The licence is not the bill. Ingest is the bill — and it grows with your traffic, not your headcount.

engine free under AGPL plus a subscription tier; Elastic Cloud Hosted priced on PROVISIONED RESOURCES rather than per host or per GB (reported ~$99/mo Standard to ~$184/mo Enterprise at entry size), or Serverless on consumption

Teams already running the ELK stack for logs — this formalises what they have rather than migrating — and estates large enough that per-host pricing has become the problem.

The catch: You run the cluster unless you buy Elastic Cloud, and operating Elasticsearch well at scale is real engineering work. The correlation UX is less polished than Datadog’s and dashboards need more assembly.

per host / month, published list; distributed tracing across services with the span-level detail that names the slow hop — billed on top of Infrastructure Monitoring rather than instead of it

Estates running microservices that need to follow one request across many services, and that already accept a platform bill rather than a per-tool one.

The catch: The meter is per host, so the bill tracks your infrastructure rather than your traffic — and it compounds with every other Datadog module you enable. Cost management is the standing operational task, not a one-time negotiation. Datadog runs no India site: its Asia-Pacific sites are AP1 (Japan) and AP2 (Australia).

per host / month, published list; the base layer of the platform — host, container and cloud-service metrics with the dashboards and alerting most estates start from

Teams that want one place for infrastructure health across cloud accounts, and are choosing the platform deliberately rather than by accident.

The catch: Metrics only: it will tell you the p99 got worse and not which span caused it. Custom metrics and high cardinality are separately metered, and cardinality is the line that multiplies a bill without traffic multiplying. Datadog runs no India site: its Asia-Pacific sites are AP1 (Japan) and AP2 (Australia).

per GB ingested PLUS per million events indexed, published list; ingest and indexing are separate meters, so what you keep searchable costs more than what you merely collect

Estates whose debugging happens in logs and who are prepared to decide, deliberately, which logs are worth indexing.

The catch: Two meters on one product is where forecasts break: teams model ingest and forget indexing. Almost every runaway observability bill in this category is a log bill. Datadog runs no India site: its Asia-Pacific sites are AP1 (Japan) and AP2 (Australia).

per 1,000 sessions / month for RUM, with synthetic tests metered separately by run; what the browser or mobile app actually experienced, including the network and third-party scripts the backend never sees

Teams whose users report slowness that server-side metrics insist is not happening — the gap between what the server did and what the device experienced.

The catch: Session-metered, so a traffic spike is a bill spike with no infrastructure change to explain it. Synthetics are a third meter again. Datadog runs no India site: its Asia-Pacific sites are AP1 (Japan) and AP2 (Australia).

per month on the Team tier (about ₹2,158), Business about $80, both annual; metered on events captured rather than hosts run — exceptions grouped by fingerprint with the release, the commit and the users affected

Teams whose actual question is “why did that crash, in which release, for how many people” — answered at a fraction of full APM pricing.

The catch: Errors and performance for the application, not the infrastructure: no host metrics, no cloud-service integration, no log platform. It is deliberately one job done well rather than a platform.

metered on transactions/spans within the same published Sentry tiers; developer-first tracing joined to the errors and the release that introduced them

Engineering teams that want tracing tied to the commit that caused the regression, without adopting a per-host platform.

The catch: Sampled by design and scoped to the application — it will not replace infrastructure monitoring, and deep cloud-service telemetry is outside it.

metered per replay within the Sentry tiers; a recording of the session that produced the error, so the reproduction step is watching rather than guessing

Teams losing time to bugs that cannot be reproduced from a stack trace alone.

The catch: Replays are a separate meter and privacy masking must be configured deliberately — an unmasked replay of a checkout page is a data-protection problem, not a debugging win.

per host / month billed annually, published starting prices: Infrastructure $15, App & Infra $60, End-to-End $75 (adds RUM and browser synthetics); a full-fidelity platform built on OpenTelemetry with no-sample tracing

Estates that want every trace kept rather than sampled, and that are already in the Cisco/Splunk relationship — a distinct SKU from Splunk Enterprise Security, which is carded on the Security category.

The catch: The published figures are starting prices per host, so the bill tracks infrastructure like Datadog's, and the final number is still a quote. Full-fidelity tracing is a genuine differentiator and a genuine volume commitment. Splunk documents no India realm for Observability Cloud.

quoted on the Splunk platform; service-level health modelling and AIOps event correlation over data the platform already holds — the layer that turns thousands of alerts into a handful of service states

Large estates already ingesting into Splunk whose problem is alert volume rather than missing telemetry.

The catch: It assumes the Splunk platform underneath and its value is proportional to how much you already ingest there. Service modelling is configuration work measured in weeks, not a switch.

quoted per monitored resource; SaaS-delivered infrastructure and application monitoring from the vendor whose depth is databases — the cloud front end to a Foglight estate

Estates already running Foglight for databases that want the same console over cloud infrastructure rather than a second platform.

The catch: Narrower application-tracing depth than the observability specialists; its strength is the database line, and buying it as a general APM is buying the wrong half of the portfolio.

quoted per monitored host or VM; virtual and cloud infrastructure monitoring with capacity planning — built for VMware-shaped estates and their migrations

Estates with a large virtualised footprint that need capacity forecasting alongside health, particularly through a hypervisor migration.

The catch: Infrastructure-scoped: no application tracing and no log platform. Its natural buyer is the virtualisation team, not the engineering team this page is written for.

about $0.28 per 1,000 events over the free allowance, Enterprise from roughly $25–30k a year; product analytics — what users did in the product, funnels and retention, not whether the server was healthy

Product and engineering teams asking which features are used and where people drop out — a different question from whether the system is up.

The catch: This is product analytics, not infrastructure observability: it will not tell you a service is down or a query is slow. Included here because the event meter and the buying team overlap, not because it substitutes for APM.

metered within the Mixpanel event tiers; replays joined to the analytics events, so a funnel drop-off can be watched rather than inferred

Teams that have the funnel data and still cannot explain the drop-off at one step.

The catch: Tied to the Mixpanel platform and to product questions rather than engineering ones. Privacy masking is a deliberate configuration, not a default.

per host / month published: $29 for infrastructure monitoring, $58 per 8 GiB of host memory for full-stack — drawn down from one annual Dynatrace Platform Subscription; one agent instruments hosts, processes and code, and a live dependency map is what the root-cause analysis reasons over

Estates where incident time is lost finding the cause rather than fixing it, and that want one agent instead of per-service instrumentation.

The catch: Full-stack is priced per 8 GiB of host memory, so large-memory hosts count several times over. Root cause is only as good as the coverage — hops the agent does not see are gaps in the map. The Mumbai region is documented; a written storage commitment is a contract question.

per GiB published, split three ways: $0.20 to ingest, $0.0007 per GiB-day to retain and $0.0035 per GiB scanned by queries — or $0.02 per GiB-day with queries bundled; logs sit in Grail beside the traces and metrics they explain

Teams that want logs in the same store as their traces, and that will set retention per source rather than keep everything for a year.

The catch: Pay-per-query charges per GiB scanned, so wide queries over long windows add up — choose between pay-per-query and bundled on how the team actually searches. DQL is new to most teams.

per 1,000 real-user sessions published ($4.50 with Session Replay), synthetic browser tests at $4.50 per 1,000 actions; each session is joined to the backend trace behind it. A Leader in the 2025 Gartner Magic Quadrant for Digital Experience Monitoring

Digital and product teams that need a slow page to arrive with the backend service that caused it, and replay evidence behind a conversion drop.

The catch: Cost scales with sessions, so a high-traffic consumer app needs a sampling policy decided up front, and replay is safe only once masking is configured and reviewed.

per node a month for network and infrastructure; APM $27.50 per service, logs $5 per GB, databases $70 per instance, synthetics and RUM priced apart — multi-year contracts billed annually

Teams that want network, infrastructure, APM and logs in one SaaS with every module on a published price, and a bridge to self-hosted SolarWinds.

The catch: Data sits in North America, Frankfurt or Australia — no India region; each module is priced separately, so model the whole bill; a Niche Player in Gartner’s Observability Platforms Magic Quadrant.

per node a month on multi-year contracts billed annually (Essentials; Advanced $14, Premier $17.50); Enterprise from 500 nodes; NPM, SAM and other modules still sold singly on quote

Network and infrastructure teams that must keep monitoring data on their own servers — including in India — with the Orion-era modules in one platform.

The catch: You run and patch it (critical flaws fixed in September 2026), and when the subscription lapses polling of new data stops; SUNBURST (2020) came through this platform’s build system — the SEC case was dismissed in November 2025.

quote-only in two editions — Express for private cloud, Premium for private or public cloud with real user monitoring — plus paid add-ons for integrations, event and metric analytics, GenAI incident management, observability, automation and discovery/CMDB; SaaS, hybrid, on-premises or air-gapped

Operations teams running many monitoring tools who want one event view above them — 1,000+ prebuilt integrations, AI correlation OpenText says cuts event volume by 30–95%, and runbooks gated by approvals.

The catch: An event and AIOps layer, not deep APM: code-level traces and logs come from the Application Observability add-on or the tools it ingests. CVE-2025-3476 (CVSS 9.4) hit the event console in 2023.05 to 24.4; no OpenText-hosted India region is published.

Pro from US$2,000 a month billed annually (US$24,000 a year) with 50 users, 1,000 flows per second and 25 monitored devices included, a 30-day trial and Premier by quote; network flow, device metrics, synthetic tests and cloud flow logs; Infoblox acquired Kentik in August 2026

Network and platform teams that need to see traffic across data centres, clouds and the internet — flow, routing and synthetic tests — rather than application traces.

The catch: Network observability, not APM: it does not trace code. Data is stored in Ashburn, Virginia or Frankfurt, with no India region, full-resolution flow is kept 45 days by default, and the published price is a US list for annual contracts only.

per managed virtual server (MVS, roughly a host) / month on SaaS; $0.03 per MVS-hour pay-per-use; self-hosted $385.20 per MVS / year; 14-day trial; SaaS in Mumbai since April 2024 (IBM staff blog)

Teams that want one-second, automatically discovered traces and dependency maps without hand instrumentation, priced per host rather than per GB.

The catch: MVS metering follows infrastructure size, not traffic, and IBM should confirm in writing how containers and Kubernetes nodes are counted; retention is not published.

logs from $0.49 per GiB ingested, traces from $0.59 per GiB, metrics from $0.008 per data point per minute; compute, unlimited users and 30-day retention for logs and traces included (metrics 13 months); committed-volume subscription

Teams whose Datadog or Splunk bill grew faster than their traffic and who want logs, metrics and traces in an open lakehouse with an AI SRE on top.

The catch: A committed-ingest subscription, so you size the commitment up front; no Indian hosting region is documented, and RUM and session replay are not listed. Snowflake bought Observe in 2026, so packaging is still settling.