| Primary category | A finance-specific operating environment spanning financial analysis, investment intelligence, quantitative models, Knowledge, Agents, workflows and governed Financial Actions. | Collaborative planning, financial modelling, budgeting, scenarios and business decision simulation. |
| Data & evidence | Workspace evidence, structured financial data, Knowledge sources, integrations and exact citation/evidence lineage where supported. | Connected planning and operational data organized into shared business models and finance workflows. |
| AI architecture | Auto — FinanceGPT Native prefers deterministic finance, bounded Native NLP and Semantic Intelligence before eligible language generation; Managed AI and BYOK remain governed escalation options. | AI-assisted planning and explanations within an FP&A environment. |
| Quantitative computation | Dedicated deterministic and statistical quantitative methods, including LQMs, remain separate from language generation. | FinanceGPT additionally provides dedicated quantitative/LQM engines for broader risk, valuation, investment and simulation workflows. |
| Governed financial action | A dedicated Financial Actions control plane governs authority, deterministic monetary provenance, screening, risk, approvals, credential-isolated execution handoff, evidence and reconciliation. | Runway focuses on planning and business decision simulation; FinanceGPT adds separately governed financial execution and reconciliation where configured. |
| Governance emphasis | Workspace policy, evidence lineage, model routing, approval boundaries, execution controls, reconciliation and audit evidence are designed as distinct control layers. | Planning collaboration, change tracking and auditability are part of Runway’s workflow design. |
| Best fit | Enterprises that want analysis, quantitative finance, evidence and governed action in one finance platform. | High-growth and planning-focused teams that want collaborative FP&A and rapid scenario modelling. |