Why observable evidence matters more than vendor demos
I learned the hard way: shiny demos and PowerPoint architecture diagrams do not survive real customers, peak traffic spikes, or the small edge cases that create catastrophic reconciliation errors. After 47 failed projects with composable commerce vendors, a single public case study changed how I evaluate every vendor. That case study included raw traces, reproducible load profiles, and a failure playbook - not just a success graph. It made me realize the difference between marketing claims and observable, verifiable evidence.
If your selection process still relies on vendor-provided screenshots or isolated KPI summaries, you are leaving hidden risk on the table. This list walks through the concrete, repeatable checks and experiments that turn vendor assertions into provable outcomes. Each item is based on real failures I encountered - missing event guarantees, silent schema drift, expensive operational hand-holding - and methods you can use to expose them through public case studies or vendor proofs. Read this as a checklist you can use during procurement, pilots, and production audits.
Lesson #1: Require public, reproducible case studies with raw metrics
Vendors often publish case studies that celebrate conversion lifts and faster checkout times. Those claims are worthless unless they publish the underlying measurements and how they were collected. A public, reproducible case study includes: the raw event exports (anonymized), the data collection scripts, and a description of the test environment. Look for timestamps, request IDs, and versions so you can validate sequences and causality.
Ask for a Git repository or artifact bundle that contains load test scenarios, the exact feature flags used, and the scripts to replay the traffic. Example: a vendor claimed 30% faster cart completion; the reproducible bundle showed they only enabled a server-side cache for logged-in users and excluded guest checkout traffic. That nuance matters. If a vendor refuses to provide reproducible artifacts, treat their claims as unverified. Public reproducibility reduces the chance the vendor's "success" was the result of an environment tailored to their product alone.
Lesson #2: Validate event and trace-level evidence - not just dashboards
Dashboards show aggregates. Aggregates can hide intermittent, high-impact failures. I now insist on trace-level evidence: distributed traces that link frontend actions to backend events, with propagation of a correlation ID across services. Look for W3C traceparent headers, consistent x-request-id values, and a timeline that shows retries, latency spikes, and error codes. The presence of traces is not enough; the case study should document sampling rates, retention windows, and query filters used to produce summary charts.
One failed project involved a payment reconciliation bug that only appeared when network latency exceeded 250 ms for a specific gateway. The vendor's dashboard averaged latency over 5-minute windows, masking the tail. Trace-level data showed the tail latencies and the retry storm that led to duplicate orders. If a public case study includes raw traces, you can run your own queries and confirm whether the vendor's instrumentation would catch your edge cases. Ask for logs tied to traces so you can inspect payloads and error messages - those are where real fixes begin.
Lesson #3: Test composability scenarios with contract tests and chaos experiments
Composable commerce means many independent components must interoperate reliably. Contract testing and chaos engineering expose integration brittleness before production. Demand that vendors publish their contract tests (Pact files or equivalent) and examples of consumer-driven contracts. A public case study that shows passing contract tests is useful. A stronger case study will include a history of contract violations and how the vendor handled versioned schema changes.
Run chaos experiments during pilots. Examples: deliberately delay inventory service responses by 500 ms, simulate duplicate event delivery, or flip a feature flag during peak traffic. The vendor should provide a failure playbook that shows how their system behaves under those conditions and how they maintain data integrity. In my experience, vendors who provide documented chaos results are clearer about eventual consistency trade-offs and the operational work required to reconcile state across systems. If a vendor cannot or will not tolerate these experiments, expect surprises later.
Lesson #4: Measure hidden failure modes - reconciliation, duplication, and eventual consistency
Many public case studies tout throughput and latency, but real commerce workloads fail in subtler ways. Reconciliation errors between order systems and payment processors, duplicated shipments, and stale inventory are business risks. Require case studies that reveal reconciliation processes: how long state lags, the frequency evaluating governance frameworks of compensating transactions, and the known classes of duplication. Insist on sample reconciliation runs and their diffs so you can see concrete mismatches.
One vendor papered over issues by reporting "99.9% successful fulfillment" while ignoring a 0.6% duplication rate that cost the retailer significant logistics overhead. The public artifacts included a reconciliation script and a table of mismatch types - timeouts, webhook delivery failures, partial payloads. Use those artifacts to estimate operational cost: if your margin is thin, even small duplication rates multiply into real losses. Demand information on idempotency guarantees, deduplication windows, and the approach to eventual consistency so you can map these failure modes to business impact.
Lesson #5: Demand reproducible CI/CD artifacts and SLO-backed guarantees
Composable systems are only as reliable as their delivery pipelines. Vendors should publish their CI/CD artifacts or at least the pipeline configuration used during the case study runs. That includes build manifests, container images with tags, deployment manifests, and the rollout strategy (canary percentages, health checks). A case study that demonstrates a seamless upgrade over a live holiday load is gold, but only if the vendor provides the pipeline used to achieve it.
Service-level objectives (SLOs) and error budgets should be explicit. Public case studies should show historical SLO adherence, incident timelines, and post-incident reviews. Ask for a table of service metrics: availability, p95 latency, mean time to recovery (MTTR), and incident categories. I rejected vendors who offered vague uptime promises without documented error budgets or a realistic rollback plan. If a vendor cannot commit to measurable and auditable SLOs, you inherit their ops debt.
Your 30-Day Action Plan: Turn these lessons into verifiable evidence
Use this action plan to convert the lessons above into procurement and pilot requirements. Below is a prioritized, day-by-day checklist you can use with vendors during discovery and the pilot phase. https://suprmind.ai/hub/ Each step has an outcome you should accept as mandatory evidence before moving forward.
- Request reproducible case study artifacts: raw event exports, load test scripts, and a Git repo reference. Ask for trace and log samples with correlation IDs and documentation of sampling strategy. Require CI/CD pipeline manifests and container image tags used in the case study.
- Replay a subset of the vendor's load profile against a sandbox instance using their scripts. Verify that the traces and logs match the vendor's published results and note any deviations. Run contract tests (or require the vendor to run them with you) and review failed contracts.
- Introduce controlled failures: latency injection, dropped messages, and duplicate delivery. Exercise rollback and recovery playbooks while monitoring reconciliation outputs. Record all incidents and compare to the vendor's incident timelines in their case study.
- Run reconciliation jobs and compute mismatch rates. Translate mismatches into operational hours and cost. Request historical SLO adherence and MTTR; validate against your synthetic incident data. Negotiate explicit error budgets and escalation paths based on your business tolerance.
- Require contract clauses that mandate reproducible artifacts for major upgrades and the right to run chaos tests in production windows. Include observability acceptance criteria: required traces, sampling, and retention for at least X days. Publish a short internal case study documenting your pilot artifacts and results so you have your own reproducible record.
Quick self-assessment quiz
Score yourself to see how rigorous your current evaluation process is. Give 2 points per "yes", 0 per "no".
- Do you require reproducible artifacts for vendor case studies? (Yes / No) Can you access trace-level data from the vendor's sandbox? (Yes / No) Do you require contract tests to be published or run jointly? (Yes / No) Do you conduct controlled chaos tests during pilots? (Yes / No) Are SLOs and error budgets explicit in vendor materials? (Yes / No)
Score interpretation:
- 8-10: Strong process. You are well-positioned to spot vendor claims that won't hold under real traffic. 4-6: Partial coverage. Tighten requirements around trace-level evidence and contract testing. 0-2: High risk. Move away from accepting marketing collateral as proof. Insist on the artifacts listed above.
Mini self-assessment checklist (copy into your RFP)
Requirement Acceptable Evidence Reproducible case study Git repo or artifact bundle with load scripts, raw exports, and step-by-step replay guide Trace-level visibility Sample traces with correlation IDs, sampling rates, and retention policy Contract tests Pact (or similar) files and historical contract violation logs Chaos/Failure playbook Documented experiments, expected behavior, and recovery steps SLOs and incident history Tabulated SLO adherence, MTTR, and post-incident reviewsAfter 47 failed projects, I no longer buy vendor stories. I buy evidence that I can run in my own environment or audit publicly. If a vendor refuses to provide the artifacts above, treat their published success as promotional material - not engineering proof. Use the 30-day plan and the checklists to force transparency early. You will save time, money, and avoid the expensive surprises that I paid for the hard way.
