Sazabi
Sazabi is an AI-native observability platform that helps engineering teams find and fix production issues faster. Autonomous agents watch application logs (stdout/stderr) to detect anomalies, unfamiliar errors, traffic spikes, and failed deployments without traditional dashboards, then deliver contextual, root-cause alerts into Slack and Microsoft Teams. Engineers investigate in natural language by mentioning the Sazabi agent, and the platform can hand off context to coding agents such as Claude, Cursor, and Codex or open pull requests to remediate. Sazabi instruments 35+ hosting providers with no code changes and exposes a hosted Sazabi MCP server so agents can query logs, inspect issues and projects, and discover Sazabi tools. Founded by Sherwood Callaway; part of the Y Combinator P26 (Spring 2026) batch; backed by an $8M seed round led by J2 Ventures, Village Global, and Y Combinator, with an open beta.
More than an index entry, but the surface is still mostly links rather than artifacts — the cohort most likely to move a full band from modest, well-targeted work.
API Evangelist profiles Sazabi the way a machine reads it — 2 machine-readable artifacts, pulled from the provider's own public surface and indexed so a developer, an analyst, or an AI agent can evaluate it against every other provider on the network.
Every provider in the network is reduced to the same set of machine-readable artifacts — OpenAPI contracts, event specifications, GraphQL schemas, runnable collections, pricing and rate-limit signals, security posture, OAuth scopes, and the agent surfaces (MCP servers and skills) that let software drive the API on its own. We profile them because the interface is the part of a company you can actually inspect: it is a truer signal of what a provider does than any marketing page. From those artifacts we compute the Kin Score — Sazabi scores 18.4/100 (emerging), with a separate agent-readiness read of 12/100 (human only). The full breakdown is below, followed by every artifact we hold — each card links through to its machine-readable definition on apis.io.
Kin Score
This is the API Evangelist rating — a single, repeatable read computed from the artifacts on this page. Green fill is points earned; the red track is points possible, so every bar shows earned-versus-possible at a glance.
Kin Score
How this is scored →scored 2026-07-27 · rubric v0.5
How we profile Sazabi
Each block below is one kind of artifact we hold for Sazabi. For each we say what it is and why it earns a place in the profile, then list every one we've indexed — capped at two rows, scroll within the panel for the rest.
MCP Servers 1
Model Context Protocol servers expose these APIs directly to AI agents. We profile them because agent-native access is the fastest-growing way this provider's capabilities actually get used.
Model Context Protocol servers that expose these APIs to AI agents.
Sazabi MCP Server
MCP SERVERSecurity Posture 1
Authentication, domain security, vulnerability disclosure, and trust-center signals — the evidence that a provider takes security seriously enough to document it. We profile it because you can't govern what you can't see.
Authentication, domain security, vulnerability disclosure, and trust-center signals.
Resources
Every other property we hold for Sazabi — documentation, portals, status pages, policies, and corporate surface — grouped by the job it does, following the integrator's arc from getting started to running in production.
Get Started 2
Portal, sign-up, and the first successful call
Documentation 1
Reference material describing how the API behaves
Agent Surfaces 2
MCP servers, agent skills, and machine-readable catalogs
Access & Security 1
Authentication, authorization, and security posture
Operate 1
Status, limits, changes, and where to get help
Commercial 3
Pricing, plans, and the legal terms of use
Company 4
The organization behind the API
← All providers · Data indexed from github.com/api-evangelist/sazabi · machine-readable index on apis.io