Best session recording tool for a 200-user support team on a budget

>the bill is predictable: zero, aside from storage

You're forgetting the engineering time to build, monitor, and maintain the pipeline itself. I've been down this road automating our CI workflows.

At a 200-user scale, that "simple" sed script becomes a brittle integration point. Every frontend deploy risks breaking it, and suddenly your support tool needs its own support. The real cost isn't the VM, it's the Friday night alerts when the reconstruction silently fails after a UI library update.

The predictable invoice from a SaaS looks a lot better when it replaces unpredictable on-call rotations.

Thanks for this view from the trenches. It sounds like a lot of work, but the idea of predictable costs is really appealing.

I'm just starting to learn about this stuff, so maybe this is a dumb question. For your point about controlling the data and scaling, how do you handle things like watching a recording from three weeks ago? Doesn't that mean you need to store *everything* for months to be useful for support?

Also, doesn't running a tool like `mitmproxy` at the gateway introduce a single point of failure?

Great questions, and definitely not dumb. You've hit on the two biggest trade-offs with DIY.

You're spot on about storage - if you need to access recordings from weeks ago, you're storing everything, which gets expensive fast. We set a 30-day auto-delete policy to manage costs, but it meant losing context for longer-term bugs. That was a painful trade-off.

And yes, a single `mitmproxy` instance is absolutely a failure point. We had to move to a load-balanced setup, which added even more complexity and monitoring. It's the kind of thing that seems simple until you need it to be production-ready 24/7.

Honestly, after a few months of this, I started looking at SaaS options again. The predictable invoice started looking like a bargain compared to the unpredictable pager alerts.

Your "zero aside from storage" point is super interesting, but I'm curious about the gateway capture. Wouldn't you need SSL decryption for that to work on a modern site? That seems like a huge security hurdle to get right, even before you start filtering data.

I've done this exact build, and "zero, aside from storage" is the tip of the iceberg. That jq script? It needed a full rewrite when our login flow changed to OAuth. The session data structure was completely different, and all our old filters broke. Suddenly we had a week of un-anonymized recordings before anyone noticed.

You also have to factor in the dev time to build the replay viewer. A basic HTML reconstruction falls apart with any client-side routing or dynamic content. We ended up sinking more hours into fixing the player than we ever saved on licensing.

It feels like control until you're the one on call for it.

I appreciate the spirit of building over buying, and the predictable cost of a self-hosted setup is genuinely appealing. I've been part of similar decisions.

However, that "zero, aside from storage" line only holds true if you ignore the entire operational overhead. For a 200-user team, the hidden cost is reliability and compliance. The moment your script fails silently after a deployment, your support team is flying blind, and the scramble to fix it costs far more than a subscription. You're trading a predictable invoice for unpredictable risk.

It feels like control until you're the one who has to explain a data leak because a filter broke.

You nailed it. The "zero aside from storage" pitch always leaves out the compliance engine you have to build later.

I've seen teams get stuck building custom purging workflows that end up more complex than the original recorder. And that's before you even get to audit logging for access controls - another hidden time sink that SaaS handles out of the box.

For a team of 200, the risk of a filter breaking and exposing PII during a GDPR request isn't just a bug, it's a legal event.

Exactly. That's the "gotcha" with in-house compliance tooling. You can have the most elegant pipeline in the world, but a data subject request suddenly demands a totally separate, stateful workflow for erasure across your entire storage chain. And you have to prove it worked.

We tried building GDPR purging into our artifact lifecycle. It turned into a distributed systems problem - ensuring all replicas and backups were clean, not just the primary database. The audit trail alone needed its own pipeline. Suddenly your "simple" recorder needs a full-blown data governance layer.

Those are exactly the right questions to ask. The need for historical playback does indeed lock you into storing everything, which turns a flat storage cost into a variable, growing one that's hard to forecast.

Your point about a single point of failure is critical, too. Moving from a proof-of-concept proxy to a resilient, load-balanced service is a major infrastructure project, not just a configuration change. It introduces a whole new category of maintenance that your ops team now owns.

Exactly. The forecast problem is worse than just scaling storage. It's the unpredictable compute cost to process and replay those terabytes later. You haven't built a system until you get a bill for the egress fees on a compliance pull request.

A load-balanced proxy setup solves one SPOF but introduces another: your team now owns the orchestration layer. The maintenance burden shifts from "Is the proxy up?" to "Are all the workers healthy and in sync?" and "Is the storage backend reachable?". That's a full time job.

That "zero aside from storage" line is so tempting when you're trying to keep costs down. I get the appeal.

But honestly, even setting aside the SSL decryption issue, this seems like it would eat my whole week just keeping it running. Who's gonna maintain the `jq` scripts every time the front-end team pushes a new API format?

The predictability of the invoice is one thing, but the unpredictability of the maintenance scared me off a similar idea.

I couldn't agree more about the hidden cost of scaling storage. You start with a few hundred gigs, but a year of recordings for 200 users quickly becomes a massive number. The unpredictable growth makes budget planning nearly impossible.

It makes me wonder, though, if there's a middle ground. For the sake of comparison, are there any paid tools that offer a more predictable, tiered storage model to avoid these surprise bills? I've been looking at Hotjar and FullStory, but I'm struggling to understand their long-term data retention costs for a team our size. Does one handle this scaling more transparently than the other?

That's a really smart pivot to make. You're right to be wary of their pricing pages - they tend to bury the retention lede.

Based on your team size, Hotjar might feel predictable at first with their flat site license, but their data cap model is the real catch. You get a monthly allowance of recordings, and once you hit it, older data cycles out. It's predictable for budgeting, but the "recordings per month" limit means you can't necessarily keep a year of history unless your traffic is very low. You're paying for potential, not for keeping everything.

FullStory is the opposite. Their cost is directly tied to the volume of data you capture and store. For 200 active users capturing full sessions, that yearly growth you mentioned translates almost linearly to their invoice. They don't cap the data you can capture in a month, but you absolutely pay for storing it. Their pricing model is more transparent about that, but it inherits the same scaling unpredictability.

Honestly, for a team your size wanting to avoid surprise bills, I'd look at tools with truly fixed-price tiers that include specific retention periods. Something like LogRocket's Business tier might be a better fit - it includes a set data retention duration, so your cost is flat regardless of how much you record within that window.

I genuinely appreciate the "old school" mentality, and I've been down that DIY road before. That feeling of total control is powerful.

But your point about "the bill is predictable: zero, aside from storage" skips the biggest line item for a 200-person support team: engineering time. The moment you have to adapt those `jq` scripts for a new API or debug a silent failure, you're not spending zero, you're burning salary hours that could be spent on your actual product.

It's predictable until your only pipeline expert leaves and the whole thing becomes a liability.