You do not migrate a deeply-integrated observability platform overnight, and you should not try. This guide takes the safe path: because 24Observe ingests OpenTelemetry, the same instrumentation feeding Datadog can ship to both at once, so you compare them on live, identical data, move one surface at a time, and retire Datadog only when you are satisfied. Honest about what carries over cleanly and what to weigh.
The single most important decision in any observability migration is to not make it a migration at all, at first. Run the new platform alongside the old, on the same live data, until you trust it — then retire the old one surface by surface. This removes the risk that makes teams put migrations off for years.
What makes this possible with Datadog specifically is that both platforms speak OpenTelemetry. Your applications and your collector already emit it (or can), and OpenTelemetry is happy to ship to more than one destination at once. So you are not choosing between Datadog and 24Observe during the migration — you are sending the same telemetry to both, watching them side by side, and forming a judgement from your own production data rather than from a vendor demo or a comparison page. That is as low-risk as a platform evaluation gets.
From there the work is incremental and reversible. You pick the surface that matters most or hurts most — often the alerts you cannot keep up with, or the part of the bill that grows fastest — recreate it in 24Observe, and run it in parallel until you are satisfied it is better. If it is, you retire Datadog for that surface; if it is not, you have lost nothing and learned something. Because 24Observe also exports your data cleanly, even a full migration later never becomes a trap: your data remains yours to take anywhere.
The rest of this guide is the practical version of that principle — how to dual-ship your telemetry, what replaces each Datadog concept, how to compare fairly, and how to cut over when you are ready. Throughout, it is honest about what carries over cleanly and what you should weigh, because a migration guide that oversells the ease only costs you trust when you hit the part it glossed over.
Send the same logs, metrics, and traces to 24Observe without taking them away from Datadog — a destination change, not a re-instrumentation.
If your services already emit OpenTelemetry, add 24Observe as a destination with two environment variables:
# Most stacks already emit OpenTelemetry, or can. # Point your existing OTel exporter (or collector) at 24Observe — # you can keep shipping to Datadog at the same time during the migration. export OTEL_EXPORTER_OTLP_ENDPOINT="https://api.24observe.com/api/v1/otlp" export OTEL_EXPORTER_OTLP_HEADERS="Authorization=Bearer obs_<your_token>"
If you run the OpenTelemetry collector, the cleaner approach is to add 24Observe as an additional exporter in its configuration, so the collector fans every signal out to both backends. Either way, you change where data goes, not how it is produced — there is no application code to rewrite and no new agent to roll across your fleet. For host-level telemetry that currently comes from a Datadog host agent, install the one-line Sensor, which ships logs and host metrics and self-enrolls; you can run it alongside the existing agent during the migration.
Within minutes, the same telemetry you see in Datadog is also flowing into 24Observe, attributed and searchable. That is the foundation for everything else: now you can recreate monitors and detections here and compare their behaviour against Datadog on genuinely identical data, which is the only fair way to evaluate.
Most Datadog concepts have a direct equivalent. The one meaningful upgrade is that monitors here open investigated incidents, not just notifications.
Recreate your most important monitors and detections first, not all of them. The point of the parallel run is to build confidence on the surfaces that matter, and a handful of well-chosen monitors tells you more than a wholesale re-creation of everything you ever configured in Datadog — much of which, on honest inspection, you may no longer need.
With both platforms seeing the same data, compare them where it counts: on a real incident. Wait for something to genuinely break, or engineer a realistic test, and watch what each does with it. In Datadog you will get the alert and the dashboards to investigate from; in 24Observe you will get an incident that arrives with a root-cause verdict and evidence attached. That difference — start the investigation, or start from the conclusion — is the heart of what you are evaluating, and it only shows up on a bad day, not a calm one.
Pay attention to the people who will live with the tool, not just the person choosing it. Have your on-call engineers and analysts work a few incidents in 24Observe during the parallel run and tell you whether arriving at a verdict beats arriving at a dashboard for them specifically. Their day-to-day experience is what determines whether a platform is adopted or quietly worked around, so their judgement should weigh heavily.
When a surface clearly works better here — the alerts are calmer, the investigations faster, the bill more predictable — retire Datadog for that surface and move to the next. There is no forced cutover date; you progress at the speed your confidence allows, and you keep Datadog running for anything still under evaluation. If you ever decide to stop, 24Observe exports your data cleanly, so the decision was never a trap door in either direction. That reversibility is precisely what lets you migrate without the usual dread.
Be honest with yourself at each step about what you are gaining and what, if anything, you are giving up. Datadog's integration breadth, dashboard depth, and APM maturity are real; 24Observe's investigation, consolidation, AI-agent coverage, and self-hosting are real. The parallel run exists so you can weigh those against each other on your own evidence, surface by surface, rather than betting the whole migration on a comparison you have not tested. Done this way, moving off Datadog is a series of small, reversible, well-informed decisions — which is the only kind worth making about something this important.
Teams often ask how long a parallel run should last. There is no fixed answer, but a useful frame is: long enough to live through a representative sample of incidents on both platforms. For a busy service that might be a couple of weeks; for something quieter, longer. The point is not to rush to a cutover date but to accumulate enough real, bad-day evidence that your judgement is grounded in how the platforms actually behaved under pressure, not in how they demoed. Resist the urge to decide on the first quiet week — the difference shows up in incidents, and you need a few to see it.
Involve the right people early, because a tooling decision made only by whoever signs the contract tends to come undone in practice. The engineers who carry the pager and the analysts who work the queue are the ones whose daily experience determines whether a platform is embraced or quietly circumvented, so put the parallel run in their hands and weight their verdict heavily. If they find that arriving at an investigated incident genuinely lightens their load, that is the strongest signal you will get; if they do not, you have learned something important before committing, which is exactly what the parallel run is for.
Finally, treat the migration as a chance to prune, not just to port. Most long-lived Datadog deployments accumulate monitors, dashboards, and alerts that no longer earn their keep — created for an incident years ago, never removed. Rather than faithfully recreating all of it, recreate what you actually use and act on, and let the rest lapse. Teams routinely discover they relied on a fraction of what they maintained, and that the move is as much a decluttering as a migration. The result on the other side is a leaner, more legible setup that investigates for you — which, for most teams, is the whole reason they started looking.
One practical caution as you cut over: do not retire a Datadog surface until its replacement has survived a real incident, not just a quiet week of green dashboards. The temptation, once the parallel run looks good, is to switch things off quickly to stop paying twice — but the value of the parallel period is the bad day, and a surface that has only been tested on calm days has not really been tested. Hold each surface in parallel through at least one genuine incident, confirm the investigation and routing behaved, and then retire it with confidence. The small extra cost of overlapping for a little longer is trivial against the cost of discovering a gap after you have already turned the old tool off.
Dual-ship your OpenTelemetry, recreate the monitors that matter, compare on live data, and retire Datadog one surface at a time — reversible the whole way.