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Observe+ with DataDog
End-to-End Observability for Startups — Faster Debugging, Better Reliability, Lower Costs Startups can’t scale what they can’t see. Our OBSERVE+ program, powered by Datadog and AWS best practices, gives founders full visibility into their infrastructure, applications, logs, AI workloads, and user behaviour — so they can ship faster and break less.
Why founders choose OBSERVE+
1. Full Visibility in Days, Not Months
We implement Datadog quickly — metrics, logs, traces, dashboards, custom instrumentation — so founders see real signals immediately.
2. Faster Debugging = Faster Shipping
We set up distributed tracing, APM, and log pipelines so your team can diagnose issues 10× faster and focus on building features instead of fighting fires.
3. Proactive Monitoring & Alerts
We create alerting and SLOs for:
- Latency
- Errors
- Database performance
- API health
- AI inference failures
- Background tasks
- CPU/Memory/Network
Your team gets alerted before customers do.
4. Cost Optimisation Through Insights
Observability exposes bottlenecks and inefficiencies so you can reduce AWS spend without compromising performance.
5. AI, Serverless & Container Visibility
We set up Datadog for:
- AWS Lambda
- ECS Fargate
- EC2 & Auto Scaling
- Bedrock / OpenAI / Vector workloads
- Event-driven architectures
6. Enterprise-Ready Reliability
Observability is a key requirement for SOC2, ISO, HIPAA, MAP, and enterprise sales — OBSERVE+ makes you audit-ready.
How OBSERVE+ Works (2–4 Week Engagement)
Phase 1: Observability Audit (Week 1)
- Application Review: APM, logs, metrics, error rates, SLOs.
- Infrastructure Review: ECS/Lambda, RDS, Dynamo, networking.
- AI/ML Review (Optional): Latency, model errors, token usage, retries.
- Gaps Identified: Missing logs, missing traces, missing dashboards, slow services.
Outcome:
A clear list of blind spots and reliability risks.
Phase 2: Datadog Foundation (Week 1–2)
- Agent Setup & Integrations: ECS, Lambda, RDS, API Gateway, Bedrock, Redis, Mongo, S3, CloudFront.
- Log Pipelines: Normalisation, enrichment, retention settings.
- Tracing: Distributed tracing and custom instrumentation.
Dashboards:
- Platform health
- API performance
- Database insights
- AI inference
- Cost & usage
Outcome:
Full platform visibility in one place.
Phase 3: Monitoring, SLOs & Alerts (Week 2–3)
- SLO Creation: Uptime, performance, latency, reliability, AI accuracy.
- Alerting Framework: Critical/Warning/Notice levels
- Incident Response Setup: Routing, escalation, notifications (Slack, PagerDuty, Opsgenie).
- Error Budgets: Clear targets for engineering teams.
Outcome:
Your team sees issues early and responds before customers are impacted.
Phase 4: Cost, Performance & Reliability Roadmap (Week 3–4)
- Cost Insights: Which services are hot, inefficient, or overprovisioned.
- Performance Tuning: Caching, DB indexing, concurrency adjustments.
- Reliability Playbooks: Runbooks for common issues.
- MAP/SOC2/HIPAA Readiness: Observability baselines aligned to compliance needs.
Outcome:
A production-ready, scalable, optimised environment.
