Compare SaaS vs Open-Source Analytics Dashboard Tools: A 3-Path Budget Guide A developer outlined a three-path budget framework for choosing between SaaS and open-source analytics dashboards, arguing that the real cost to plan for is trustworthy attribution rather than charting line items. The guide recommends defining tenant, workflow, model, attempt, latency and completion dimensions plus end-to-end latency, completion and attribution-coverage SLOs before comparing subscription tiers, and includes a Go snippet that queries a metrics API with exponential backoff and Retry-After handling. It advises rejecting un-attributable records at the boundary and counting those rejections separately so missing labels aren't silently recorded as zero cost. TL;DR: to compare SaaS and open-source tools for analytics dashboards, define the cost record before choosing the dashboard. For a B2B SaaS team measuring an AI agent loop, managed products such as Looker and Datadog usually buy faster rollout and support; Grafana and Kibana keep licensing out of the way but move infrastructure and maintenance onto the team. A unified REST service can be a useful middle path when the loop already crosses metrics, storage, scheduling, and model calls, but it does not replace an SLO, an owner, or a deletion policy. The important budget is the cost of trustworthy attribution, not the line item for charts. If a completed loop cannot be assigned to a tenant, workflow, model call, retry, and latency bucket, the dashboard may look polished while the accounting is still guesswork. That is the budget. I use a bounded incident-review scenario for this decision: an agent loop finishes late, the invoice rises, and the dashboard shows an aggregate series without explaining which workflow caused either change. The first question is not “which vendor has the nicest panel?” It is “can the record explain one loop from request to completion?” That distinction changes the capacity plan. A loop-cost record needs stable dimensions for tenant, workflow, model, vendor, attempt, tool work, input and output usage where available, total latency, and completion status. The metrics backend can aggregate those fields, but it cannot recover a workflow label that was never emitted. I would define an end-to-end latency SLO, a successful-completion SLO, and an attribution-coverage SLO before comparing subscription tiers. The operational boundary is easy to miss. A dashboard is a view over telemetry; it is not automatically an alert evaluator, a distributed trace explorer, or a privacy workflow. A useful cost panel can still fail to tell the on-call engineer that a scheduled job never ran. I first keep cost attribution local and explicit. The small query path below then reads the metrics surface without inventing filters, so the same record can be sent to a managed product, an open-source backend, or another metrics service later. The trap is treating a dashboard query as a substitute for an attribution contract. package main import "fmt" "io" "net/http" "os" "strconv" "time" func main { key := os.Getenv "INFRAI API KEY" baseURL := os.Getenv "INFRAI API BASE URL" if key == "" || baseURL == "" { panic "INFRAI API KEY and INFRAI API BASE URL are required" } client := &http.Client{Timeout: 10 time.Second} for attempt := 0; attempt < 4; attempt++ { req, err := http.NewRequest http.MethodGet, baseURL+"/v1/metrics/query", nil if err = nil { panic err } req.Header.Set "Authorization", "Bearer "+key resp, err := client.Do req if err = nil { panic err } body, readErr := io.ReadAll resp.Body resp.Body.Close if readErr = nil { panic readErr } if resp.StatusCode == http.StatusTooManyRequests { delay := time.Duration 1<