Guide: Avoiding You.com's tendency to hallucinate pricing info for software.

Your point about database services is exactly where the hallucinations hurt the most. The pricing for managed Postgres is a minefield of instance types, IOPS, multi-AZ deployments, and backup retention. Getting one piece wrong changes the total by 2-3x.

Your specific, time-bound query tip helps, but it still fails for anything with a complex, conditional cost structure. I've seen it generate a perfect-looking RDS cost breakdown that used the deprecated "db.m4.large" and omitted Provisioned IOPS entirely. It looked convincing because the format was flawless.

For any cloud service, my rule is to never accept a unit cost without building the actual configuration in the provider's calculator and exporting the estimate. Even that's just a snapshot. The only true source is the invoice from a small, controlled pilot project.

You've nailed the core misuse. Treating it as a research tool assumes a foundation of verified facts to retrieve, which doesn't exist for dynamic commercial data. Its strength is synthesizing a coherent narrative from patterns, which is precisely why the resulting feature lists and pricing tables feel so dangerously authoritative.

The "better hallucination" problem extends to integration scenarios. I've seen it generate flawless-looking API rate limit tables and webhook payload schemas for CRM platforms that were accurate to a previous major version. The structure is perfect, the field names are plausible, but the "added in v2023.2" flag is missing from a critical webhook event, leading to a sync design that fails in staging.

Your blank spreadsheet rule is the only sane methodology. It forces you to treat each data point as an unverified hypothesis until you manually correlate it against a primary source at a specific point in time. Even then, as others have noted, the primary source itself is often a moving target.

Yeah, the database service pricing is a perfect example of where this falls apart. Your tip about time-bound queries is smart, but even that can't handle the conditional logic of cloud pricing.

I've been burned the same way looking at data pipeline tools. You.com will give me a neat table comparing Fivetran and Airbyte, with per-active-row prices that look right, but it'll miss the entire credit-based consumption model one of them switched to last quarter. The format is so convincing.

My only fix is to never let a generated cost enter my spreadsheet. I have to manually configure a trial in the vendor's own calculator and screenshot it. Even then, like you said, it's just a snapshot. Makes you wonder if the real primary source is just your own last invoice.

The credit-based model shift is such a perfect trap. It's not just missing a number, it's missing the entire cost structure. Happened to me recently with a CDP platform, where the model confidently described a per-seat license long after they'd moved to event-volume pricing. The table looked flawless, so it slipped into an early deck.

Your point about the *format* being convincing is everything. A neat table with plausible numbers creates this immediate credibility. It feels like you've done the work.

Makes me think the only real use for the tool here is as a reminder to ask the vendor, "Has your pricing model changed from a flat subscription in the last 12 months?" The hallucination becomes a checklist of what *not* to trust.

Your specific example with managed Postgres pricing is the critical failure mode. Even your time-bound query fix falls apart because cloud pricing isn't a static list, it's a function of dynamic resource selection and commitments.

The only reliable method is to treat the official pricing page as merely the specification for a parameterized query, which you must then execute yourself in the provider's calculator. For your MySQL to Postgres migration, the real cost isn't a monthly price for a plan, it's the output of configuring vCPU, memory, storage type, IOPS, backup retention, and multi-AZ replication in the AWS RDS, GCP Cloud SQL, or Azure Database calculators. An LLM cannot perform that calculation because it lacks the live pricing engine and the context of your actual workload requirements.

Your corroboration step must be active configuration, not passive reading. The hallucinated "discontinued plan" is a symptom of the model retrieving and blending outdated text artifacts. It cannot access the real-time product catalog or execute the pricing logic.

Oh, the "snapshot of a fleeting moment" is such a good way to put it. I got caught by that with an email automation platform last year. The model described a perfect mid-tier plan that included dedicated IPs, which was true... for about a month when they were testing a new package. By the time I was building the budget, that feature had been moved to the enterprise tier only.

It makes you realize these models aren't just pulling from stale data, they're often pulling from temporary marketing or abandoned A/B tests.

You're treating the symptom, not the disease. The problem isn't just verifying the starting point, it's relying on any starting point the tool gives you for commercial terms. Your own example proves it: the technical specs were fine, the pricing was fiction. That means the model is perfectly capable of sourcing accurate data, it just doesn't prioritize commercial accuracy because there's no consequence for being wrong.

The real failure is using it for this task at all. Procurement isn't about finding a number, it's about understanding the structure that generates the number. A time-bound query might get you last year's list price, but it will never capture the shift to a credit model, the future price increase baked into the contract, or the negotiated discount you could actually get. You're polishing a guess.

Start with a blank spreadsheet and the vendor's current public calculator. Anything else is building on sand.

Totally feel this. That "starting point" approach is exactly right, but I've found the primary source isn't just the vendor's main pricing page. For something like managed Postgres, you often need to dig into the *calculator* itself, because the listed "instance price" is meaningless without storage, IOPS, and backup configs. The LLM will often grab that headline instance cost and present it as a monthly total, which is the most dangerous kind of hallucination - it's a real number, but it's only 30% of the actual cost.

Your time-bound query trick is clever for getting a static snapshot. I've tried similar phrasing like "as documented in their 2023 pricing blog post." It sometimes works to anchor the model to a specific page, but then you're just getting old data, which has its own problems for planning.

Honestly, for any cloud service now, I skip the chatbot for pricing entirely and go straight to building a dummy config in the provider's calculator. It's the only way to capture the conditional logic. The chatbot's value is in summarizing the *trade-offs* between, say, provisioned vs. burstable performance, which you can then go and price out manually.

That's not an AI problem, that's a sales problem. The finance team and the sales deck disagree because the pricing model is designed to be ambiguous. The vendor wants that wiggle room for negotiations and future changes.

The true primary source isn't a document, it's the executed contract's pricing appendix. Everything before that is marketing. An AI will always find the prettier marketing version because that's what's published.

Exactly! The starting point vs final answer distinction is so important. I've seen that same managed Postgres trap - it'll give you the base instance cost and leave out the storage, IOPS, and backup fees that easily double the price.

Your time-bound query tip is a good workaround, but I've noticed it can still pull from archived press releases or old comparison articles. Now I add "current public pricing page" to my prompts, which nudges it slightly better.

Honestly, for any serious budget, I've stopped using it for numbers altogether. I'll ask for a feature matrix or technical comparison, then manually fill a spreadsheet from the vendor's calculator. It's more work, but at least the budget doesn't blow up later.

>Honestly, for any serious budget, I've stopped using it for numbers altogether.

This is the conclusion I keep coming back to. I was trying to use it to get a quick sanity check on Snowflake vs BigQuery storage costs, and it gave me a beautifully formatted table with numbers that looked plausible. When I went to the actual pricing pages, the structure was so different - it's all about compressed storage and per-second compute. The model had just averaged out some outdated blog post.

Now I do the same as you: use it for the initial list of features or vendors to consider, then close the browser tab before opening a single spreadsheet. The temptation to copy-paste that clean table is too strong, even when I know better.