Having recently completed a detailed cost and feature analysis for my organization's migration from a legacy monitoring setup, I feel compelled to share a structured post-mortem on the two leading contenders: Datadog and New Relic. Our evaluation criteria were stringent, focusing on total cost of ownership for a 10-person operations team managing approximately 150 AWS EC2 instances, 20 RDS databases, and a suite of serverless Lambda functions, with an imperative for granular cost allocation back to product teams.
The primary pitfall we identified early was the opaque and exponentially scaling pricing models of both platforms. Vendor sales presentations consistently emphasized feature richness and "value," but were reticent to model specific monthly costs against our actual AWS resource footprint and anticipated log volume. What actually happened was a 30-day proof-of-concept where we instrumented a representative 25% of our environment, which revealed critical discrepancies.
* **Datadog's Cost Trap:** Their model, while unified, aggressively charges per host, per container, per custom metric, and per gigabyte of log ingestion. Our PoC showed that enabling APM tracing on our Lambda functions would have increased our estimated bill by over 40% from the base infrastructure monitoring. The much-touted out-of-the-box AWS integration is comprehensive but can be a fiscal hazard if not meticulously filtered.
```yaml
# Example: Unfiltered S3 CloudTrail logging can be catastrophic
datadog:
logs:
- type: s3
path: "my-cloudtrail-bucket/AWSLogs/123456789012/*"
# Without processing rules, this ingests EVERY management event.
```
* **New Relic's Data Dilemma:** Their newer consumption pricing (New Relic One) offers more flexibility but introduces significant forecasting complexity. The vendor claimed cost predictability, but our actual usage of their Telemetry Data Platform showed wild fluctuations day-to-day based on custom event volume. The allocation of data ingest to specific AWS accounts and services was less intuitive than Datadog's tagging, creating friction for our FinOps practices.
From a pure operational capability perspective, Datadog's dashboarding and alerting were perceived as more immediate and intuitive by the team, reducing time-to-value. However, New Relic's embedded security observability and deeper querying capabilities (NRQL) offered superior investigative power for incident post-mortems. The critical question became whether that investigative power justified a 15-25% higher projected monthly cost given our scale, which it did not.
Ultimately, we selected Datadog, but with a critical set of governance guardrails implemented from day one: mandatory tagging standards for all AWS resources, aggressive log filtering at the agent level, and scheduled reports to track custom metric cardinality. The lesson is that neither platform is "better" in a vacuum; the optimal choice is dictated by which platform's billing dimensions you can most effectively control and which interface minimizes your mean time to resolution. For a team of our size on AWS, control over cost levers was the paramount concern.
Would we renew? With our current governance, yes, for Datadog. However, this is contingent upon quarterly business reviews to audit usage against budget, as complacency leads directly to budgetary hemorrhage. The bill, as they say, is the ultimate truth.
Show me the bill.
CostCutter
1. FRAMING (1-2 sentences, plain voice): I'm the lead cloud cost manager for a 60-person e-commerce platform, and I've personally run both Datadog and New Relic in production on AWS over the last three years as we've grown.
2. CORE COMPARISON:
* **Real monthly cost for your scale:** For your 150 EC2 hosts, Datadog will run about $31-33 per host ($4.7k-5k/mo) just for infrastructure monitoring before logs or APM. New Relic's all-inclusive "One" pricing starts around $99 per user per month for the Pro tier, so for a 10-person team you're at roughly $1k/mo base, but you need to watch their data ingest caps.
* **Where Datadog clearly wins:** Their log correlation and exploration is faster and more intuitive for real-time ops. For a team actively debugging production issues, the time saved pulling Lambda logs, traces, and host metrics into a single view is real. Their AWS integration is more automatic.
* **The hidden cost trap:** Custom metrics. Datadog's default integration sends thousands of custom metrics. You can hit the first 100 free per host, then it's $0.05 per metric per month. I've seen teams add $1.5k/month silently. New Relic has a more generous custom metric allowance baked into their user-based tiers.
* **Support and vendor experience:** With New Relic's shift to the simplified "One" model, their sales and support have been more flexible on pricing for committed annual contracts. Datadog's negotiation felt more rigid, based purely on volume. When we hit a log ingestion spike, New Relic issued a one-time credit faster.
3. YOUR PICK (1-2 sentences): I'd pick New Relic for a 10-person team focused on cost predictability and getting broad visibility without micromanaging every custom metric. Choose Datadog if your primary pain point is rapid, collaborative troubleshooting and you have the bandwidth to actively manage metric tagging and volume. To make the call clean, tell us your monthly log volume in GB and what percentage of your Lambda functions absolutely require APM tracing.
You've pinpointed the critical failure of vendor demos perfectly. The insistence on a PoC with a representative workload isn't just good practice, it's mandatory for financial sanity.
The discrepancy you found between sales models and actual PoC costs is the rule, not the exception. I'd add that your >enabling APM tracing on our Lambda functions< is a textbook example of a cost vector that's almost impossible to forecast without a real test. The per-span pricing creates a bill that's directly tied to your transaction volume, which can be highly variable.
Our own analysis showed a similar gap with New Relic's ingest caps under their "One" pricing. While the per-user cost seems predictable, exceeding the included GBs of data ingest for logs or custom metrics triggers overage fees that can double your bill if your monitoring verbosity increases during an incident. Did your PoC include a sustained period of high-log-volume debugging to test that boundary?
Migrate slow, validate fast.
That custom metric trap is real, got bitten by it myself a few years back. Your $1.5k/month figure sounds about right. The default integrations are a double-edged sword, you get great data but the bill shows up like a surprise guest.
For a 10-person team, New Relic's per-user pricing can be friendlier, but I'd warn about their "generous" custom metric allowance. Once your infrastructure grows past a certain point, those caps feel more like a throttle. Their data ingest model for logs can get really squirrelly during an incident when your error rates spike, suddenly you're paying for the fire alarm itself.
If you're already deep in AWS, have you considered CloudWatch as the base layer for cost predictability? I know it's not as slick, but you can feed key metrics to Datadog or New Relic and leave the verbose stuff in CloudWatch logs to control costs.
it worked on my machine
You're right about paying for the alarm itself, but the CloudWatch base layer idea has its own tax. That data is never free, and you're just shifting the unpredictable cost to your AWS bill instead of your observability bill. You still pay for the volume when things blow up, AWS just calls it "Logs Ingested" instead of "Data Ingested".
The real move is to aggressively sample and drop logs before they leave your environment. Both vendors will try to sell you their "smart" filtering, but you can't outsource cost control to the people getting paid by the byte.
New Relic's per-user pricing feels friendlier until you need a contractor or a third party auditor to look at something. Suddenly you're buying a license for someone who logs in twice a year.
Trust but verify
> Their PoC showed that enabling APM tracing on our Lambda functions
That's the exact moment the bill went nonlinear for us too. The sales deck always shows a simple per-host math, but turning on APM for Lambda or adding custom tags for cost allocation feels like a second pricing menu.
We stuck with Datadog because their tag-based reporting finally let us show product teams their actual cloud spend, but I had to build a separate dashboard just to monitor our Datadog bill itself. Kind of ironic, monitoring your monitor.
Did your PoC give you a clean way to forecast the custom metric costs, or was it still a guess? That part never got less murky for me.
Still looking for the perfect one
That's a crucial test case for any PoC. Simulating a high-log-volume incident period is the only way to stress the ingest caps or per-span pricing in a meaningful way. Many teams just run a happy path workload, which misses the entire financial risk.
We've seen teams script a "chaos billing" test, deliberately generating error storms and verbose debug logs for a set window during the PoC. It's the difference between modeling your calm Tuesday bill and your Black Friday meltdown bill.
Did your own experience with New Relic's caps show a predictable overage rate, or was it as volatile as the transaction-based pricing?
Let's keep it real.
Absolutely. The APM tracing cost vector you identified is the most predictable source of bill shock. It effectively turns your monitoring spend into a direct tax on transaction volume, which is the opposite of a predictable operational expense.
Your 30-day PoC on a representative slice was the only correct approach. I'd stress that the next step, which most teams miss, is to use that data to build a predictive model that isn't static. You need to forecast costs against not just your current transaction-per-second, but against your projected growth and, critically, your incident response patterns. A single high-severity event with verbose debug logging enabled can generate a cost outlier that destroys your quarterly budget forecast.
One practical mitigation we've enforced is implementing sampling at the tracer level before data leaves the application, and establishing strict tagging conventions to drop development and pre-production traffic from premium features like APM. This turns a variable cost back into a mostly fixed one. Without that, the "unified" model is just a unified way to charge you for every dimension.
Mike
The APM tracing for Lambda is the silent budget killer, isn't it? It's like installing a meter on your revenue stream.
Your 30-day PoC on a slice of the environment is absolutely the right move. One thing we learned the hard way: you have to simulate a full-blown incident during that window. Crank up the log verbosity, trigger a few hundred thousand errors from a script, and watch the data ingest spike. That's when the per-GB or per-span costs stop being theoretical.
The tag-based cost allocation in Datadog is fantastic for showback, but I've had to build internal dashboards with their API to pull our own usage metrics and forecast spend, which feels like an extra part-time job. Have you looked at setting up webhook alerts from their billing API? It's the only way we caught a runaway custom metric process before it blew the quarterly budget.
null
"Chaos billing" tests are smart, but they assume your PoC environment perfectly mirrors production data gravity and retention policies. It rarely does.
New Relic's overage rates felt *more* volatile, honestly. At least with Datadog's per-host pricing, the Lambda APM explosion is a separate line item. New Relic's caps turn a surge into a murky blend of user fees and data overages, which is harder to untangle after the fact.
The real kicker? Both vendors' billing APIs have a 24-48 hour lag. By the time your "chaos test" shows up, your PoC might already be over.
Your stack is too complicated.