FastAPI Production Observability (Sentry, Prometheus, ELK) — Setup Engagement

Your FastAPI app ships with print statements and a Grafana dashboard nobody trusts. Incidents start in Slack, not in a ranked alert — and you still grep logs to find the request ID. I am Raman Sapezhka (PlantagoWeb), a solo backend engineer: I wire FastAPI production monitoring so errors, latency, and business-critical paths are visible before customers complain. You work directly with me — no agency hand-offs or generic runbooks.

From "we have logs" to ranked alerts

Most teams already run something — Sentry on staging, Prometheus scraping /metrics, Filebeat shipping to Elasticsearch. The gap is not tooling; it is signal quality and on-call reality.

• Logs without correlation: request IDs missing across Uvicorn, Celery workers, and background tasks — you cannot follow one checkout or webhook end to end.
• Metrics without SLOs: CPU and RPS charts exist, but nobody knows which p95 regression should page the engineer at 2 a.m.
• Sentry noise: every 404 and validation error floods the inbox; real 500s hide in the backlog.
• ELK cost creep: unstructured JSON blobs inflate index size; searches time out during incidents.
• Alert fatigue: fifteen channels, zero severity — on-call mutes everything.

I start with a short audit of what you already have, then prioritize: trace one money-moving endpoint from ingress to database, define alert tiers (P1 revenue-impacting vs P3 informational), and document who gets paged when. Background on patterns and stack choices is in our FastAPI observability overview.

What the setup engagement includes

Fixed-scope work tailored to your stack — typically one production FastAPI service plus workers. Deliverables you can hand to your team:

• Sentry: FastAPI + Starlette integration, release tracking, environment tags, sampling rules, and noise filters so P1 errors surface first.
• Prometheus: /metrics exposure, RED/USE dashboards, histogram buckets aligned with your SLOs, alert rules for error rate and latency.
• ELK logging setup: structured JSON logs (request ID, user/session, route, duration), Filebeat or Fluent Bit shipping, index lifecycle policies to control cost.
• Correlation: shared trace or request ID across API, Celery/RQ, and outbound HTTP — so one click in Kibana or Sentry finds the full story.
• Runbook draft: top five alerts, what they mean, first checks, escalation — not a 40-page PDF nobody reads.
• Handoff: Terraform/Helm snippets or docker-compose overlays, plus a 30-minute walkthrough with your team.

Need OpenTelemetry export, Grafana Cloud, or Loki instead of full ELK? We scope that in the diagnostic — I do not force a stack you will not operate.

Related services and the performance cluster

Observability is often the first step before deeper work. Common follow-ons from the same engagement or a later sprint:

• Django project performance optimization — when traces show ORM or cache bottlenecks beyond logging.
• Monitoring and logging setup (Sentry, Prometheus, ELK) — sibling engagement for Laravel, Symfony, or mixed stacks.
• CI/CD pipeline setup for FastAPI — deploy observability configs with your GitHub Actions or GitLab pipeline.
• PostgreSQL + Prometheus/Grafana monitoring — when API latency is really a database story.

Full catalog: PlantagoWeb services. Profile and stack: Raman Sapezhka.

Free diagnostic — book a call

The diagnostic is 20–30 minutes, no obligation. You describe production pain; I ask about traffic shape, current tools, and on-call habits. You leave with a ranked list of gaps and a fixed-scope proposal — not a sales deck.

• What is already deployed (Sentry DSN, Prometheus scrape targets, log shippers).
• Which endpoints or jobs must never fail silently.
• Realistic alert routing (PagerDuty, Slack, email) and who owns follow-up.
• Ballpark timeline and price for the setup engagement.

Book a free observability diagnostic — mention FastAPI, rough traffic, and whether you use Kubernetes, Docker Compose, or bare VMs.

Schedule a call