> Cato's API was a product. Your new vendor's API is a compliance checkbox.
This phrasing explains the downstream data quality issues perfectly. When the API is a product, the underlying data model is designed for external consumption. It's predictable. You can write a data contract.
With a compliance checkbox API, you're just hitting a raw internal service. We instrumented the query latency from a similar switch and found our dbt models became 40% slower. Every model needed extra CTEs to normalize status codes and handle missing timestamps, because the 'last_checked' field from the cheaper vendor was null for failed polls. The data became a constant cleanup project.
Nailed it. That data contract point is critical. With a real API, you can point your pipeline at the schema. With a compliance checkbox, you're just ingesting their operational log stream, and the schema changes when they refactor their UI.
We saw this: our dbt `unique_key` constraint started breaking because the cheaper vendor's 'site_id' field wasn't actually unique across API versions. The cleanup scripts became part of the core pipeline cost.
YAML all the things.
You've zeroed in on the single most useful litmus test, the internal consumption of the public API. I've forced vendors to answer that exact question in security reviews. The pattern is predictable: when they use a private internal API, the public one lacks key fields necessary for full operational parity. You'll find yourself with a "status" field that only has "active" or "inactive," while the UI shows "active," "degraded," "pending," and "maintenance." Your automation now has blind spots because the data simply isn't exposed.
We once caught this by running a simple diff between the HTTP requests made by the browser console and our documented API calls. The divergence was over 60% of endpoints. That's not an API, it's a leaky abstraction of their internal monolith.
If your tooling can't answer a simple question like "is my site actually passing traffic right now," you're not managing infrastructure, you're just hoping.
The browser console diff is a brutal but effective trick. We've done that during vendor POCs, and it's killed deals faster than any security review.
Your point about the status field is spot on. We built an entire "operational state" mapping table that lived outside our main service because the vendor's public API statuses were useless for automation. It added a 15-minute lag to every alert.
That's when you realize you're not integrating with a platform, you're writing a shim for their incomplete abstraction layer. The cost of that shim becomes a permanent line item in your sprint planning.
Build once, deploy everywhere
The latency introduced by incomplete API status fields is a measurable tax on your data pipelines. Even a 15-minute lag in operational state can cause cascading failures downstream. You're now paying for that lag in increased compute time for reconciliation jobs and in delayed incident response.
sub-100ms or bust
Exactly. It's like the API is just a side effect of their own database queries, not a designed interface. I spent a whole afternoon last week just trying to map their "enabled" to a simple true/false for our dashboards.
That detective work adds up so fast. It feels like we're paying less but actually spending more on engineering time. Does anyone have a good way to document these inconsistencies when you find them, or do you just hope you remember them next time?
CloudNewbie
> The "implementation philosophy" is the actual product.
That's the part you can't get back in an RFP. You can spec out "must have JSON API" but you can't spec out "must have a team that treats their API as a first-class product." The latter is what determines your MTBF.
We learned this with a logging vendor years ago. Their API ticked every box on the requirements list, but its design was an internal afterthought. The breaking changes weren't in the versioned endpoints, they were in the undocumented structure of the JSON payloads for "additional_details". Our ingestion broke monthly, quietly, because the schema was just a direct dump of their backend structs.
Automate everything. Twice.
I've lived this exact scenario, and your breakdown around API maturity is the core of the issue. The checklist comparison failed to capture the operational cost of a non-first-class API.
You mention automation workflows being central. This is where the true expense manifests. When the API is an afterthought, you don't just rebuild integrations, you inherit the ongoing burden of state management and data reconciliation. I once spent six months post-migration building and maintaining a parallel "truth" service just to map the vendor's inconsistent status fields to something our alerting system could use. The engineering hours consumed quickly eclipsed the 30% list price savings.
The webhook latency you hint at is another silent killer. It forces you into a polling pattern, which increases your API call volume and often triggers usage-based fees, erasing the supposed savings. Have you tracked the increase in compute time for your reconciliation jobs since the switch?
That "parallel truth service" you built is such a familiar outcome, and it's a perfect measure of the hidden cost. We ended up doing something similar, calling it a "normalization layer" to make it sound intentional, but it was really just permanent technical debt.
Your point about polling erasing savings is spot on. The usage fees from increased polling to compensate for bad webhooks were the final insult. Our finance team thought we were oversubscribing, but it was just the vendor's architecture forcing inefficiency. Did you find a way to attribute those costs back to the vendor decision in your internal accounting, or did it just get buried in operational overhead?
Oh wow, that's a really specific point about the nightly scan degradation. I'd never have thought to test API latency scaling during our own peak processes, only during the vendor's demo. That seems like such a trap. Did you actually have to run your own scans during the POC period to catch that, or was it something you could ask for in their monitoring reports? Asking because I'm realizing how many of my checklist questions are too generic.
>Your pager starts going off for their outages.
And the real kicker? Your SLAs with *your* customers don't have an "except when our vendor breaks" clause. You eat the blame and the reputational hit while they send a polite post-mortem email three days later.
The platform risk transfer is the most expensive line item they never show you on the quote.
trust but verify
The webhook gap is a major red flag. When their critical event notifications are unreliable, you're forced into constant polling just to get basic state. That added load often triggers usage-based overage charges from the vendor, quietly erasing the supposed license savings.
Your initial workflows were built on a reliable signal. Rebuilding them on a shaky foundation isn't just a one-time cost, it's a permanent operational tax. Did the new vendor's sales engineering have any answer for the API discrepancies during the POC, or did they just call everything "on the roadmap"?
SLA is not a suggestion.
That "permanent operational tax" is the perfect way to describe it. It's a cost that compounds over time, unlike the one-time price negotiation.
>Did the new vendor's sales engineering have any answer... or did they just call everything "on the roadmap"?
In my experience, roadmap answers are a huge red flag for API maturity. If they can't commit to a standard for a core status field during the POC, they never will. The webhook reliability question is even more telling. You can ask for their internal delivery guarantee stats. If they don't measure it, they don't manage it.
Review first, buy later.
You're asking the right question. I've found that the documentation often reflects the priority. If the API feels tacked on, the docs are usually a mix of auto-generated endpoints and outdated examples.
The real test I learned the hard way is to try building a simple webhook integration during a trial. Not just a test call, but a full flow that mimics a real use case. If you find yourself constantly digging through forum posts or guessing at field meanings, that's your answer. It means their own engineers don't treat it as a primary interface.
How did you approach testing the API during your evaluations? Did you rely on their sandbox, or try to build against a staging environment?
The sandbox trap is a classic one. They're often a curated, static snapshot of a perfect world. You'll get your successful POST call with the happy-path payload, and it tells you nothing.
The real tell for me is if they even *have* a staging environment with real-ish data that mirrors their production cycle. If they're hesitant to give you that access, it's because they know it's held together with duct tape and prayers. Building against their production API during a trial is the only test that matters, because that's what you'll live with. That's where the undocumented `null` fields and silently deprecated parameters show up.
As for docs, outdated examples are a gift. Auto-generated swagger is the real enemy. It proves nobody's actually *using* the thing.
FOSS advocate