Gram by Speakeasy

Gram by Speakeasy is speakeasy control plane for connecting, securing, and scaling organizational AI and MCP usage. The fetched application shell identifies Gram as the Speakeasy AI Control Plane for connecting, securing, and scaling AI usage across an organization. The public page was a JavaScript application behind login, so detailed entitlements were unavailable. Gram is positioned around publishing governed API capabilities as agent-accessible tools.

The practical feature set recorded from vendor material includes AI integration control plane, Governed tool connectivity, Central organizational management, MCP-oriented API exposure. These capabilities are most relevant for Publishing company APIs for agents, Managing MCP connectivity, Centralizing tool-call security. Start evaluation with one bounded, repeatable task and a named human reviewer. Measure completion quality, time saved, failure recovery, and how often a person must correct the result. For agentic products, test ambiguous instructions, stale data, permission failures, retries, and cancellation instead of evaluating only a polished demonstration.

Pricing observed during this run was: Current plans: Manual verification required (Homepage and pricing URL returned the same JavaScript shell). Prices, quotas, included model usage, overages, taxes, annual discounts, and contract terms can change, so capture a dated quote before deciding. A low headline subscription can still become expensive when usage, seats, managed infrastructure, or premium support are added. Model Context Protocol support is prominent: Gram is an MCP-oriented control plane for publishing governed tools; exact transports require verification. Treat every exposed tool as a security boundary. Test authentication, least-privilege scopes, user attribution, logging, timeouts, and destructive-action controls before connecting production accounts.

Before production adoption, confirm administrator controls, retention and deletion, data residency, exports, service commitments, model-provider terms, and whether customer data is used for training. Run the pilot with representative inputs, and use sensitive or regulated examples only after appropriate agreements and controls are in place. Keep approval gates around consequential actions until behavior is predictable. Gram by Speakeasy belongs on a shortlist when its integration model and operating controls fit a real process, not merely because it produces an impressive one-off result.

Competitive context, pricing evidence, and evaluation

Gram is best compared with Model Context Protocol (MCP) as the connectivity baseline, Cloudflare AI Gateway for centralized AI traffic, Auth0 for identity, and Composio for packaged SaaS actions. Its differentiator is publishing a company's own structured API capabilities through a governed MCP-oriented control plane.

Both fetched URLs returned only a nine-character application shell. No dollar amounts, quotas, deployment regions, SLA, or support terms were visible, so pricing is correctly recorded as contact vendor and this entry remains flagged. Request a quote separating platform fees, users, environments, tool calls, implementation, and support.

Pilot five read-only operations and two scoped writes. Test expired credentials, schema changes, prompt injection in tool output, oversized responses, timeouts, retries, cancellation, and privilege escalation. Require user-level attribution and verify revocation. Measure completion rate, p95 latency, policy-denial accuracy, operator time, and recovery after a gateway failure. Gram fits organizations standardizing internal API access for agents; it is harder to justify when a team needs only a few low-risk local tools.