AI workflows are now business-critical  and regulators, auditors, and CFOs all want answers the execution layer can't give them. Vharta is the governed execution platform: run Airflow, Langflow, and agentic AI workloads through one set of policy gates, one audit trail, and one cost meter.

Governance suites can tell you the rules but don't run the work. Orchestrators run the work but weren't built for AI-era evidence. Agent frameworks govern only their own graphs. Cloud copilots lock your data into someone else's region. Vharta sits at the intersection none of them own: governed, multi-engine execution.

What that means in practice:

▸ Policy-as-Code on every run: OPA Rego gates evaluate tenant, budget, and engine permissions in milliseconds, fail-closed, with an immutable audit event on every decision. No workflow path bypasses approval or metering.

▸ Compliance evidence by default: SOC2 Type II, HIPAA, and GDPR controls built in, with audit-ready trails designed for the obligations phasing in under the EU AI Act. Customers cut audit prep from six weeks to two days.

▸ Per-run cost attribution: >99% accuracy across every DAG run, flow, and LLM call, attributed to tenant and cost center. Your finance team sees exactly what every workflow costs.

▸ Sovereign by design: BYOK across Anthropic, OpenAI, Google, Bedrock, and self-hosted models, with policy-driven routing that keeps regulated data where your rules require. No vendor lock-in. 

Multi-tenant isolation proven across 21 production tests. Tenant onboarding in under 10 minutes. Migration paths from DataStax Langflow, Azure Managed Airflow, and Dagster.

One platform. Three engines. Full governance. 

Vharta.ai

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