Sanity vs Speakeasy
Side-by-side trajectory, velocity, and editorial themes.
Sanity is quietly wiring its CMS to be operated by agents as much as by humans.
Sanity is shipping on several fronts in parallel: a maturing MCP server and agent tooling, a Media Library growing real asset-management depth, and steady Studio and SDK ergonomics. The recent run is incremental but coherent — richer Media Library metadata and reference tracking, searchable reference fields, and a stream of MCP tool fixes. Nothing here reshapes the product; it is compounding polish on an already broad platform.
The clearest theme is agent-operability. The MCP server, a skills-install CLI command, agent-focused doc quickstarts, and copy-paste commands 'for humans and agents' all point at Sanity treating AI coding agents as a first-class way to drive the CMS. In parallel, Media Library is being built out toward a full DAM, and @sanity/presets is trimming schema boilerplate.
Expect the MCP and agent surface to keep expanding and Media Library to keep gaining DAM-grade features; the presets package suggests more ready-made schema building blocks ahead.
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.
See more alternatives to Sanity →
See more alternatives to Speakeasy →