Kinde vs Speakeasy
Side-by-side trajectory, velocity, and editorial themes.
Kinde broadens its auth surface to passkeys while building out billing and B2B controls.
Kinde is shipping monthly feature roundups that consistently advance three fronts: authentication breadth, self-serve billing, and enterprise/B2B controls. The latest release adds passkeys (WebAuthn/FIDO2) for passwordless sign-in, the clearest capability jump in the window. Recent months also brought WhatsApp verification, IdP-initiated SAML, invite controls, and an MCP server for AI agents — a developer-focused auth platform widening on every axis.
Kinde is racing to close the feature gap with incumbent auth providers while differentiating on developer experience and built-in monetization. Authentication is going passwordless and omni-channel (passkeys, WhatsApp, SAML), billing is becoming a first-class self-serve product, and the MCP server stakes an early claim on auth for AI agents. The direction is a single platform that handles identity and billing together.
Expect continued enterprise hardening — likely deeper SSO/SCIM and organization-level controls — paired with more billing automation, as Kinde pushes up-market into B2B.
Speakeasy's Gram is building the governance layer for enterprise AI-coding agents
Speakeasy's platform (Gram, plus the Elements line) governs and observes AI coding agents — Claude Code, Codex, Cursor — across an organization. The recent cadence is fast and dense: prompt-guardrail evaluation, risk policies (including flagging personal versus corporate AI accounts), RBAC scopes for who can read whose agent sessions, shadow-MCP enforcement, per-provider cost and usage breakdowns, and OAuth/CIMD plumbing for strict identity providers. Claude Sonnet 5 is now the default in-app model.
Speakeasy is racing to become the control plane for AI-agent usage in the enterprise: not just connecting agents to tools via MCP, but proving guardrails work before enforcing them, detecting shadow and personal-account usage, attributing cost by provider, and auditing who read which session. The v0.81.0 evaluation workbench — replaying real transcripts through a policy with saved regression sets — signals a shift from static policies to tested, regression-guarded ones. Governance rigor, not raw feature count, is the differentiator being built.
Expect deeper policy tooling (more evaluation, regression, and sensitivity controls), broader provider and account-type visibility, and continued MCP-governance hardening as more coding agents enter the enterprise.
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