
Six families, four states, no red crosses. Tap a state to highlight it.
22 of 22 capabilities shown
| Capability | Qua | Cursor | OpenRouter | ChatGPT / OpenAI | Claude / Anthropic | Gemini Enterprise | Open models / DIY |
|---|---|---|---|---|---|---|---|
| ModelsWhich intelligence you can use. | |||||||
| Core | Core | Core | Supported | Supported | Supported | Adjacent | |
| Core | Supported | Core | Adjacent | Adjacent | Supported | Core | |
| Core | Supported | Supported | Adjacent | Adjacent | Supported | Core | |
| Context & continuityWhat survives a model change. | |||||||
| Core | Adjacent | Adjacent | Supported | Supported | Supported | Adjacent | |
| Core | Verify | Verify | Verify | Verify | Verify | Adjacent | |
| Core | Adjacent | Adjacent | Verify | Verify | Verify | Adjacent | |
| Core | Verify | Supported | Verify | Verify | Verify | Adjacent | |
| GovernanceWho is allowed to do what. | |||||||
| Core | Supported | Adjacent | Supported | Supported | Supported | Adjacent | |
| Core | Verify | Adjacent | Verify | Verify | Verify | Adjacent | |
| Core | Supported | Supported | Supported | Supported | Supported | Adjacent | |
| Core | Supported | Adjacent | Core | Supported | Core | Adjacent | |
| Core | Core | Supported | Core | Core | Core | Adjacent | |
| Core | Core | Supported | Core | Core | Core | Adjacent | |
| EconomicsWhat the work cost and returned. | |||||||
| Core | Verify | Adjacent | Verify | Verify | Verify | Adjacent | |
| Core | Supported | Supported | Verify | Verify | Supported | Adjacent | |
| DeploymentWhere execution is allowed to run. | |||||||
| Core | Core | Core | Core | Core | Core | Supported | |
| Verify | Verify | Verify | Verify | Verify | Supported | Core | |
| Verify | Verify | Verify | Verify | Verify | Verify | Core | |
| Verify | Verify | Verify | Verify | Verify | Verify | Core | |
| Agents & interopHow agents plug in. | |||||||
| Core | Core | Adjacent | Core | Core | Core | Adjacent | |
| Supported | Core | Adjacent | Core | Core | Core | Adjacent | |
| Verify | Verify | Adjacent | Verify | Verify | Core | Adjacent | |
ModelsWhich intelligence you can use.
Context & continuityWhat survives a model change.
GovernanceWho is allowed to do what.
EconomicsWhat the work cost and returned.
DeploymentWhere execution is allowed to run.
Agents & interopHow agents plug in.
Comparison reflects publicly documented product capabilities and Qua's analysis. Competitor products evolve rapidly. Last reviewed: August 2026.
Pick a platform. See the structural difference.
Every line is drawn from public product documentation. Where a vendor’s status is ambiguous, we say so.
Cursor makes developers dramatically more productive. Qua governs AI work across the enterprise.
Cursor is a strong product with sophisticated enterprise security, model and agent controls, MCP governance, analytics and access to multiple frontier models. The distinction is structural, not a feature gap.
Use Cursor to build software. Use Qua to govern enterprise intelligence across software, finance, operations, research, sales, legal, agents and future AI systems.
Models keep improving. The enterprise problem keeps moving up the stack.
When intelligence becomes interchangeable, one question is left: who controls the work?
Models are commoditizing
The leading model today may not be the leading model six months from now.
Agents are multiplying
Enterprises will operate fleets of specialized agents across departments, applications and providers.
Context is becoming infrastructure
Enterprise knowledge, task state, permissions and institutional memory increasingly matter as much as raw model intelligence.
Economics move from tokens to outcomes
Cheap tokens do not guarantee cheap workflows. Multi-step agentic execution can dramatically increase total inference consumption.
Qua is built for that world — not for one where a single chatbot, model or cloud wins.
Not all AI platforms solve the same problem.
Qua doesn’t need the layers below it to lose.Qua gets more valuable as they improve.
Open models aren’t a threat to Qua. They are part of the reason Qua exists.
Open-weight models are improving rapidly in reasoning, coding, multimodality and agentic capability. Enterprises increasingly want the option to self-host, fine-tune, keep sensitive workloads private, avoid provider lock-in, reduce inference cost, meet sovereignty requirements and use specialized domain models.
DeepSeek
Strong open reasoning and coding lineage, deployable under your own control.
Kimi / Moonshot
Long-context open-weight capability suited to document-heavy internal work.
Gemma / Google
Gemini's open-weight family — the default self-hosting baseline for many regulated enterprises.
Other open weights
Domain and sovereign models an enterprise fine-tunes for itself.
Choose the model.Keep the operating system.
Model routing asks which model should answer. Qua asks first whether a model needs to answer at all.
The Answer WaterfallQua's resolution order: personal knowledge, then enterprise search, then models — cheapest trustworthy resolution first, with the reason each tier passed or failed recorded. resolves at the cheapest trustworthy tier and records why each tier passed or failed.
The cheapest model call is sometimes no model call.Qua optimizes the resolution path — not merely the provider.
Modeled economics · not a universal customer resultStart with one model. Finish with another. Never restart the work.
Session LedgerThe model-independent record of a Session — task state, authorized context, sources, corrections, model path, agent actions, human interventions, approvals, policy decisions, cost and outcome.
Persistent underneath every handoff.
- Task state
- Authorized context
- Sources
- Corrections
- Model path
- Agent actions
- Human interventions
- Approvals
- Policy decisions
- Cost
- Outcome
The innovation is not model switching.It is preserving the governed state of the work while intelligence changes underneath it.
Copying the whole conversation isn’t portability.
Moving a full conversation from one provider to another may also move information that the receiving model, user, agent or infrastructure boundary should never receive. Qua treats context as governed state.
Move the work — not everything anyone has ever said during the work.
Enterprise work isn’t single-player. Enterprise AI shouldn’t be either.
Most AI began as one human talking to one model. Enterprise work runs on corrections, review, approvals, escalations and hand-offs. Qua treats those actions as part of the AI execution itself, inside Agent SessionQua's unit of work: one governed task that humans and agents share, with its own state, permissions, model path and receipt..
Share the work, not just the transcript.
Token logs tell you what AI consumed. Qua tells you what the enterprise received.
An Outcome ReceiptWhat an accepted piece of work cost and how it was produced: resolution path, premium baseline, actual cost, policy, approver and net saving. connects cost, model path, context handoff, sources, policy, human approval, acceptance, reuse and saving into one record — attached to a result somebody accepted, not to a request somebody sent.
Unit economics belong at the outcome.Not merely at the token.
Illustrative receipt · modeled figuresToday’s best model is temporary. Your enterprise architecture shouldn’t be.
2024
- One dominant chatbot
2025
- Multiple frontier models
Now · 2026
- Frontier models
- Open models
- Agents
- Routers
What comes next
- Specialized models
- Private enterprise models
- Long-running agents
- Agent swarms
- Multimodal agents
- On-device models
- Sovereign and industry models
- Machine-to-machine agents
Models will change. Providers will change. Agents will change. Protocols will change. Infrastructure will change.The enterprise still needs identity, context, policy, provenance, economics and accountability.
What Qua is, and what it isn’t.
Qua is
- An enterprise AI execution layer
- A multi-player AI workspace
- A model-neutral orchestration layer
- A policy-aware context layer
- A runtime governance layer
- An economic control plane
- A sovereign AI execution architecture
Qua is not
- A foundation model
- A model marketplace
- Only a model router
- Only enterprise search
- Only RAG
- Only an agent builder
- Only an AI chatbot
- Only a developer IDE
Qua doesn’t need to win the model race.Qua is designed to govern whoever wins it.
Intelligence is becoming abundant. Governed execution is not.
AI models
Who has the smartest model?
AI applications
Who has the best AI product?
AI orchestration
Who controls how models, agents, people and enterprise context execute work together?
The enterprise orchestration layer becomes more important — not less — as models commoditize.
One architecture. Different reasons to care.
Reduce AI sprawl and model lock-in.
Keep one architecture while the model landscape changes.
Control what context crosses every AI boundary.
Know what accepted AI work actually costs and saves.
Preserve provenance, approvals and accountability.
Use the best available intelligence without rebuilding the workflow.
Qua vs the rest, answered plainly.
Qua vs Cursor — what is the difference?
Cursor is an AI coding environment: it is exceptional at writing, refactoring and reviewing code inside the IDE. Qua is not an IDE. Qua is the enterprise execution layer that keeps a task's context, permissions, approvals, provenance and economics intact across every model, agent and team member — including work that never touches a repository.
Qua vs OpenRouter — is Qua just a model router?
OpenRouter's abstraction is the request: pick a model, forward the call, bill the tokens. Qua's abstraction is the Session: enterprise knowledge, policy, human approvals, cross-provider provenance and accepted-outcome receipts persist while the model underneath changes. Routing is one function inside that, not the product.
Qua vs ChatGPT Enterprise, Claude Enterprise or Gemini Enterprise?
Frontier platforms are excellent, and they are built around their own models. Qua is vendor-neutral by design: the same governance, context and measurement apply whether the work resolves on GPT, Claude, Gemini, an open-weight model or a customer-hosted private model.
Does Qua replace our existing AI vendors?
No. Qua sits above them. Existing model contracts, enterprise search and agent investments keep running; Qua governs how they are used and reports what the work actually cost and produced.
Can Qua run inside our own perimeter?
Qua is designed to present the same operating model across cloud, customer VPC, on-prem and air-gapped deployments. Deployment architectures should be confirmed with the Qua team for your specific environment.
How does Qua reduce enterprise AI cost?
The Answer Waterfall resolves each request at the lowest capable tier — personal knowledge and enterprise search first, fast models next, premium models only when the expected value clears an economic gate. Every response carries a receipt showing the actual cost against a premium baseline.
Don’t standardize your enterprise on one model. Standardize how every model is governed.
Bring your frontier models. Bring your open models. Bring your enterprise knowledge. Bring your agents. Bring your private infrastructure. Qua gives the work one place to continue.
Quantify your AI usage, unify your team, amplify your savings — every outcome measured, every team connected, every model yours.
Competitive landscape last reviewed: August 2026