Prometheus vs Speakeasy
Side-by-side trajectory, velocity, and editorial themes.
Prometheus ships 3.13 LTS while hardening the 3.5 line against a steady drip of CVEs
Prometheus is running two supported tracks at once: the long-lived 3.5 LTS, which now takes near-monthly security-only patches, and the new 3.13 LTS, which lands a large batch of PromQL, service-discovery, and TSDB work. The bulk of recent releases are security maintenance and incremental engine improvements rather than new user-facing surface.
The center of gravity is experimental PromQL (start-timestamp-aware rate/increase, smoothed/anchored rate over native histograms, new scalar and search functions) and native-histogram maturation across TSDB and scrape. Alongside that runs a disciplined security cadence — sanitize-html bumps, credential-forwarding fixes on redirects, snappy-decode limits — backported across both LTS lines.
Expect 3.13.x to stabilize out of RC and continue the native-histogram and start-timestamp buildout behind feature flags, with the 3.5 LTS line receiving security-only patches as new CVEs surface.
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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