What Is Rocketgraph? Architecture, Pricing Model, and AI Triage, Explained
Rocketgraph is an observability platform that delivers metrics, logs, and traces at scale on object-storage economics, with AI agents that triage issues automatically. This is the technical explainer: how the architecture works, why it stays cheap, and what the AI actually does.
Key takeaways
- Rocketgraph separates storage from compute: telemetry lands in object storage in open columnar formats, and query engines spin up on demand.
- Pricing is usage-based — ingest and query, no per-host, per-agent, or per-seat fees — so cost tracks value instead of fleet size.
- AI triage agents run on the full telemetry lake: they cluster alerts, correlate with deploys, rank probable causes with evidence, and draft incident summaries.
- Ingestion is OpenTelemetry-native: point an OTLP exporter at Rocketgraph and data flows, with no proprietary agent to install.
Rocketgraph is an observability platform that delivers metrics, logs, and traces at scale on object-storage economics, with AI agents that triage issues automatically. This post is the technical explainer behind that sentence.
The architecture in one diagram's worth of words
Telemetry arrives over OTLP, passes through a streaming tier that powers live dashboards and alerting, and lands in object storage in open columnar formats. Query engines spin up against that lake on demand and disappear when idle. The AI triage layer sits on top with access to everything — including months-old baselines, because retention is cheap when it lives in object storage.
Three consequences fall out of this design:
- Retention stops being rationed. Keeping 13 months of traces costs object-storage prices, not hot-index prices.
- Cost tracks usage, not fleet size. A thousand small hosts emitting modest telemetry cost less than fifty hosts emitting a firehose — as it should be.
- Triage sees history. "Is this normal for a Monday?" is answerable, because last quarter's Mondays are still queryable.
Why the pricing model is the product
Observability pricing fails teams in predictable ways: per-host fees tax fleet growth, per-series fees tax cardinality, per-seat fees tax collaboration. Rocketgraph charges for two things — ingest and query — and nothing else. The design goal is simple: your bill should grow when you get more value, not when you autoscale.
The full reasoning (and the levers that keep any stack cheap, ours included) is in our cost playbook for observability at scale.
What the AI triage agents do
When an anomaly or alert fires, a Rocketgraph agent:
- Clusters related alerts into a single issue with one title, so one failure pages one human once.
- Correlates the issue against the change feed — deploys, config flips, feature flags — and walks the trace graph across services.
- Ranks probable root causes, each with evidence attached: trace exemplars, log excerpts, the deploy diff, and an explicit confidence level.
- Drafts the incident summary and keeps the timeline current through mitigation.
Agents propose; humans confirm. Every triage run is stored as an auditable transcript — including the misses, because an agent you can't audit is an agent you can't trust.
Getting data in: five minutes with OpenTelemetry
If you already run an OTel Collector, onboarding is an exporter block:
exporters:
otlp/rocketgraph:
endpoint: ingest.rocketgraph.app:4317
headers:
x-rocketgraph-key: ${ROCKETGRAPH_API_KEY}
service:
pipelines:
traces:
exporters: [otlp/rocketgraph]
metrics:
exporters: [otlp/rocketgraph]
logs:
exporters: [otlp/rocketgraph]No proprietary agent, nothing to install on hosts, and dual-shipping alongside your current vendor is the recommended way to evaluate — see the migration path for the step-by-step.
Who Rocketgraph is for (and not for)
It's built for teams whose observability bill is scaling faster than traffic, who want long retention without rationing, and who'd rather have AI do the first pass of incident correlation. It's not the right pick if you need a decade-old vendor for procurement, or a long tail of niche integrations on day one — we say so plainly in our comparison post.
Questions the docs don't answer: hello@rocketgraph.app.
Frequently asked questions
What is Rocketgraph?
Rocketgraph is an observability platform that delivers metrics, logs, and traces at scale on object-storage economics, with AI agents that triage issues automatically — clustering alerts, correlating telemetry with deploys, ranking probable root causes, and drafting incident summaries before a human is paged.
How much does Rocketgraph cost?
Rocketgraph is usage-based: you pay for what you ingest and what you query, with no per-host, per-agent, or per-seat fees. There is a free tier for small workloads. Current numbers live at rocketgraph.app/pricing — and because storage is object-storage-backed, long retention does not multiply the bill.
Is Rocketgraph compatible with OpenTelemetry?
Yes — OpenTelemetry is the native ingestion path. Point your existing OTel Collector or SDK exporters at a Rocketgraph OTLP endpoint and telemetry flows. No proprietary agents are required, and your instrumentation stays portable.
How does Rocketgraph compare to Datadog?
Datadog is a broader suite with a much larger integration catalog, priced per host plus per custom-metric series plus per GB indexed. Rocketgraph is narrower and newer, priced on usage with telemetry on object storage, and includes AI triage in the core product rather than as an add-on. Teams typically consider Rocketgraph when the Datadog bill starts growing faster than traffic.
Founder & CEO, Rocketgraph
Kaushik founded Rocketgraph to make observability affordable at any scale. He writes about telemetry economics, object-storage architectures, and using AI agents to triage production incidents.