Vercel vs Speakeasy
Side-by-side trajectory, velocity, and editorial themes.
Vercel widens its AI Gateway and compute limits as regulation reshapes model access
Vercel's cadence splits between AI Gateway expansion (new models from Moonshot and DeepSeek-via-Azure, harness-level agent APIs in AI SDK 7) and core platform reach (30-minute functions, drag-and-drop Drop deploys, Nitro v3 workflow integration, threshold billing). The AI Gateway is increasingly the center of gravity, and it is now exposed to regulatory pressure.
Vercel is consolidating as a neutral routing and compute layer for AI workloads: more models behind one gateway, harness abstraction in AI SDK 7, and longer-running functions to host agentic jobs. The Claude Fable 5 suspension shows that aggregating third-party models inherits their regulatory risk. Expect continued breadth on the gateway and deeper agent-runtime tooling.
Look for more models and providers added to AI Gateway and further function/runtime limits raised to court long-running agent workloads. Model availability will increasingly hinge on external compliance constraints rather than Vercel's own roadmap.
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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