When running a proof-of-concept 'bake-off' between two or three shortlisted vendors' products, what design choices keep the comparison fair, and what commonly makes POC results look good in the trial but misleading once the product is actually in production?

## What a bake-off is A POC bake-off is a **time-boxed, hands-on trial** where two or three shortlisted vendors' products are run against the same realistic workload so their actual behavior — not their sales deck — can be compared directly. Designing it fairly starts before any vendor is engaged. The evaluation team defines: - the specific use cases the POC must exercise (e.g., ingest a representative dataset, run specific query patterns, integrate with a specific auth provider) - the pass/fail or scored criteria for each - the timebox (commonly two to four weeks per vendor, or run in parallel if resources allow) Every vendor gets the **identical dataset, identical use cases, identical timebox, and identical level of allowed vendor involvement** (e.g., standard documentation plus one weekly office-hours call, no dedicated implementation engineer embedded full-time) — because letting one vendor's sales engineer live in your Slack for three weeks while another gets a support ticket queue produces a result that measures vendor generosity, not product fit. ## Why run one at all The bake-off exists because **vendor claims and demos are optimized for the sale, not for truth**. A scripted demo shows the product's best path through curated data; a reference architecture diagram omits the workarounds a real customer needed. Hands-on trial with your own data and your own integration points is the only way to surface how the product actually behaves against your specific constraints — your schema quirks, your auth provider, your query patterns — none of which a generic demo will expose. It also de-risks the decision before signing a multi-year contract: catching a fundamental incompatibility during a three-week POC costs far less than catching it six months into a production rollout. ## What rigour costs The cost of a rigorous bake-off is real: - Running parallel POCs with two or three vendors consumes weeks of engineering time from your own team, since someone has to build the test harness, prepare the representative dataset, and evaluate results for each vendor. - Vendors often push back on a fully fair setup because it removes their ability to differentiate through service quality, which some legitimately consider part of the product. - A shorter or single-vendor POC is cheaper but produces a weaker signal — without a comparison baseline, 'the product worked' tells you little about whether a competitor would have worked better, faster, or cheaper. - Teams under schedule pressure often compress the bake-off to the point where it barely exceeds what the vendor's own demo would have shown, defeating its purpose while still consuming a few weeks. ## Why the POC flatters and production disappoints The recurring failure is that POC results look great and production results disappoint, for a consistent set of reasons. 1. First, the **'concierge effect'**: vendor sales engineers heavily hand-hold the POC — tuning configs, writing custom scripts, working around known bugs live — support no real customer gets once the account moves from sales to a support queue post-signature. 2. Second, **scale and data-quality mismatch**: POC datasets are small and clean (curated exports, not years of accumulated production mess with nulls, duplicates, and schema drift), so performance and data-handling problems that only appear at real volume or with real dirty data never surface. 3. Third, **time horizon mismatch**: a two-week POC can't reveal problems that only show up after months — upgrade friction, support responsiveness on a real incident, behavior after data grows 10x, or how the vendor handles a breaking API change. 4. Fourth, **integration cherry-picking**: POCs often exercise the easy, well-documented integration paths and skip the hardest one, precisely because it's hard to stand up quickly — and that's exactly the integration that turns into the actual production bottleneck. ## A worked example A team evaluating two API-gateway vendors runs a three-week bake-off. Every vendor gets: - the same synthetic-but-representative traffic replay based on sanitized production logs - the same requirement to integrate with the company's existing OIDC provider - the same 'one office-hours call per week, ticket queue otherwise' support rule to simulate post-sale reality Vendor A's sales engineer initially offers to 'just hop on a call and get it working' outside the agreed support rule; the evaluation lead declines and holds the boundary, which turns out to matter — Vendor A's product needs a nonstandard OIDC claim-mapping workaround that only the sales engineer knew about, a gap that would have bitten the team in production support. Vendor B's product integrates cleanly through documented config. That single data point, which a looser bake-off would have hidden behind unlimited vendor hand-holding, becomes a deciding factor precisely because the process was designed to simulate real post-sale conditions rather than the best-case sales-supported conditions.