Kanawai AI

Kanawai AI is an enterprise control layer for discovering, costing and governing an organization’s AI use. It is aimed at CFOs, FinOps and platform leaders, CAIOs, and governance teams rather than individual users. Its strongest case is bringing inventory, financial accountability and enforceable policy into one deployment, provided the organization can maintain reliable identity mapping.

Overview

Kanawai continuously finds AI tools, plugins and model endpoints, including unsanctioned use, then combines that inventory with consumption reporting and governance controls. The result is an organization-wide view instead of separate lists maintained by finance, security and engineering. That breadth suits companies managing many providers and departments; it is unnecessary for a small team seeking a single AI application.

Key features

Inventory and cost visibility

AI inventory is continuously refreshed, covering sanctioned and unsanctioned tools as well as model endpoints. Token consumption and cost can be viewed by model, person and department across connected providers. Attribution extends to users, teams and workloads, while the platform recommends cheaper models when they can produce the same outcome. This gives finance and platform teams a practical way to identify concentration and waste, but the AI FinOps pilot’s attribution depends on identity mapping. Recommendations for model routing and right-sizing do not automatically change production behavior; Kanawai makes those changes only when the customer requests them.

Governance and compliance

Kanawai converts regulatory frameworks and internal policies into machine-enforceable rules. Continuous evaluation, containment, evidence generation and escalation are designed for ongoing control rather than a one-time assessment. Its governance materials cover NIST AI RMF, NIST CSF 2.0, HIPAA, GDPR and CCPA. Integrations listed for governance include Palo Alto Networks, Cisco, Forescout, Okta, Microsoft, Google, Ping Identity and Armadin, while the AI system inventory plan also lists Anthropic Claude, AWS, Microsoft Azure, Cloudflare, Microsoft 365, OpenAI Codex, Salesforce, Slack, MuleSoft, Apigee and Google Cloud Application Integration.

Deployment and audit trail

The service operates at the application layer through scoped OAuth 2.0, SAML 2.0 or OIDC flows. It requires no endpoint agents and no changes to cloud environments, which can reduce rollout friction for organizations with strict infrastructure controls. Governance actions produce cryptographically signed, tamper-evident artifacts with trusted timestamps and chain-of-custody logs, giving audit and security teams evidence they can preserve. Deployments can be isolated in the United States, Europe or Asia. The Trust Center lists HIPAA, SOC 1 Type 1 and SOC 2 Type 2 and provides security documentation.

Pricing

AI FinOps Pilot

The AI FinOps Pilot costs nothing for a billed 30-day pilot. There is no cost during the pilot and no purchase obligation; a paid subscription is optional after the executive briefing. It includes AI system inventory, risk assessments, policy and controls, and cloud deployment. This is a sensible way for CFOs, FinOps leads, CAIOs and platform teams to validate organizational fit, but 30 days is a short window for cleaning identity data and establishing dependable attribution. Kanawai has no free plan beyond this pilot, and paid pricing is custom.

Platforms

Kanawai AI is delivered on the web. Its deployment model uses cloud infrastructure and application-layer identity flows rather than endpoint agents or cloud-environment modifications.

Who it's for

Choose Kanawai when several departments use multiple AI providers and leadership needs one inventory, cost view and policy mechanism. It is particularly relevant to CFOs and FinOps leads controlling token spend, CAIOs setting enterprise policy, and platform or security teams responsible for integrations and evidence. An individual, a small team with one provider, or a buyer needing an automatically executing optimization engine should look elsewhere.

Pros and cons

• Pro: Continuous discovery includes unsanctioned tools, so governance is not limited to applications employees have formally registered.
• Pro: Cost and token views by model, person and department connect technical consumption to financial ownership.
• Pro: Machine-enforceable rules and signed, tamper-evident evidence support recurring compliance work and audits.
• Pro: Scoped OAuth, SAML and OIDC flows avoid endpoint agents and cloud-environment changes.
• Con: Pilot spend attribution depends on accurate identity mapping, which can delay useful chargeback and reporting.
• Con: Model-routing and right-sizing outputs are recommendations; customers must request operational changes.
• Con: There is no ongoing free plan, and subscription pricing is custom after the 30-day pilot.

Alternatives

CognitiveView is a better starting point for teams wanting a free plan with unlimited members, 250 issues and broad integration, API and webhook access. ComplyLayer suits buyers who need a no-credit-card, no-time-limit free tier covering unlimited AI systems and use cases. Prufer is worth considering when a freemium governance product with a seven-day audit-data retention limit on its free plan is acceptable. Trusys AI fits teams seeking a free starter tier for one application with evaluation and observability quotas. Verisum offers a free Explorer tier for buyers who prefer a lightweight assessment entry point. Openlayer Guardrails is the alternative for API or self-hosted teams wanting a free basic workspace with inference-pipeline and log quotas. SAS/STAT is a conventional statistical platform with contact-based pricing rather than an AI-governance focus. AI Verify is the choice when an open-source toolkit under the Apache 2.0 license is the priority. For a broader directory, see AI Governance Software.

Verdict

Kanawai AI is a strong choice for organizations that need one enterprise view of AI inventory, spending and enforceable governance, especially where audit evidence and regional data isolation matter. The main reason to choose it is the combination of discovery, attribution and policy controls in an application-layer deployment. Look elsewhere if your team cannot establish dependable identity mapping, needs automatic optimization changes, or requires a permanent free plan and transparent subscription price.