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March 30, 2026
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4 min read

Hardening Our Detector API for Production Reliability

By the Cereby Engineering Team March 30, 2026 • 8 min read

TL;DR

We completed a production hardening cycle for our Detector API focused on three goals: secure default access, stable runtime behavior, and repeatable operations. The result is a service that is safer to expose, more resilient under burst traffic, and easier to operate during deploys and recovery.

The Problem: A Working API Is Not a Production API

Our detector endpoint already produced correct scores, but production reliability requires more than functional correctness. As traffic increased, we needed stronger guarantees around:

  • transport security
  • authenticated access behavior
  • protection from burst and abusive traffic
  • reboot and deploy predictability
  • operator verification after changes

Without these controls, any scoring model can become operationally fragile even if inference quality is high.

Design Goals and Constraints

We defined the hardening scope with clear constraints:

  1. Keep the detection contract stable for existing clients.
  2. Avoid exposing model-serving internals directly to the public edge.
  3. Enforce secure transport and authenticated access by default.
  4. Add protection mechanisms that fail predictably under pressure.
  5. Make day-2 operations reproducible by any engineer on call.

Architecture Changes

1) Standardized Runtime Architecture

We standardized on a containerized service behind a reverse proxy to separate edge responsibilities (TLS termination, request control) from inference responsibilities (scoring logic and model execution).

This boundary reduces blast radius, improves observability of edge behavior, and keeps the model service off direct public exposure paths.

2) Enforced Secure Transport

We enforced HTTPS-only access with explicit redirect behavior for insecure requests. This removed ambiguous transport behavior and made encryption the default execution path for all clients.

3) Locked Down API Access

We tightened authentication handling on scoring endpoints and aligned integration behavior so valid and invalid credentials produce deterministic responses.

This converted auth from an implicit assumption into an explicit, testable contract.

4) Added Traffic Protection Controls

We introduced request/connection safeguards at the edge layer to reduce overload risk from spikes and abusive patterns.

The key change is controlled degradation: under pressure, the system now fails in bounded, observable ways instead of degrading unpredictably.

5) Improved Operational Reliability

We formalized lifecycle management for startup/reboot behavior and documented a verification runbook that validates stack state, transport behavior, and authenticated scoring.

This reduced operational guesswork and made post-change checks deterministic.

Implementation Notes and Lessons

Hardening surfaced edge cases that are common in production systems:

  • initialization ordering across dependent components
  • health verification behavior when endpoints require authentication
  • environment-variable handling across container boundaries
  • certificate bootstrap sequencing during first-time setup

The important outcome is not that issues happened, but that each one now has a documented, repeatable resolution path.

Outcomes

After this cycle, the Detector API now exhibits:

  • stronger security defaults at the edge
  • improved resilience under non-ideal traffic
  • deterministic startup and recovery behavior
  • better operator confidence through explicit runbooks

In short, we moved from feature-complete to operations-ready.

What Comes Next

The next hardening milestones are:

  • dedicated lightweight health semantics
  • stronger request-level observability and correlation
  • external uptime alerting for faster incident detection
  • additional perimeter protections as traffic volume grows

Reliability is a product feature, and we will continue treating it as an engineering discipline rather than an afterthought.

Visual Summary

flowchart TD
    A[Client Request] --> B[Input Validation]
    B --> C[Detection Pipeline]
    C --> D[Structured Error Handling]
    D --> E[Correlated Logging + Metrics]
    E --> F[Health Signals + Uptime Alerts]
    F --> G[Faster Incident Response]