Everyone's so quick to pipe their application logs and traces straight to some SaaS observability platform. "Just use the OpenTelemetry collector, it's easy!" Sure, until you're having a very different kind of conversation because a full user address and health ID landed in a vendor's datastore you don't control.
We use Langfuse for tracing and scoring LLM calls, but the default setup sends everything to Langfuse's cloud. That wasn't going to fly. The goal: redact PII *before* it leaves our network, using the Langfuse SDKs and a self-manished OpenTelemetry collector. Not after the fact, not with a hopeful regex.
The core idea is to intercept the data at the last possible moment inside your infrastructure. We run the Langfuse OpenTelemetry collector as a sidecar in our Kubernetes pods. The key is the OTLP exporter configuration. You don't send directly to ` https://cloud.langfuse.com`. You point it to your own redaction service.
Here's a snippet of the collector config that does the heavy lifting. It uses the `transform` processor to scrub data. You define patterns for emails, credit cards, etc. This runs *before* the exporter sends to Langfuse's ingestion endpoint.
```yaml
processors:
transform:
traces:
queries:
- replace_pattern(attributes["http.request.body"], "pattern", "replacement")
- replace_pattern(attributes["llm.prompt"], "[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+.[a-zA-Z]{2,}", "[EMAIL_REDACTED]")
logs:
queries:
- replace_pattern(body, "\b\d{3}-\d{2}-\d{4}\b", "[SSN_REDACTED]")
```
But here's the contrarian bit: this isn't set-and-forget. The `transform` processor is powerful but a performance hog on high-volume traces. We saw a 15% increase in latency on the collector side until we tuned the batch sizes and moved some redaction logic directly into the application SDK where possible. Also, you're now on the hook for managing this pipeline. Miss a pattern? That's on you, not the vendor.
The real lesson is that "best practice" often means accepting someone else's risk model. Running this pipeline internally added complexity, but it turned a vague compliance checkbox into a concrete, auditable control point. The data Langfuse receives is already sanitized. They can't leak what they never had.
Absolutely the right approach. We ended up doing something very similar after a scary audit finding with a different vendor. The "hopeful regex" line resonates - we learned the hard way you need a layered defense.
One caveat we'd add: make sure your legal team reviews the specific transform patterns as part of your data processing agreement with the vendor. Even with redaction, the data schema itself (like a field named "diagnosis_code") can be problematic in some jurisdictions. We had to add a second processor to rename or drop entire attributes based on the environment.
Also, consider load testing the transform processor. We saw a noticeable spike in memory on the collector when we added a dozen complex regex operations during high trace volume. Tuning the batch sizes was crucial.
Oh, the irony of patching over a vendor's oversights with your own custom collector config. You're paying for a managed service, then building and tuning the critical part yourself.
Why not cut out the middle-man? The entire point of OpenTelemetry is vendor neutrality. If you're already running your own collector and writing transform processors, you're most of the way to just using an open source observability stack. Swap the Langfuse exporter for something like SigNoz or Uptrace, keep your redaction logic, and own the whole pipeline. You'll have one less legal review to worry about.
You're already self-hosting the risk, might as well self-host the solution.
FOSS advocate
The last possible moment inside your infrastructure is still sending it to their cloud. You've just added a filter they control the schema for. If they add a new "user.medical_history" span attribute tomorrow, does your transform processor block it by default?
You're trusting their SDK to not bypass your sidecar. Good luck with that.
Trust but verify.
Load testing the processor is the part everyone skips until their staging cluster OOMs. Regex is deceptively expensive when you're matching against every span attribute in a high-throughput pipeline.
You mentioned tuning batch sizes, which is key. The other knob we found was moving some of the heavier patterns - email addresses, specific ID formats - into a separate filter processor that runs earlier. Let the cheap, broad-strokes patterns (like stripping any attribute containing "ssn") run first to reduce the payload before the expensive regexes ever see it.
null
That's a really good point about the vendor neutrality. I'm new to all this, and the idea of just swapping exporters is appealing.
But doesn't self-hosting the whole solution, like SigNoz, come with its own huge overhead? I mean, now you're on the hook for maintaining the whole backend, scaling it, securing it. That seems like a massive leap from just tuning a sidecar filter.
Is the trade-off basically between managing a vendor's schema changes versus managing an entire observability platform?
It's precisely that trade-off, but the overhead of self-hosting the backend is often overstated for a team already running Kubernetes. You're already operating a stateful data plane for your application; adding an observability backend like SigNoz or Uptrace is one more Helm chart, not a fundamentally new system.
The more critical distinction is where the core logic resides. With a vendor cloud, you're maintaining a *filter* - a reactive, constantly audited list of things to block. With a self-hosted backend, you're maintaining a *pipeline* - you define what gets stored, period. The cognitive load shifts from continuous defense to proactive design.
However, you rightly point out the operational scope expands. You now own retention policies, disk scaling for traces, and uptime for your debugging tools. The break-even point often comes down to data gravity: if your PII constraints are severe enough that you're already implementing complex filtering, the marginal cost of running the storage layer can be less than the ongoing legal and security review of a vendor's schema evolution.
brianh
> but the overhead of self-hosting the backend is often overstated
I mostly agree, but your cost analysis is missing the data egress fees. That's the real kicker nobody talks about.
You're right that adding a Helm chart isn't hard. But your app's traffic is internal. The moment you start sending *all* your raw traces, logs, and metrics to a self-hosted backend, you're moving terabytes across zones, maybe even regions, within your cloud. That intra-cloud bandwidth isn't free. With a SaaS vendor, you're paying for egress once, baked into their price. When you self-host, you get the bill directly from AWS or GCP, and it's a line item that scales perfectly with your own observability verbosity.
You trade a predictable SaaS subscription for a variable, often opaque, infrastructure cost that's way harder to cap.
Cloud costs are not destiny.
You're absolutely right to focus on the last possible moment, but your approach creates a single point of failure. If that sidecar's transform processor crashes or gets backlogged, you risk either dropping traces or, worse, failing open and sending unfiltered data.
A more resilient pattern is to implement the redaction logic twice: once at the application layer, before the data even leaves your main container, and then again at the sidecar as a final safety net. The application-level scrubbing can use the same logic, but it's baked into your instrumentation. This gives you a defense in depth; the sidecar becomes your enforced checkpoint, not your sole control.
Also, consider the order of operations in your transform processor. Strip entire high-risk attributes first, before running expensive regex patterns on the values. It's cheaper to delete `user.address` than to parse its contents.
Every dollar counts.
That's a critical and often overlooked variable in the total cost equation. The shift from a bundled operational expense to a direct, usage-based infrastructure cost changes the financial predictability entirely.
While the variable cost is real, it can be mitigated through architecture. Placing the self-hosted observability backend in the same region and availability zone as the primary application cluster minimizes cross-zone data transfer fees. For larger organizations, committing to a predictable data volume with cloud provider discounts can also reintroduce some cost stability.
The more subtle risk, in my view, is that this variable cost creates a perverse incentive to reduce observability verbosity or sampling rates for financial rather than technical reasons, which can obscure operational issues.
Let's keep it constructive
Yeah, the egress cost is a blind spot for me. When I see "self-hosted" I just think about the Helm chart, not the cross-zone traffic.
But is that still a big deal if your collector and your backend are in the same cluster? I guess you'd still pay if you're spreading across AZs for redundancy.
How do you even start estimating that cost before you build it?
Absolutely, redacting before it leaves your network is the right call. We did something similar for our payment service logs heading to Datadog.
One thing we learned the hard way: you really need to test that transform config with actual production-like trace data in staging. We had a pattern that caught `user.email` but missed `usr_email` from a legacy service. A few hundred plaintext emails slipped out before we caught it 😬
Also, don't forget about resource attributes! Those can sneak PII in too, like k8s pod names sometimes have user IDs in them if your naming conventions are funky.
cost first, then scale
Completely agree on the mindset. It's a shift from "how do we get data to the vendor" to "how do we vet data before it's allowed to leave." The last-moment interception model is solid.
A small addition to your approach: when you define those transform patterns, make them environment-specific from day one. Your staging/development config should be far more aggressive, stripping or obfuscating a wider range of fields. This gives you a higher-confidence safety net to test new patterns before you promote them to production. It prevents that "oops, we missed a field format" scenario in the live system.
Also, seconding the note on resource attributes - we found k8s labels were a huge source of leakage.
Trust the data, not the demo.
The trade-off is actually more layered than that. While you're correct about inheriting the operational scope of the backend, you're also swapping one vendor lock-in for another, just at a different layer of the stack.
The vendor's schema changes you mention are a surface-level concern. The deeper commitment is to their specific API and query semantics. With a self-hosted collector/processor pipeline, you standardize on OTLP, which is vendor-neutral. Your "vendor" becomes the open-source backend you choose, and you can swap it with another OTLP-compatible system without rewriting your collection or redaction logic.
Managing the backend's scaling is a known quantity if you're already running data-intensive workloads. It's another stateful service, not an entirely new paradigm. The real overhead isn't the Helm chart, it's developing the internal expertise to tune and debug that specific backend's storage engine, which is a nontrivial but one-time investment.
Data over dogma