Metrics, logs, and traces in one pane — self-healing, with no central point of failure, running in your own cloud at 3–12× below metered SaaS.
A complete open-source observability platform — Prometheus + Thanos, Loki, and Tempo — deployed hub-and-spoke so your telemetry never leaves your perimeter, and your bill stops climbing with every host and every series.
See the NumbersMost observability goes wrong in one of two ways — and both are expensive.
We regularly meet teams whose entire observability story is metrics and logs jammed into a single search engine, with no distributed tracing at all. That's one pillar that works, one that's broken, and one that's missing. Numeric metrics run through a full-text index are slow and cardinality-fragile — they fall over mid-incident, exactly when you need a fast aggregate. And with no traces, reconstructing a failure across a dozen services becomes manual archaeology. It's blind on the loop where you investigate and fragile on the loop that pages you.
The commercial alternatives — Datadog, Grafana Cloud, even AWS's own managed services — meter per host, per active series, and per gigabyte ingested, at list prices well above raw infrastructure. Cost scales with volume forever. The more successful your product gets, the more you pay to watch it, with no upper bound.
An integrated open-source stack — Prometheus + Thanos (metrics), Loki (logs), Tempo (traces), and Grafana (visualization) — deployed as a hub-and-spoke model: a central hub for query, alerting, and object-storage-backed long-term retention, with a lightweight spoke in each monitored cluster that federates telemetry to the hub. All three signals, cross-linked in a single pane of glass, running entirely in your own cloud account. Telemetry never leaves your perimeter.
Observability does two different jobs, and we architect for both. The fast loop runs at machine speed — a local Prometheus in each cluster evaluating its own rules and driving its own scaling and recovery, so the watcher fails independently of the watched and keeps working when the network is flaky and the central plane is saturated. The slow loop runs at human speed — a central, richly queryable record for investigating what actually happened. One stack, both loops, matched to the job.
The savings come from architecture, not discounts. Long-term retention lives on object storage (~$23/TB-month, lifecycle-tiered), not on always-on search or time-series databases. Compute scales with ingest and query — not with host count. And there is no per-series or per-host meter. This isn't "free": you still pay for compute and storage. The point is that flat infrastructure has a ceiling and metered SaaS doesn't — so the gap widens as you grow.
The economics are what get noticed first — but they're only half the story. The same architecture is also markedly more resilient. Every spoke is autonomous: a local Prometheus in each cluster evaluates its own rules, fires its own alerts, and drives its own scaling and self-healing — from inside its own blast radius, without waiting on anything central.
That makes detection, alarming, and recovery faster — nothing has to round-trip to a central system before a problem is caught or fixed. And it means the hub is not a single point of failure: if the hub is unreachable, every spoke keeps watching, alerting, and healing itself. The central tier is for cross-cluster investigation and long-term history — not for keeping the lights on. The oldest rule in monitoring holds: the watcher fails independently of the watched.
This isn't a paper design. The same stack has already displaced commercial observability in production:
~$60,000/month → self-hosted
A fintech trading platform replaced a ~$60,000/month Datadog deployment with this stack — all three signals, self-hosted, running in its own cloud account.
~10× data · ~½ cost · tracing added
An enterprise client replaced an AWS-managed stack that had no distributed tracing at all — now handling roughly 10× the data volume at about half the cost, with the tracing they never had.
Same reference workload throughout — roughly 5M active metric series, ~160 GB/day of logs, tracing on, ~90 hosts — priced against current public list prices:
| Option | ~$/month | vs. this platform |
|---|---|---|
| This platform — self-hosted open source | ~$4,000 | — |
| AWS managed (AMP + Managed Grafana + CloudWatch Logs + X-Ray) | ~$12,000–15,000 | ~3–4× |
| Grafana Cloud | ~$25,000–40,000 | ~6–10× |
| Datadog | ~$30,000–50,000 | ~8–12× |
Every metered option has no ceiling — cost climbs with cardinality and ingest indefinitely. The self-hosted stack pays flat infrastructure with no per-series meter, so the gap only widens as you grow. Even carrying a healthy managed-service margin, it still lands below AWS's own managed services — with all three signals included and your data staying in your account.
Figures are order-of-magnitude estimates against public list prices verified August 2026; actuals vary with cardinality, query load, HA level, and instance pricing (Graviton and committed-use discounts lower them). Pricing covers compute and storage only — data-transfer charges depend heavily on architecture: cross-AZ replication, and any traffic into or out of the cloud, can add up at scale, so where the hub and spokes sit (co-located and kept in-region, inside the customer's own account) materially affects the total.
Because the entire stack is open source, there are no per-seat or per-node licenses to buy — or to pass on to your own customers. For on-premise and air-gapped products, the whole observability platform can ship embedded in what you sell, self-hosted in the customer's environment, at the cost of infrastructure alone.
And these aren't obscure components. Prometheus, Grafana, Loki, Tempo, and Thanos are among the most widely deployed tools in the industry — which means every capable large language model has already been trained on them and their documentation in the wild.
So you can ship more than the stack. You can ship an AI diagnostic bot that investigates incidents, maintains the platform, and extends it on the customer's behalf — even when the customer's own team doesn't know how. Operations grounded in tools every model already understands, running in the customer's environment, all at the cost of infrastructure.
Architected, built, documented, and handed off to your team — so you run it, not rent it.
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