Switched from Arize AI to Evidently AI - honest comparison after 6 months

Storing the full JSONB in Postgres is the right move for flexibility, but you're right to worry about schema drift. We version the profile objects themselves with the Evidently library version as a top-level field and treat the JSON structure as immutable once written. Any new fields in a future Evally version go into a new column or a separate table; we don't try to backfill or alter the old JSONB.

The bigger headache isn't new fields, it's the breaking changes in metric calculation logic between major versions. A profile from v0.2 might not be comparable to one from v0.3, even if the schema is intact. Our policy is that long-term trend analysis only uses profiles generated from the same library minor version. We keep the old container images around to regenerate summaries if we absolutely have to.

That's a decent versioning strategy, but you're still stuck with the comparison problem across major versions. Keeping old container images to regenerate is heavy.

We solved this by always storing the raw data samples used to generate the profile, not just the profile. The Evidently profile object is a derivative. We keep the source data (a parquet file in S3) and the exact commit hash of the pipeline code that generated it. Then we can rebuild profiles with any version of Evidently we want.

It's more storage, but it's cheaper than maintaining a zoo of old container images.

The 30% savings from the pricing model shift is the easy part to quantify. I'd look at the infrastructure line items next month. Embedding Evidently profiles can be surprisingly compute-heavy if you're not careful.

>serverless/Jamstack vibe

That vibe often bills per-second. If you're calculating statistical tests on every inference batch, your Lambda or Cloud Run charges could silently match the old SaaS fee. The real savings usually come from moving to a scheduled batch job, like running profiles hourly against aggregated logs in S3. It decouples monitoring compute from your inference path and costs less.

Did you see a noticeable change in your AWS/GCP compute bill after the switch, or did the cost just move from the Arize invoice to your cloud provider?

You're absolutely right about the configuration work just shifting location. The "real-time" claim often glosses over the architectural cost of embedding statistical tests in a serving path. It forces you to choose between latency spikes and sampling, which itself skews your metrics.

The more subtle trade-off is in validation. When you define the reference window and statistical test in a Python script, you lose the guardrails of a structured UI. I've seen teams accidentally compare a production window against another production window instead of the training baseline, because the connection is just a variable in code. The error is silent until someone audits the script.

So the simplicity isn't in the work, it's in the ownership. You trade a constrained interface for unlimited flexibility, and most teams aren't disciplined enough to document the constraints they now have to self-impose.

That's a critical point about the cost migration. We saw our cloud compute bill increase by about 15% initially, which was an expected trade for the control. However, you can't just look at the raw compute cost in isolation.

The real financial analysis has to include the engineering time you're now spending on optimization, like shifting from per-inference profiling to that scheduled batch job you mentioned. The savings only materialize after you've done that architectural work, which is a non-trivial project. Without it, you're absolutely right, the cost just moves from the SaaS invoice to your infrastructure tab, and you've added operational overhead.

Our break-even came after we stopped using Evidently in any request path and moved entirely to profiling warehouse tables on a schedule. Did your team manage to keep any real-time checks, or did you also move to a fully decoupled model?

Yeah, the stakeholder friction is real. The Grafana dashboards work for engineers, but product folks kept asking how to see "what changed last week" or "why the alert fired." Arize's UI packaged that narrative.

We solved it by building a dead simple Flask app that queries the Evidently JSON in Postgres and renders a static HTML report. It's just a cron job that emails a link. Ugly, but it gives them a single click.

The hidden cost isn't the tool, it's rebuilding those packaged insights yourself.

Ah, the "few lines of Python" integration. That's the marketing line that gets everyone excited. I don't doubt it's technically true for a POC. The reality, as others have hinted, is you've just traded configuring a SaaS dashboard for architecting, scaling, and maintaining your own monitoring pipeline.

Your 30% savings on the Arize invoice is likely a mirage once you factor in the engineering cycles spent building that "serverless vibe." You moved from a fixed, predictable cost to a variable, hidden one on your cloud bill. The real-time drift metrics sound great until you're paying for Lambda concurrency every time a model serves a prediction.

Simplicity is only simple if your team never needs to hire for the niche skills required to keep that custom stack running. When your one Evidently expert leaves, what's the plan?

You've nailed the hidden tradeoff, and that "mirage" effect is real. But I think there's a third path that doesn't lock you into a SaaS or a complex homegrown pipeline.

The real value in "few lines of Python" is that it's a *portable* integration. You can start with a naive Lambda that profiles every 10th inference, see the cost balloon, and then pivot without rewriting your whole monitoring logic. Move those same lines into a scheduled Glue job or a Dagster task. The core logic stays the same.

The hiring risk is true for any bespoke system, but I'd argue knowing how to orchestrate batch jobs and query JSONB in Postgres is a more transferable skill than being an expert in Arize's specific API and UI. You're trading vendor lock-in for infrastructure lock-in, which for some teams is an acceptable swap.

That's an excellent point about historical analysis. We're storing the key metrics from each Evidently report in our existing Prometheus instance, then using Grafana for dashboards. The full JSONB snapshots go to S3 for deep dives, but Prometheus gives us the long-term trend lines for things like feature drift, model performance, and data quality checks.

It did add a small but non-zero maintenance step, you're right. We wrote a simple parser to extract the numerical metrics we care about from the report and push them as gauges. But the upside is that our model metrics now live alongside our system metrics, so we can correlate a drift spike with a deployment or an infrastructure event. That's been more valuable than I expected.

Correlating drift with deployment events is the killer app that Arize never quite gave us. The packaged dashboards always felt walled off.

My caveat on the Prometheus route is cardinality. If you track every feature's drift individually as a gauge, your metric count explodes and you're back to managing retention policies. We had to aggregate to just the top 5 drifting features per model to keep it sane.

That said, once you see a P95 latency spike line up perfectly with a categorical feature drift on your Grafana board, you can't go back. It turns monitoring from a check box into a debugging tool.

Exactly. That cardinality explosion is why we never push individual features. Instead, we push a histogram of drift scores for each model run. One gauge for the 50th percentile, one for the 90th, one for the 95th. You lose the specific feature name, but you keep the shape of the problem.

When the p95 drift score spikes, you know *something* is wrong. Then you go to the JSON snapshot in S3 to see which feature it was. It keeps Prometheus lean and still gives you that "oh, the drift spike started right after the 3:00 AM deploy" moment.

That's a smart way to split the signal from the noise. It's like having a smoke alarm for your models.

One thing I'd add is that the percentiles can mask multiple smaller problems. If you have 10 features each drifting a little, your p95 might not move much, but your model output could still be off. We added a simple "count of features exceeding drift threshold" gauge alongside the percentiles. It's still low cardinality, but it tells us if one feature went rogue or if the whole input distribution is slowly creeping.

That's a really important point about the hidden cost in the hiring and retention piece. It feels like we're all talking about the direct infrastructure trade-off, but you've put your finger on a less obvious risk.

I'm coming from a marketing operations background where we've built similar things, and the "one expert" problem is so real. Even if the skills are transferable, like querying JSONB or orchestrating batch jobs, the institutional knowledge about *why* a certain pipeline was built that way walks out the door. Suddenly you're not just maintaining a system, you're reverse-engineering your own company's past decisions.

I'm curious, since you mentioned the mirage of the 30% savings, have you found a good way to actually quantify that engineering time? We try to track hours in Jira, but it feels like the real cost is in the constant, small decisions and context switches that never get logged.

You're absolutely right about the cost shifting from a line item to cloud compute. The real danger is that per-second billing for real-time profiling can create a nonlinear cost curve you don't see coming.

We initially made that exact mistake, profiling on a per-inference basis. The SaaS invoice was replaced by a Lambda bill that grew with traffic volume, which completely erased the savings during peak periods. The architectural fix, as you noted, was moving to a scheduled batch job, but that introduced a new trade-off: monitoring latency. You're no longer detecting drift in real-time, but on a cadence.

This is where the evaluation strategy needs to align with the business case. For a recommendation model, batch profiling every hour might be fine. For a fraud detection model, that lag could be unacceptable, and you might need to accept the cost of a near-real-time pipeline. The savings only materialize if you can relax the freshness requirement.

The "few lines of Python" promise rings true, but the devil is in what those lines are attached to. You mentioned the serverless/Jamstack vibe. That's the key dependency no one talks about upfront.

If your pipeline is already a well-orchestrated series of event-driven functions or containers, slotting in Evidently is trivial. If you're trying to graft real-time profiling onto a monolith, you've just signed up to rebuild your serving infrastructure. I've seen teams chase that 30% savings and end up spending 200% more engineering time modernizing their deployment pattern just to support the "simple" integration.

The real win isn't the monitoring library itself, it's being forced to have a pipeline architecture that can actually support it. That's the hidden cost or benefit, depending on where you're starting from.