What Is Memfault and Why Embedded Teams Are Using It Instead of Printf Debugging
You add 47 printf statements to track a heap corruption bug. Three days later, you find it: a use-after-free triggered by a specific BLE connection sequence. You strip out the printfs, rebuild, reflash, test, ship. Six weeks later, the bug resurfaces in the field. A customer sends you a one-line report: “Device keeps rebooting.” No backtrace. No register state. No variable values. No serial terminal. Just silence from 3,000 devices you can’t physically touch.
This is the structural problem with printf debugging. It’s not a skill issue. Printf was designed for a world where the engineer and the device are in the same room. Connected devices broke that assumption years ago, and most embedded teams are still working without a replacement.
That replacement is embedded observability: structured, persistent, fleet-wide telemetry from deployed firmware. And the platform most embedded teams encounter first when they go looking for it is Memfault.
Why Printf Debugging Breaks Down at the Field’s Edge
Printf deserves respect. Zero infrastructure, instant feedback, works everywhere. For bench-level debugging, it’s still the fastest path from “that’s weird” to “oh, there it is.” Nobody should stop using it for local development. (For expanding your debugging toolkit beyond printf, see our guide on getting started with hardware debugging.)
But printf has structural limitations that no amount of cleverness can fix:
Local only. Once a device ships, the serial port is gone. Your customer isn’t going to connect a UART adapter and email you a log.
Synchronous and intrusive. Every printf alters timing. If you’re chasing a race condition or an interrupt-priority bug, the act of observing it can make it disappear. The embedded world’s version of Heisenbugs.
Lossy. No persistence, no history. If the device rebooted before you saw the output, it’s gone. If it happened at 3 AM, it’s gone.
Unstructured. Grep is not analytics. You can’t aggregate “battery_mv: 3412” across 10,000 devices by eyeballing serial output.
Scales to one. One device on your bench, one engineer watching a terminal. That workflow breaks at fleet scale, and fleets are the reality for any connected product.
The embedded industry tolerated this tooling gap for decades because most devices weren’t connected. They are now, and the gap is costing teams weeks of engineering time per field incident.
What Memfault Actually Is
Memfault is a cloud-based observability platform purpose-built for embedded devices and IoT. It was founded in 2018 by engineers from Fitbit and Oculus, people who had personally stared at the ceiling at 2 AM trying to reproduce field failures from vague customer reports. The platform has years of production use across consumer electronics, industrial IoT, and connected medical devices, and it’s well-established in the embedded community through the Interrupt engineering blog, conference talks, and podcast appearances.
Here’s what it does at a mechanical level: A lightweight SDK runs on your device. When something interesting happens (a crash, a watchdog reset, a metric threshold crossed) the SDK packages that data into compact chunks. Those chunks upload over whatever transport the device has: BLE, Wi-Fi, cellular, even deferred via USB. Memfault’s cloud ingests the data, symbolicates coredumps against your ELF file, deduplicates identical crashes, aggregates metrics across your fleet, and presents everything in a dashboard engineers can actually triage from.
If you’ve worked with Datadog or Sentry in cloud/backend contexts, the mental model translates directly. Except the “server” is an STM32 with 256KB of RAM and no stdout.
┌─────────────┐ ┌──────────────┐ ┌─────────────────┐
│ Device │ │ Transport │ │ Memfault │
│ (firmware │────▶│ (BLE/WiFi/ │────▶│ Cloud │
│ + SDK) │ │ Cellular) │ │ │
│ │ └──────────────┘ │ - Symbolicate │
│ Captures: │ │ - Deduplicate │
│ - Coredumps │ │ - Aggregate │
│ - Metrics │ │ - Alert │
│ - Traces │ │ │
│ - Reboots │ ┌──────────────┐ │ │
└──────────────┘ │ Engineer │◀────│ Dashboard & │
│ (browser) │ │ API │
└──────────────┘ └─────────────────┘The CRAFT Framework: A Mental Model for Embedded Observability
Cloud observability has the USE Method (Brendan Gregg) and the RED Method (Tom Wilkie), mental models that tell engineers what signals to look at. Embedded hasn’t had an equivalent. Here’s one useful way to think about it:
+--------------------------------------------------+
| CRAFT: Embedded Observability |
| Mental Model |
+--------------------------------------------------+
| |
| C - Crashes Hard faults, panics, |
| unhandled exceptions |
| |
| R - Reboots Unexpected resets, watchdog |
| triggers, brownouts |
| |
| A - Assertions Failed assert() / runtime |
| invariant violations |
| |
| F - Fleet Aggregated metrics across |
| Metrics all deployed devices |
| (battery, connectivity, temp) |
| |
| T - Traces Event-level breadcrumbs |
| for reconstructing behavior |
| |
+--------------------------------------------------+Each letter maps to a concrete category of signal you need from devices in the field. Walk through them:
Crashes. A BLE-connected wearable hard-faults in the field. Memfault captures the coredump, uploads it on the next connection, and auto-symbolicates it to the exact file, function, and line. No reproduce steps needed. You’re looking at register state and local variables within minutes of the event.
Reboots. You notice that 12% of devices on firmware v2.3.1 are hitting watchdog resets, but 0% on v2.3.0. That’s instant bisection. Without reboot tracking, this is a support ticket that says “my device restarts sometimes,” and you have no idea which firmware version, which hardware revision, or how often.
Assertions. You put a defensive assert() in your CAN bus parser because a certain state “should never happen.” It fires on 30 devices in a specific geographic region. The impossible state isn’t impossible; it’s triggered by a third-party ECU with nonstandard behavior. You know this in hours, not months.
Fleet Metrics. Average battery drain increases 15% after an OTA. On your bench, with your test fixture and your controlled environment, everything looks fine. Across 8,000 devices in varied conditions, the regression is obvious in an aggregated dashboard. Invisible at n=1, unmistakable at n=8,000.
Traces. A device crashes, but the coredump alone doesn’t explain why. A trace shows the sequence: BLE connect → sensor read → timeout → retry → retry → retry → stack overflow → crash. You’re debugging a behavioral pattern, not just a crash address.
Memfault’s feature set maps almost directly to these signal types, which is no coincidence; the platform was designed around them. But the CRAFT model is useful regardless of tooling. If you’re building in-house telemetry, these are the five categories to cover. If you’re evaluating other tools like Percepio’s DevAlert (strong on trace visualization) or Golioth (more device-management focused), CRAFT gives you a checklist to evaluate coverage against.
Printf and Memfault: Different Paradigms, Not a Replacement
This isn’t a binary choice. Most teams use both.
Capability | Printf | Memfault
---------------------|-----------------|------------------
Works on bench | Yes | Yes
Works in field | No | Yes
Timing impact | High | Minimal (async)
Crash capture | Manual (if any) | Automatic coredump
Fleet visibility | None | Aggregated dashboard
Historical data | None | Persistent
Structured data | No | Yes (typed metrics)
Requires rebuild | Yes, per change | No (SDK configured)Printf stays in the workflow for local iteration: quick hypothesis testing, hardware bring-up, early development. Memfault adds the layer that printf structurally cannot provide: post-deployment visibility across the fleet. The shift isn’t “stop using printf.” It’s “stop relying exclusively on printf for production firmware.”
What the Day-to-Day Workflow Looks Like
Here’s a realistic scenario after integration:
Firmware v2.4.0 ships to production. Three days later, Memfault’s dashboard shows a new crash signature affecting 2% of devices, all on a specific hardware revision. An engineer opens the issue, sees a symbolicated backtrace pointing to a null pointer dereference in the SPI flash driver, examines the captured register state and local variables, and identifies that a timing change in v2.4.0 exposed a missing null check after a DMA transfer.
They push a fix. It goes through CI. They use Memfault’s OTA management to stage the update to 5% of affected devices first, monitor for 48 hours, confirm the crash signature disappears, then roll out fleet-wide. The entire cycle, from detection through verified fix, takes days instead of the weeks or months that a “device keeps crashing” support ticket typically burns.
This is the embedded equivalent of the deploy → observe → iterate loop that cloud teams take for granted. The feedback loop changes how teams prioritize: you fix what’s actually failing in the field, not what you think might fail based on bench testing.
When Embedded Observability Might Not Fit
Honesty about limitations builds better decisions. Memfault (or any cloud-based observability) may not be the right fit if:
- Your devices have no upload path. Fully air-gapped hardware can’t send telemetry, though Memfault supports chunked and deferred uploads for intermittently connected devices.
- Your target is extremely constrained. Sub-32KB RAM targets may not have room for any SDK, though Memfault’s is designed for constrained environments and the footprint is configurable.
- You already have mature in-house infrastructure. Some large embedded shops have built custom telemetry pipelines over years. The concepts still apply, and the platform may be redundant, though the maintenance burden of in-house solutions is real and ongoing.
Making Embedded Observability Part of Your Engineering Practice
The shift from printf to embedded observability mirrors what happened in cloud infrastructure a decade ago. Backend teams moved from “log to stdout and grep” to structured observability with Datadog, Sentry, and Honeycomb. It wasn’t because logging was wrong. It was because the systems outgrew it.
Embedded is at the same inflection point. Fleets are growing. Firmware complexity is increasing. Customer expectations for reliability and rapid fixes are borrowing from the smartphone world. Observability isn’t a luxury anymore; it’s infrastructure.
Whether you adopt Memfault, evaluate alternatives like Percepio or Golioth, or build on the CRAFT principles internally, the concrete first step is the same: pick one deployed product, instrument it for crash and reboot tracking, and spend two weeks looking at what comes back. Most teams are surprised by what they find, the bugs that were always there, invisible, because nobody was looking.
The goal isn’t to abandon the serial terminal. It’s to make sure your serial terminal isn’t the only pair of eyes you have on 10,000 devices.
Hubble Network enables Bluetooth-native connectivity from any device to satellite—so your fleet stays observable even where terrestrial infrastructure doesn’t reach. See how it works →