A production error and a slow database query are not the same problem, and Sentry and Datadog are built around different answers to that distinction. Sentry starts from "something broke, show me exactly what happened." Datadog starts from "show me everything happening across the entire stack, all the time." Both can technically do parts of the other's job. Neither is the right default for every team.
What Sentry Actually Does
Sentry is an open-source application monitoring platform with 39,000+ GitHub stars, founded in 2012. It has become the default error tracking tool across the industry. When something breaks in production, Sentry is usually the first place a developer looks.
The free developer tier includes 5,000 errors per month. Paid tiers: Team at $26/month (50,000 errors, 1-hour data retention), Business at $80/month (custom retention, advanced integrations, SSO). That pricing structure is simple and predictable, which matters more than it sounds like it should.
Every captured error includes a full stack trace, breadcrumbs of the user actions leading to the crash, device and browser context, and the release version that shipped the bug. The SDK auto-instruments in most frameworks (JavaScript, Python, Go, Ruby, Java, iOS, Android, React Native) with minimal setup, usually a few lines of initialization code.
Sentry has expanded well past basic error capture: session replay lets you watch a video reconstruction of what a user was doing when the error fired, performance monitoring adds transaction traces and slow query detection, profiling generates CPU and memory flame graphs, and cron monitoring alerts when a scheduled job fails to run. For a team that lives in stack traces and wants to fix bugs fast, this feature set covers the job without requiring a second tool.
Self-hosting Sentry is fully functional and free, but Reddit threads consistently flag the resource requirement: 8GB+ RAM for a comfortable deployment. That makes self-hosted Sentry a reasonable choice for a team with infrastructure expertise and a reason to keep error data in-house, and a poor choice for a small team without dedicated DevOps capacity.
What Datadog Actually Does
Datadog is a publicly traded (DDOG) cloud monitoring platform founded in 2010, covering the entire observability stack: infrastructure metrics, application performance monitoring, log management, real user monitoring, synthetic testing, security monitoring, and CI/CD pipeline visibility.
The pricing is modular and host-based, which is where most surprises come from. Infrastructure monitoring runs $15/host/month. APM adds $31/host/month. Log management is $0.10/GB ingested plus $0.0025/GB indexed. Real user monitoring is $1.50 per 1,000 sessions. Each product is billed separately, and costs compound as you add more of them. A 50-host production environment running infrastructure monitoring, APM, and log management can reach $5,000-10,000/month without much effort.
This is the most consistent complaint about Datadog across Reddit and G2: the bill is predictable right up until it isn't. Log ingestion in particular catches teams off guard when a service unexpectedly gets verbose (a misconfigured logger, a debug flag left on, a retry loop spamming logs) and the log volume charge spikes with it.
What you get for that cost is real breadth. Datadog integrates with 600+ technologies through pre-built integrations. Dashboards, alerting, SLOs, and incident management are first-class, not bolted on. Machine learning-based anomaly detection cuts down alert noise on infrastructure that changes shape constantly (autoscaling groups, ephemeral containers). For a platform team responsible for the health of dozens or hundreds of services across a company, this is the tool built for that job.
Where They Actually Overlap
Both tools do application performance monitoring. Datadog APM tracks distributed traces across services and ties them to infrastructure metrics, useful when the question is "which service in this call chain is slow." Sentry's performance product tracks transaction traces tied to specific code paths and releases, useful when the question is "which deploy introduced this regression."
Both can alert on error rate. Datadog folds error rate into a broader alerting system alongside CPU, memory, latency, and custom business metrics. Sentry's alerting is scoped to errors and performance issues tied to your application code, with less reach into infrastructure-level signals.
Neither replaces the other cleanly. A team running both (which is common at mid-size and larger companies) typically uses Sentry for "what broke in the code, and who broke it" and Datadog for "is the system as a whole healthy right now."
Cost at Realistic Team Sizes
For a 10-person engineering team on a handful of services: Sentry Team at $26/month covers error tracking completely. Datadog for the same footprint, running just infrastructure and APM on 10 hosts, runs $460/month before log management is even added. The gap is not close at this scale.
For a 50-engineer team running dozens of services in production: Sentry Business at $80/month (possibly with usage overages depending on error volume) still handles error tracking affordably. Datadog at that scale, covering infrastructure, APM, and log management properly, is realistically a $3,000-8,000/month line item. That cost is justified when the team genuinely needs cross-service visibility and incident correlation. It is not justified if the actual daily need is "tell us when the app throws an exception."
The Self-Hosted Alternative
For teams evaluating Datadog's infrastructure-metrics use case specifically and balking at the price, Grafana with Prometheus is the free, self-hosted alternative that covers metrics and dashboards without the per-host billing. It requires real operational investment to reach equivalent capability (someone has to run and maintain it), but for a team with the DevOps capacity, it eliminates the recurring cost entirely. This is the same tradeoff pattern that shows up across the self-hosted software landscape covered in the best self-hosted apps roundup: free and self-managed versus paid and managed, with the deciding factor almost always being whether the team has spare engineering capacity to spend on infrastructure instead of a subscription line item.
Who Should Use Sentry
Your primary need is knowing when and why the application code breaks, with enough context to fix it fast. This is Sentry's whole design center.
You want predictable pricing. $26 or $80 per month, full stop, versus a modular bill that scales with usage across five different products.
You are a small-to-mid engineering team without a dedicated platform or SRE function. Sentry's setup is minutes, not a project.
Who Should Use Datadog
You need visibility across infrastructure, application performance, and logs in one correlated system, not three separate tools stitched together.
Your team has the budget and the operational maturity to configure Datadog properly, including watching log ingestion costs actively rather than discovering them on the invoice.
You are running infrastructure at a scale where "is the system healthy" is a harder question than "did this specific request fail," and you need cross-service tracing to answer it.
The Recommendation
Start with Sentry. It is cheaper, faster to set up, and solves the most common early-stage problem, which is "something is broken and I need to know what and why." Almost every team benefits from this regardless of size.
Add Datadog (or a self-hosted Grafana/Prometheus stack) once infrastructure-level questions become the bottleneck: intermittent latency across services, capacity planning, or incident response that requires correlating logs, traces, and metrics simultaneously. That threshold usually arrives around 15-20 hosts or a genuinely distributed service architecture, not before. Paying for Datadog's full stack before you have that problem is paying for breadth you are not using yet.