Sifflet vs Datadog Pricing: Which Data Observability Tool Costs Less

Short answer: You cannot compare these two on a rate card, because they do not sell the same unit. Sifflet sells one annual contract, 48,000 dollars for 12 months on AWS Marketplace, with capacity described in monitored assets. Datadog sells consumption, billing its Data Observability line per host-hour for clusters and per job-hour for orchestrators, and that usage also generates billable spans, containers and logs in other Datadog products. Sifflet is the predictable number. Datadog is the one that moves with your compute. Neither publishes a figure you can plug into a spreadsheet without a call.

This question comes up because both companies now use the same words. Datadog has a product line on its pricing page called Data Observability, with Jobs Monitoring and Quality Monitoring underneath it, and it has owned Metaplane since April 2025. Sifflet has been selling data observability since 2021. So a buyer reasonably assumes there is a price on each side to compare. There is not, and the reason is structural rather than evasive. Everything below was checked at the source on September 17, 2026.

Sifflet and Datadog pricing side by side

What Datadog actually sells under Data Observability

The line has two products and they bill on different clocks. Jobs Monitoring for Clusters is billed per host, per hour, and Datadog is precise about how the host count is derived: it samples the number of unique instrumented hosts every five minutes and averages those twelve samples across the hour. Its worked example is a cluster that runs 100, then 300, then 150, then 50 unique hosts in four intervals and idles for the rest, which averages to 50 billable host-hours for that hour. Jobs Monitoring for Orchestrators bills per job, per hour instead, summing the duration of every monitored job run across the month and rounding up to the next whole hour.

Quality Monitoring is the piece that maps most closely to what Sifflet does, and it is the piece with the least public detail. The billing units for the Jobs products are spelled out at length in Datadog's own FAQ. The dollar rates themselves are injected into the page by script and do not appear in the served HTML, so a static read of the page returns the meters without the numbers. Read earlier in September 2026, the Jobs Monitoring list rates sat at 0.05 and 0.072 dollars per host-hour depending on plan. Treat any figure you find quoted elsewhere as a starting point and confirm it in your account, because a consumption rate is the part of a bill that gets negotiated.

The Datadog charges that do not appear on the Data Observability line

This is the part that catches finance teams, and Datadog documents it openly rather than hiding it. Job execution traces from Jobs Monitoring for Clusters generate Span Ingestion and Span Indexing usage, and anything beyond the allotted 0.205 GB of ingested spans and 1,370 indexed spans per host per hour lands as an additional charge. Running jobs on Kubernetes generates container monitoring usage, with an allotment of ten Jobs Monitoring containers per host. Turning on log collection, which most teams do because that is where the useful failure detail lives, is billed through Datadog Log Management.

There is one more wrinkle worth knowing before you model anything. Hosts running Jobs Monitoring normally do not also count as Infrastructure hosts, which is a genuine saving. The exception is Spark on Kubernetes, where any node running the Datadog agent counts as an Infrastructure host, and only the nodes actively running Spark jobs count toward Job Host Hours. If your data platform is Spark on Kubernetes, and a lot of them are, that exception applies to you specifically. None of this makes Datadog expensive by itself. It makes the total hard to forecast from the rate card alone, which is why teams on consumption-metered tooling usually end up watching the cloud bill for sudden movements rather than trusting the monthly estimate. One more practical detail: Jobs Monitoring is available in the AP, EU and US regions but not in GovCloud (US), which rules it out for some federal workloads.

What Sifflet actually sells, and why the two prices do not line up

Sifflet's only published price is 48,000 dollars for a 12 month AWS Marketplace contract. Its own pricing page is a real page with a complete three tier feature matrix, and it carries no dollar figures at all. What it publishes is capacity: Entry up to 500 monitored assets, Growth up to 1,000, Enterprise at 1,000 and above with flexible scaling. The awkward detail is that the marketplace contract is not denominated in assets. It sells a single dimension called Data Observability Platform Credits, and AWS's own summary of the listing concedes that it does not break down a fixed per credit rate. So the public ladder counts assets, the thing you sign counts credits, and the conversion between them is not published anywhere. We have taken the whole listing apart, including the per-asset arithmetic and the tier gating, on our Sifflet pricing page.

Two gating details matter for a US enterprise evaluation. Single sign-on is not in the Entry tier, it starts at Growth, so if your security review mandates SSO then the cheapest rung is closed to you no matter how few assets you watch. Pipeline monitoring, which is the capability that overlaps most directly with Datadog's Jobs Monitoring, is Enterprise only. That is the honest version of a Sifflet versus Datadog comparison on pipeline coverage: to get the thing Datadog sells self-serve, you are in Sifflet's top tier and its direct enterprise sales motion. Note too that the old sifflet.ai domain now serves a 114 byte page that forwards to a lander, so any research you did against those URLs describes a site that has moved to siffletdata.com.

Is Sifflet cheaper than Datadog?

For warehouse data quality at a steady asset count, Sifflet is the more predictable number and often the cheaper one, because 48,000 dollars a year does not move when your pipelines get busier. For pipeline and job monitoring on a small or bursty platform, Datadog is almost certainly cheaper to start, because you pay for the hours your jobs actually run and there is no annual floor. The crossover is compute intensity, not data volume. A team running a handful of dbt jobs against a large warehouse pays Sifflet a lot and Datadog very little. A team running thousands of Spark jobs on Kubernetes can invert that inside a quarter, especially once span and container spillover lands.

Does Datadog do data quality monitoring?

Yes, through Quality Monitoring in its Data Observability line, and through Metaplane, which it acquired in April 2025 and which still sells standalone with its own tiers. The strategic read is that Datadog is approaching data quality from infrastructure upward: it already watches the hosts, the containers and the job runs, so data checks are an extension of a platform your team is likely already paying for. Sifflet is approaching it from the catalog outward, with health signals attached to metadata so business users can judge whether a table is trustworthy without asking an engineer. Those are different products that answer different questions, and the pricing difference follows the architecture rather than the other way around.

Can you compare these two on price at all?

Only by modeling your own usage, which is the unsatisfying but correct answer. Take your real numbers: how many assets you would catalog, how many jobs you run a month, their average duration, how many hosts they occupy, and whether you are on Kubernetes. Price Sifflet against the asset ceilings and get the credit conversion in writing before you sign, because the listing publishes no overage rate and states that all fees are non-cancellable and non-refundable except as required by law. Price Datadog by multiplying job-hours and host-hours by your account rates, then add span, container and log spillover, which is the line people forget. We have broken down the Datadog side separately in our guide to Datadog data observability pricing, and put both alongside the rest of the market on our data observability pricing comparison.

If the reason you are comparing these two is that you need freshness, volume, schema, distribution and lineage monitoring on a cloud warehouse, and you want to know the number before the call rather than after it, that is the gap we built for. Dataobservability connects read-only to Snowflake, BigQuery, Databricks or Redshift, reads your dbt project, and publishes every price: 99 dollars a month for 50 monitored tables, 299 for 250, 799 for 1,500, billed yearly, with a 14 day trial and no credit card. You will know within an afternoon whether it catches your real incidents, which is a faster answer than either of the alternatives above can give you.