Collect, store, explore and alert on metrics, logs and traces across applications, infrastructure and AI. Compare the cost of your current workload, then model how your needs could grow within your budget.
Use Insights to investigate issues, build dashboards and create alerts. Manage your OpenTelemetry collectors centrally, and bring your existing Grafana dashboards into the evaluation.
Evaluate xScaler alongside your existing platform with a month-long parallel proof of concept. Extensions are available by agreement.
100 servers · 1 TB logs · 100K metric series · 300 GB traces · a month. xScaler: $821.50 per month (USD). Competitor estimates: SigNoz: $1,298.40 per month; Dynatrace: $9,031.72 per month; Datadog: $12,448.00 per month; Grafana Cloud: $1,409.00 per month; New Relic: $989.20 per month; Dash0: $1,644.00 per month; Groundcover: $5,314.75 per month.
xScaler combines OpenTelemetry collection, managed metrics, logs and traces, and Insights dashboards and alerts in one observability platform. Evaluate each stage with your existing workflows and data volumes.
Every unique metric label set creates another active series. Keep bounded dimensions such as service, region and environment as labels. Put unbounded identifiers such as request IDs, user IDs and session tokens in exemplars, logs or traces instead, then model the resulting series count in the calculator.
Select a log row or metric exemplar to follow its trace across both latency charts. Focusing or pointing to a control also selects it and stops the demonstration tour.
LogQL · {service="payment-service"} |= "slow query"Requires exemplars with trace context on the histogram. Without them the metric stays queryable, but the jump to a trace goes through time and shared service attributes.
Requires the propagated trace_id and span_id written into the log line. Then a line opens its trace and a span opens the lines written while it ran.
Insights is xScaler's workspace for querying telemetry, building dashboards and creating alerts. Reuse the query that finds a problem as a dashboard panel, an alert rule or a shared snapshot.
Use a read-only snapshot when another engineer needs the investigation window without rebuilding it.
The xScaler agent is a standard OpenTelemetry Collector. Add the opamp extension on the first host, then manage versioned configuration deployments from the portal.
Central configuration reduces the number of separate collection workflows a team has to operate. It does not remove the need to plan collector placement, permissions, staged deployment, parallel running and rollback for a wider rollout.
eBPF infrastructure collection and trace auto-instrumentation depend on operating system, runtime, privileges and deployment method. Confirm the supported boundary before including them in the evaluation.
extensions:
opamp:
server:
ws:
endpoint: wss://<your-opamp-endpoint>/v1/opamp
headers:
Authorization: "Bearer ${ENROLLMENT_TOKEN}"| Agent | Status | Version | Pipelines | Seen |
|---|---|---|---|---|
| agent-01 | healthy | 0.112.0 | 3 | 2s |
| agent-02 | healthy | 0.112.0 | 3 | 3s |
| agent-03 | healthy | 0.112.0 | 3 | 1s |
| agent-04 | upgrade due | 0.111.4 | 3 | 4s |
| k8s-daemonset (18) | healthy | 0.112.0 | 2 | 1s |
Model the cost of your current metrics, logs and traces workload, then adjust the volumes for growth or broader coverage. The xScaler Scale estimate includes 90-day retention. Competitor retention and included services vary as shown. Discuss other retention requirements with our team.
xScaler does not meter hosts. Host-priced vendors do.
USD / month
Paid Scale estimate. All entered usage is charged. Free plan allowances are not deducted.
xScaler pricing combines a monthly base fee with usage charges for active metric series and log and trace ingestion. The estimate below uses the displayed rates and workload inputs.
Indicative monthly estimates in USD using the entered volumes and the assumptions shown. Retention, included services and support differ by provider. These are not equivalent-service quotes. Tax, volume discounts and negotiated discounts are excluded. Self-hosted figures exclude engineering labour. View assumptions.
A tenant is the isolation boundary: its own endpoints, its own token, its own data. You choose its region when you create it, from any AWS, Azure, Google Cloud or Tencent Cloud region, and one account holds as many as you need, so an obligation to keep data in one place costs you a tenant rather than a second contract.
AWS
us-east-1Region fixed at tenant creation.
One login, one bill, separate data · Own endpoint per tenant Region catalogue ·
Connect Claude Code, Codex, Cursor or any MCP client to your tenant. It writes the PromQL, LogQL and TraceQL itself, so you ask in English and it goes and looks rather than waiting to be handed a log paste.
There is no API key to leak. Whoever connects signs in through the browser and ticks the capabilities the connection may use, and it never exceeds that person's role.
Demonstration data · Coding-agent transcript
Why did checkout latency spike at 14:32?
p99 went from 240ms to 3.1s for four minutes. Every slow trace waits on payments-db, and the DB logs show 212 lock-wait timeouts in that window, starting 20 seconds after the 14:31 deploy of payments v2.14.0. That release added an index migration that ran against the live table. Want me to draft an alert rule for lock waits above 50/min?
histogram_quantile(0.99,
sum by (le) (rate(http_request_duration_seconds_bucket{service="checkout-api"}[1m]))){ resource.service.name = "checkout-api" && duration > 2s } | select(span.db.statement){service="payments-db"} |= "lock wait timeout" | jsonp99 went from 240ms to 3.1s for four minutes. Every slow trace waits on payments-db, and the DB logs show 212 lock-wait timeouts in that window, starting 20 seconds after the 14:31 deploy of payments v2.14.0. That release added an index migration that ran against the live table. Want me to draft an alert rule for lock waits above 50/min?
One plan, one bill. Metrics, logs and traces are meters inside it, not three products to buy, and Insights, the MCP server and the open query APIs are on every plan including the free tier.
Scroll horizontally to compare all three plans.
FreeNo card $0 forever Metrics, logs and traces together, one tenant, the whole of Insights. | ScaleMost teams start here $19 / month + usage One package for all three signals. No series or volume ceiling to plan around. | EnterpriseAnnual Talk to us Custom limits, retention up to a year, custom SSO and a dedicated engineer. | |
|---|---|---|---|
| Signals | |||
| Metrics · active series | 20,000 | $0.002175 / series | Custom |
| Logs · ingestion | 50 GB / month | $0.45 / GB | Custom |
| Traces · ingestion | 50 GB / month | $0.45 / GB | Custom |
| Retention | 90 days | 90 days | Up to 1 year |
| Platform | |||
| Insights, MCP server, query APIs | Included | Included | Included |
| Multi-tenancy | Not included | Included | Included |
| Audit logging | Included | Included | Included |
| SSO | Google · Microsoft | Google · Microsoft | Custom provider |
| Enterprise | |||
| Bulk data extract | Not included | Not included | Included |
| Uptime SLA | Not included | 99.9% | 99.9% |
| Support | Community | Priority | Dedicated engineer |
100 servers · 1 TB logs · 100K metric series · 300 GB traces · a month
Retention shown per vendor · 60-second metric samples · 30-day month · USD · logarithmic scale · last updated 2026-09-30
Indicative monthly estimates in USD using the entered volumes and the assumptions shown. Retention, included services and support differ by provider. These are not equivalent-service quotes. Tax, volume discounts and negotiated discounts are excluded. Self-hosted figures exclude engineering labour. View assumptions.
Straight answers on cost, risk and the exit path. Where a claim depends on your workload, the answer says so and tells you how to check it.
Something still unresolved?
Start with a recent bill and the workload behind it: active metric series, log and trace volumes, retention, required capabilities and support.
Use the calculator for an initial estimate, or review the comparison with our team. We compare equivalent requirements and account for relevant contract discounts, currency and additional charges. A parallel PoC then helps validate the workload assumptions and projected cost.