Streaming & AI/LLM Observability · Full‑Stack OTEL
The Outage You Can't Explain.
The Bill You Can't Justify.
Somewhere a consumer is lagging, or an LLM call is quietly retrying itself into a five-figure bill — and your dashboards can't tell you which. Data at Depth wires OpenTelemetry into Prometheus, Tempo, Loki, and Grafana across your streaming pipelines and your AI tooling — token cost, latency, and error rate, correlated with the trace that caused them.
Data at Depth
Results
Before & after
High-volume streaming platform · Millions of events/day
Challenge
Kafka consumer lag spiked silently with no alerting. The team found out pipelines were broken when downstream processes failed — or when a stakeholder noticed. P1 incidents averaged 45+ minutes to detect.
Outcome
Deployed full observability instrumentation across the pipeline — consumer lag tracking, throughput metrics, and dashboards covering error rates and consumer group health. P1 detection dropped to under 4 minutes. Zero missed incidents in the first 90 days.
Series B · Kubernetes · AI assistants · 80+ engineers
Challenge
The engineering org rolled out AI coding assistants and integrated LLMs into their product. No telemetry existed on adoption, latency, cost per team, or errors. Finance couldn't explain the API bill.
Outcome
Built AI tool telemetry pipeline on Kubernetes — token usage, latency, error rates, and cost attribution visible per team per day across both managed and self-hosted backends. Delivered dashboards for adoption rates, p95 latency, and cost attribution by team. Two high-cost integration patterns identified and fixed within 30 days.
About
Data at Depth
Data at Depth is built on 7+ years of production data infrastructure experience — enterprise-scale event streaming pipelines and full observability stacks for AI tool adoption on Kubernetes.
Most engineering teams find out something broke when a customer complains. Data at Depth builds the visibility layer that changes that.
Data at Depth works with Series B–D SaaS and mid-market engineering teams (30–300 engineers) running Kafka or Kinesis, or adopting AI coding tools and LLM features, with no dedicated platform or observability team — not healthcare, health-tech, or insurance.
Production Kafka at enterprise scale
Data at Depth brings 3+ years of production-scale streaming pipeline experience — millions of events per day. Not tutorials. Not side projects.
Full observability, streaming and AI alike
Data at Depth has built and shipped OpenTelemetry → Prometheus, Tempo, Loki → Grafana for both streaming pipelines and AI/LLM tool adoption — token usage, latency, cost attribution — on self-hosted or managed backends, any cloud.
Observability-native AI integration
Data at Depth applies the same instrumentation discipline to your RAG pipeline or agent from day one — OpenTelemetry across retrieval, generation, and tool-use steps, not bolted on after launch.
AWS certified on both tracks
Data at Depth is backed by AWS Certified Solutions Architect (SAA-C03) + AWS Certified Data Engineer (DEA-C01) expertise. The architecture is sound — clients don't have to wonder.
Statistical rigor
Data at Depth's rigor is grounded in an MS in Biomedical Sciences — evidence-based diagnosis and validation, not gut feel, applied to how problems are scoped and solutions are measured.
Ready to see what your pipeline — or your AI stack — is doing?
Most teams already know something's wrong — they just don't have the data to prove it. Let's start there.