Vaporware in Production: How Press-Release Products Break Real Systems

What vaporware really is (and what it isn’t)

Vaporware is a product or feature announced publicly without a reliable, verifiable path to delivery. Sometimes it never ships. More often it ships as something narrower, slower, less integrated, or less supportable than the announcement implied. The key point is not “it was late.” The key point is you were asked to make decisions as if it already existed.

There’s a difference between:

• Normal slippage: a feature exists internally, has working code, and the vendor is dealing with QA/regulatory/support readiness.
• Strategic vapor: the feature is announced primarily to freeze a market, stall churn, or blunt a competitor’s launch—without an implementation that could realistically meet the claim.
• Accidental vapor: engineering believed it was feasible, then physics, complexity, or integration reality showed up with a chair and sat down.

From an SRE perspective, the label matters less than the behavior: treat unshipped functionality as non-existent. Not “probably soon.” Not “beta.” Not “we saw a demo.” Non-existent until you can run it, observe it, and support it.

Dry truth: a vendor demo is a performance, not a measurement. Your job is to turn it into a measurement.

Joke #1 (short, relevant): The roadmap is the only artifact in tech that gets less accurate the more colorful it is.

Why vaporware keeps happening in corporate IT

Vaporware is not just “marketing being marketing.” It’s an equilibrium created by incentives:

1) Announcements are cheaper than delivery

A press release costs almost nothing. Shipping a reliable feature costs engineering time, QA, docs, support training, operational tooling, and—worst of all—ongoing maintenance. If leadership can get revenue impact from the announcement alone, the temptation is obvious.

2) Buyers often buy narratives, not capabilities

Procurement workflows reward comparability. Vendors respond with checkboxes. Checkboxes reward grand claims. And suddenly “multi-region active-active, zero RPO” is a line item next to “supports SNMP.” Reality is not that neat.

3) Technical teams are pulled into pre-commitment

The most dangerous moment is when leadership asks engineering: “Can we assume this will exist by Q3?” That’s when architecture becomes a hostage to someone else’s calendar.

4) The corporate immune system is weak against optimism

Optimism sounds like momentum. Skepticism sounds like “blocking.” In many orgs, the skeptic is correct and still loses—until production makes them right in public.

5) Integration is where promises go to die

Most vaporware claims aren’t impossible in isolation. They are impossible when combined with everything else the product already does: encryption, snapshots, replication, quotas, multi-tenant isolation, telemetry, compliance logging, and “works with our identity provider.”

Historical facts and context you can actually use

Some context helps because vaporware repeats patterns. Here are concrete points worth remembering (not nostalgia, just practical pattern recognition):

1. The term “vaporware” spread in the early 1980s in the personal computing press, as vendors pre-announced products to stall competitors.
2. Pre-announcements became a competitive weapon once software distribution and updates were slow; capturing mindshare early mattered as much as shipping.
3. The 1990s enterprise era normalized “futures” selling: large contracts were negotiated on roadmap commitments, not just current capability.
4. Antitrust attention sometimes increased vendor caution around pre-announcements, but it never eliminated them; the practice just got more carefully worded.
5. The cloud era revived vaporware in a new form: “region coming soon,” “service preview,” and “waitlist” features that are used in architectures long before they’re GA.
6. Storage vendors historically over-promised on dedupe and compression ratios, because workloads vary and synthetic benchmarks make easy lies.
7. “Zero downtime migration” has been a recurring claim for decades; real migrations are constrained by application behavior, not just storage plumbing.
8. Security roadmaps are a frequent vapor hotspot: “immutable backups,” “air-gapped recovery,” and “ransomware protection” often arrive incomplete or operationally brittle.
9. Open standards have been used as marketing shields: “S3-compatible” and “Kubernetes-native” can mean wildly different levels of compatibility.

Notice the theme: vaporware thrives where verification is hard. If you can’t test it quickly, you’re more likely to be sold a story.

Operational failure modes: how vaporware breaks systems

Failure mode A: Architecture depends on unshipped primitives

The most expensive mistake is when you build a design that requires a particular feature to exist—cross-region writes, consistent snapshots, transparent key management, whatever—and then discover the real product can’t do it, or can’t do it under your constraints.

Diagnosis pattern: the “temporary workaround” becomes permanent and grows teeth: cron jobs, fragile scripts, manual steps, exceptions in audits, and Rube Goldberg failovers.

Failure mode B: “Beta” is treated like a supported feature

Beta is not a stage of maturity; it’s a stage of liability. Vendors will often say “limited support.” In practice it means: limited on-call, limited bug-fix urgency, and documentation that reads like it was written during a taxi ride.

Failure mode C: Non-functional requirements get dropped

The announcement covers throughput, but not observability. Or it covers encryption, but not key rotation. Or it covers “immutable,” but not legal hold, access logging, or recovery workflows. Production requires the boring edges.

Failure mode D: Integration debt goes straight to ops

When a vendor says “integrates with your ecosystem,” your ecosystem is always bigger than they mean. Identity, networking, DNS, proxying, audit, ticketing, backups, monitoring, cost allocation. The gap becomes your glue code. Glue code becomes your pager.

Failure mode E: Procurement locks you in before reality arrives

Multi-year commitments based on future features are classic. By the time the feature slips or under-delivers, you’re already migrating data into the platform, training staff, and rewriting runbooks. Switching costs are now a cage.

Here’s the operational axiom: if a product claim cannot be validated in your environment with your data path, it is not a capability—it is a risk.

One quote, because it survives audits and budget meetings. Kim (Gene) Kim’s idea is frequently expressed like this; I’m paraphrasing to stay honest:

Paraphrased idea (Gene Kim): Reliability is not a feature you bolt on later; it’s a property you design and run into the system from the start.

Fast diagnosis playbook: find the bottleneck fast

This playbook is for the moment when “the new platform” is deployed and something is off: latency spikes, throughput collapses, failover doesn’t fail over, or a promised feature behaves like a rumor. You need a fast answer: is this the application, the host, the network, the storage, or the product’s missing piece?

First: confirm what is actually deployed

• Check versions, enabled modules, license state, and feature flags. Half of “vaporware incidents” are “it exists but not in your SKU/region/build.”
• Verify that you’re not reading marketing docs while running last quarter’s firmware.

Second: identify the dominant symptom

• Latency (tail latency especially) usually means contention, queueing, or sync writes.
• Throughput ceilings often mean a single choke point: NIC bonding, one busy core, one queue depth, one gateway node.
• Consistency anomalies usually mean caching layers or replication semantics that are weaker than assumed.
• Failover failures often mean split-brain prevention is doing its job—at the cost of availability—or your dependencies weren’t included.

Third: test the data path in isolation

• Benchmark local disk vs network storage vs object gateway.
• Measure with tooling that shows IOPS, latency distribution, and CPU usage.
• Always capture the command, the output, and the environment details. Otherwise you are arguing with vibes.

Fourth: map the “promised feature” to observable behavior

• “Immutable” means you should be unable to delete or overwrite objects/blocks within a retention period—even as an admin.
• “Active-active” means two independent write paths without a hidden primary.
• “Zero RPO” means you can kill a site mid-write and still have the write committed elsewhere with defined semantics.

Fifth: decide quickly—fix, workaround, or rollback

If the product can’t meet a non-negotiable requirement, don’t “optimize.” Roll back and renegotiate. Optimization is for systems that are fundamentally correct. Vaporware is fundamental incorrectness wearing a blazer.

Practical verification tasks (with commands)

These are real tasks you can run during due diligence, POCs, or incident response. Each includes: command, what the output means, and the decision you make. They’re Linux-centric because production is.

Task 1: Confirm kernel and OS basics (avoid phantom perf bugs)

cr0x@server:~$ uname -a
Linux app01 6.5.0-14-generic #14-Ubuntu SMP PREEMPT_DYNAMIC x86_64 GNU/Linux

Output meaning: Kernel version impacts NVMe, TCP stacks, io_uring, and filesystem behavior. “The vendor tested on 5.4” matters if you’re on 6.5.

Decision: If your kernel differs significantly from vendor’s stated support matrix, either align versions for the POC or treat results as non-reproducible.

Task 2: Verify product version and build (marketing loves ambiguity)

cr0x@server:~$ cat /etc/vendor-storage/version
VendorStorageOS 3.2.1-build.4587

Output meaning: You’re running a specific build; “3.2” in slides is not a build number.

Decision: If the promised feature requires “3.3+”, stop the architecture discussion until you can run that version.

Task 3: Check license or entitlement state (features vanish here)

cr0x@server:~$ vendorctl license show
License: ENTERPRISE
Features:
  replication: enabled
  immutable-snapshots: disabled
  s3-gateway: enabled
  kms-integration: enabled

Output meaning: The feature isn’t “missing,” it’s disabled by license. That still counts as “not available to you.”

Decision: Either upgrade entitlement as part of the contract or redesign assuming the feature does not exist.

Task 4: Verify DNS and routing to the supposed “multi-region” endpoints

cr0x@server:~$ dig +short storage-global.example.internal
10.40.12.10

Output meaning: “Global endpoint” resolves to one IP. That’s not global; that’s a single VIP wearing a trench coat.

Decision: If HA relies on DNS-based failover, demand tested failover times and cache behavior, or move to an anycast/load-balancer approach you control.

Task 5: Observe network path and MTU issues (latency masquerading as storage)

cr0x@server:~$ ip -br link
lo               UNKNOWN        00:00:00:00:00:00
ens160           UP             00:50:56:aa:bb:cc
ens192           UP             00:50:56:dd:ee:ff

Output meaning: Interfaces are up. Doesn’t prove jumbo frames or offloads are correct, but it’s a baseline.

Decision: If you expected a dedicated storage NIC and it’s not present, stop and fix the environment before benchmarking.

Task 6: Check for packet loss on the storage network

cr0x@server:~$ ping -c 20 -i 0.2 10.40.12.10
PING 10.40.12.10 (10.40.12.10) 56(84) bytes of data.
64 bytes from 10.40.12.10: icmp_seq=1 ttl=63 time=0.421 ms
64 bytes from 10.40.12.10: icmp_seq=2 ttl=63 time=0.389 ms
...
--- 10.40.12.10 ping statistics ---
20 packets transmitted, 20 received, 0% packet loss, time 3811ms
rtt min/avg/max/mdev = 0.362/0.401/0.463/0.024 ms

Output meaning: No loss, sub-millisecond RTT—good. If you see loss or jitter, storage will look “slow” because retransmits murder tail latency.

Decision: Fix network loss/jitter before blaming storage or declaring a product “underperforming.”

Task 7: Confirm mount options (sync/async and atime can be silent killers)

cr0x@server:~$ mount | grep /mnt/data
10.40.12.10:/vol0 on /mnt/data type nfs4 (rw,relatime,vers=4.1,rsize=1048576,wsize=1048576,hard,timeo=600,retrans=2)

Output meaning: NFS version and rsize/wsize affect throughput; “relatime” reduces metadata writes.

Decision: If the vendor promised performance on NFSv4.2 and you’re on 4.1, align first. If you see “sync” forced where you expected async, re-evaluate latency expectations.

Task 8: Measure filesystem latency distribution quickly with fio (not just averages)

cr0x@server:~$ fio --name=randread --directory=/mnt/data --rw=randread --bs=4k --iodepth=32 --numjobs=4 --size=2G --time_based --runtime=60 --group_reporting
randread: (groupid=0, jobs=4): err= 0: pid=3121: Mon Jan 22 10:12:01 2026
  read: IOPS=52.1k, BW=203MiB/s (213MB/s)(12.0GiB/60s)
    slat (nsec): min=740, max=190k, avg=3200.1, stdev=4100.8
    clat (usec): min=85, max=21000, avg=510.4, stdev=980.2
     lat (usec): min=90, max=21010, avg=514.0, stdev=982.0
    clat percentiles (usec):
     |  50.00th=[  320],  90.00th=[  910],  99.00th=[ 4500],  99.90th=[14000]

Output meaning: Median looks fine, but 99.9% at 14ms may break your database. Tail latency is the truth; averages are a lullaby.

Decision: If tail latency exceeds application SLOs, the product is not a fit unless you can change workload patterns (batching, caching, write coalescing) or move tiers.

Task 9: Verify discard/TRIM behavior (thin provisioning claims depend on it)

cr0x@server:~$ lsblk -D
NAME   DISC-ALN DISC-GRAN DISC-MAX DISC-ZERO
sda           0      512B       2G         0

Output meaning: DISC-GRAN and DISC-MAX show discard capability. If it’s missing, your thin-provisioning story may be fantasy.

Decision: If discard is required for capacity reclamation and it isn’t supported end-to-end, plan for higher real capacity consumption or change the storage protocol/device type.

Task 10: Check replication status and lag (roadmap “near real-time” vs reality)

cr0x@server:~$ vendorctl replication status
Pair: dc1-vol0 -> dc2-vol0
Mode: async
Last snapshot sent: 2026-01-22T10:10:12Z
Lag: 00:07:41
Queue: 1822 ops
State: healthy

Output meaning: Async replication with 7m41s lag is not “zero RPO,” not “synchronous,” and not “instant failover.” It can still be useful—just not for the story you were sold.

Decision: Decide whether your business can tolerate that RPO. If not, you need a different design (true sync, app-level replication, or a different product).

Task 11: Validate “immutable snapshot” behavior with an attempted delete

cr0x@server:~$ vendorctl snapshot delete --volume vol0 --snapshot snap-2026-01-22
ERROR: snapshot is locked by retention policy until 2026-02-22T00:00:00Z

Output meaning: This is what immutability looks like: the system refuses deletion and tells you why.

Decision: If an admin can delete it anyway, it’s not immutable; it’s “please don’t.” Treat ransomware recovery claims as unproven.

Task 12: Check actual data reduction ratios on real data (not vendor math)

cr0x@server:~$ vendorctl stats datareduction --volume vol0
Logical used: 12.4TiB
Physical used: 9.8TiB
Reduction ratio: 1.27:1
Dedupe: 1.05:1
Compression: 1.21:1

Output meaning: Your data reduces 1.27:1, not 5:1. That’s normal. Vendor claims often assume VDI clones or synthetic data.

Decision: Capacity planning should use measured ratios with safety margins. If the business case required 4:1, the business case was vapor.

Task 13: Find CPU saturation on the client (encryption/compression can move the bottleneck)

cr0x@server:~$ mpstat -P ALL 1 5
Linux 6.5.0-14-generic (app01) 	01/22/2026 	_x86_64_	(16 CPU)

10:14:03 AM  CPU    %usr   %nice    %sys %iowait    %irq   %soft  %steal  %guest  %gnice   %idle
10:14:04 AM  all   62.11    0.00   18.44    1.21    0.00    0.88    0.00    0.00    0.00   17.36
10:14:04 AM   3   96.00    0.00    3.00    0.00    0.00    1.00    0.00    0.00    0.00    0.00

Output meaning: One CPU pegged. That could be single-threaded encryption, a user-space driver, or a gateway process.

Decision: If the bottleneck is client CPU, storage tuning won’t help. You need parallelism, different drivers, or offload.

Task 14: Confirm queue depth and device latency for block storage

cr0x@server:~$ iostat -x 1 3
Linux 6.5.0-14-generic (app01) 	01/22/2026 	_x86_64_	(16 CPU)

avg-cpu:  %user   %nice %system %iowait  %steal   %idle
          58.12    0.00   17.90    2.44    0.00   21.54

Device            r/s     rkB/s   rrqm/s  %rrqm r_await rareq-sz     w/s     wkB/s   wrqm/s  %wrqm w_await wareq-sz aqu-sz  %util
nvme0n1        820.0  52480.0     0.0   0.00    0.62    64.00   210.0  26880.0     0.0   0.00    1.10   128.00   0.35  68.00

Output meaning: r_await/w_await show latency; %util indicates saturation. High await with low util often points to upstream (network, array) latency.

Decision: If %util is low but await is high, stop tuning the host and start testing the storage path and array behavior.

Task 15: Validate that “S3-compatible” actually supports required headers/features

cr0x@server:~$ aws --endpoint-url http://10.40.12.20:9000 s3api put-object --bucket backups --key test.txt --body /etc/hostname
{
    "ETag": "\"9b74c9897bac770ffc029102a200c5de\""
}

Output meaning: Basic PUT works. That doesn’t mean versioning, object lock, or multipart behavior matches your tooling expectations.

Decision: If you need object lock/retention and it’s not available or not compatible with your backup software, don’t accept “S3-compatible” as a substitute for requirements.

Task 16: Test failover mechanics, not just “status: ready”

cr0x@server:~$ vendorctl cluster failover --to-site dc2 --dry-run
Dry-run result:
  Will stop services on: dc1-gw1, dc1-gw2
  Will promote volumes: vol0, vol1
  Will update VIP: 10.40.12.10 -> 10.60.12.10
  Blocking issues:
    - quorum device unreachable
    - 2 clients have active locks on vol0
Exit code: 2

Output meaning: The system is telling you failover will not work right now, and why. This is gold.

Decision: Fix quorum reachability and client lock behavior before you declare HA ready. If lock handling is incompatible with your apps, you need a different failover plan.

Joke #2 (short, relevant): Nothing says “high availability” like a feature that becomes available right after the outage retro.

Three mini-stories from the corporate world

Mini-story 1: The outage caused by a wrong assumption

The company was mid-migration from a legacy SAN to a “cloud-native storage platform” sold as active-active across two data centers. The announcement deck was gorgeous: dual writes, automatic failover, zero RPO. The architecture team designed the new database tier around the idea that any node could write to either site, so maintenance windows would be mostly a myth.

During the POC, a vendor engineer ran a demo where they unplugged a switch and the UI stayed green. Everyone clapped. No one asked what kind of workload was running, or whether the writes were synchronous, or where the actual commit point lived.

Months later, in production, a scheduled power maintenance event hit one data center. The platform “failed over” but the database experienced a wave of commit latency spikes, then errors. The application layer retried aggressively, which created a queue, which increased latency further. Classic self-inflicted storm.

The post-incident reality: the product wasn’t truly active-active for writes. It was active-active for reads and metadata, with a hidden primary for certain write paths. Under the hood, some write operations were acknowledged before being safely committed at the second site. The vendor hadn’t lied so much as spoken in marketing dialect.

The corrective action wasn’t magical tuning. They changed the design: explicit primary site per database cluster, synchronous replication handled at the database level for the critical datasets, and a tested failover procedure that included application quiescing. The vendor feature became “nice when it works,” not “the foundation of correctness.”

Mini-story 2: The optimization that backfired

A different org bought an all-flash array whose upcoming software version promised “inline compression with no performance penalty.” Their CFO loved the capacity story. Engineering loved the idea of fitting more data into the same racks. The plan was to enable compression across the board as soon as the new release arrived.

When the release shipped, they enabled compression globally during a low-traffic window. The first few hours looked fine. Then, as daily batch processing kicked in, latency crept up. The array CPU hit sustained high utilization. The batch jobs ran long, overlapping with interactive workloads. Now everyone suffered.

The array was doing what it was designed to do, but the “no penalty” claim assumed a certain compressibility profile and a certain write pattern. Their workload had bursts of already-compressed data, plus a mix of small random writes. Compression added CPU cost without saving much space, and it amplified the latency tail.

The fix was unglamorous: disable compression for the high-churn datasets, keep it for cold and log-like data, and enforce per-volume policies. They also added performance tests to the change process: if a feature changes the CPU profile, it gets treated like a new hardware dependency.

The lesson: “optimization” features are the most common form of vaporware-adjacent disappointment. They ship, they work, and they still don’t work for you.

Mini-story 3: The boring but correct practice that saved the day

A global company evaluated a new backup platform that promised “immutable backups with instant recovery” on an S3-compatible object store. The vendor pitch focused on ransomware readiness: air-gapped, locked, unstoppable. The security team was ready to sign.

The SRE lead insisted on a dull requirement: an acceptance test script that would run nightly in the POC environment. Not once. Nightly. It would create a backup, attempt deletion, attempt overwrite, attempt to shorten retention, then attempt restore into a sandbox VM. Every run would emit logs, exit codes, and a simple pass/fail summary.

Two weeks in, the nightly run started failing: the “object lock” behavior was inconsistent after a minor upgrade. Sometimes deletion failed (good). Sometimes deletion succeeded (very bad). The vendor initially blamed “misconfiguration.” The script made the issue reproducible and undeniable.

The vendor ultimately acknowledged a bug in how retention was enforced when lifecycle policies were also enabled. Without the boring nightly test, the company would have discovered this bug during a real incident—when the adversary helpfully tests your backups for you.

They didn’t just dodge a bullet; they institutionalized the idea that security claims are operational claims. If you can’t test it, you don’t own it.

Common mistakes: symptoms → root cause → fix

1) “Feature is in the product,” but it’s missing in your environment

Symptoms: UI shows grayed-out settings; CLI returns “unknown command”; docs mention it but your build doesn’t.

Root cause: SKU/licensing, region limitations, or the feature only exists in a newer build than what’s deployed.

Fix: Treat availability as a deployable artifact: verify version + license state in the POC; bake into the contract that delivery includes your SKU and your regions.

2) “Active-active” behaves like active-passive during stress

Symptoms: One site’s gateway nodes are hot; failover causes session drops; write latency increases when both sites are used.

Root cause: Hidden primary for write serialization, quorum constraints, or synchronous semantics only for some operations.

Fix: Demand explicit semantics: where is the commit point, what is acknowledged when, and what happens under partition. Test by inducing partition, not just power-off.

3) Performance claims collapse under real workloads

Symptoms: Benchmarks show high IOPS, but application feels slow; 99.9p latency is terrible; throughput plateaus early.

Root cause: Vendor benchmarks used sequential IO, ideal queue depths, cache-warmed reads, or compressible data. Your workload is mixed and noisy.

Fix: Benchmark with fio profiles that match your app. Compare tail latency and CPU usage. Refuse to sign on “up to” claims.

4) “Immutable” backups can be deleted by someone with the right permissions

Symptoms: Admin role can remove retention, delete snapshots, or change object lock configuration retroactively.

Root cause: Immutability is implemented as policy, not enforcement; or enforcement is scoped incorrectly.

Fix: Validate with destructive tests. Require separation of duties: a distinct security principal for retention policy changes, plus audit logs you can ship off-platform.

5) Migration tooling is promised, but the downtime is real

Symptoms: “Live migration” works for idle volumes, fails for busy databases; cutover requires application freeze longer than planned.

Root cause: Dirty page rate exceeds replication rate; app-level locks; inconsistent snapshot support; or throttling to protect the source.

Fix: Run migration rehearsals with production-like churn. Measure dirty rates, plan staged cutovers, and accept that some migrations require maintenance windows.

6) Observability is an afterthought

Symptoms: You can’t see latency percentiles, replication lag, cache hit rates, or per-tenant utilization without opening a ticket.

Root cause: Vendor shipped core functionality but not operational instrumentation; or metrics exist but aren’t exportable.

Fix: Make telemetry an acceptance criterion. If you can’t export metrics into your monitoring stack, the feature isn’t production-ready.

Checklists / step-by-step plan

Step-by-step plan: evaluate “press-release” features without getting burned

1. Write the requirement as an observable behavior. Example: “After site loss, writes continue within X seconds, and no committed transactions are lost.” Not “active-active.”
2. List non-functional requirements explicitly. Metrics export, audit logs, RBAC, upgrades, rollback, support response expectations.
3. Demand the support matrix and lifecycle policy. OS versions, kernel versions, hypervisors, firmware baselines.
4. Turn every claim into an acceptance test. If it can’t be tested, it can’t be trusted.
5. Run a POC with production-like failure injection. Partition links, kill nodes, fill disks, rotate keys, expire certs.
6. Benchmark with your IO patterns. Tail latency, mixed read/write, realistic dataset sizes, cache-cold runs.
7. Verify operational workflows. Upgrade procedure, restore procedure, user provisioning, incident escalation.
8. Negotiate contracts on delivery, not intention. Tie payments or renewals to tested capability, not roadmap slides.
9. Design an exit strategy on day one. Data export formats, migration tooling, dual-write period.
10. Keep the fallback architecture viable. Don’t delete the old platform until the new one passes months of boring tests.

Decision checklist: when to walk away

• The vendor can’t state precise semantics (RPO/RTO, consistency model, commit behavior).
• The feature requires “professional services customization” to be usable.
• Telemetry and auditability are missing or proprietary-only.
• The POC requires unrealistic conditions (special hardware, private builds, hand-tuned configs) that won’t exist in production.
• The vendor won’t put delivery criteria into writing.

Contract checklist: stop paying for vapor

• Acceptance criteria: written tests and pass/fail definitions for key features.
• Delivery windows with remedies: service credits, right to terminate, or delayed payment schedules.
• Support obligations: response times, escalation paths, on-call coverage, patch timelines.
• Upgrade rights: ensure you’re entitled to the version that supposedly contains the feature.
• Data portability: ability to export data in standard formats without punitive fees.

FAQ

1) Is vaporware always malicious?

No. Some vaporware is optimistic engineering colliding with complexity. But the impact on your systems is the same: you can’t run a promise.

2) What’s the difference between “preview” and vaporware?

Preview can be legitimate if it’s usable, testable, and has clear limits. It becomes vaporware when you’re pressured to depend on it as if it’s GA.

3) How do I push back without sounding like a blocker?

Ask for observable behaviors and acceptance tests. You’re not arguing; you’re defining what “done” means in production.

4) What if leadership already signed a contract based on roadmap features?

Shift the conversation to risk containment: isolate the dependency, design a fallback, and negotiate amendments tied to tested delivery.

5) Which product areas are most prone to vaporware?

Cross-region consistency, “zero downtime” migrations, ransomware-proof immutability, transparent multi-cloud, and performance features that claim “no overhead.”

6) How do I test “active-active” properly?

Test network partitions, not just node failures. Force split scenarios, verify commit semantics, and measure client behavior under failover.

7) Why do vendor benchmarks rarely match our reality?

Because they’re optimized for the demo: warm caches, ideal queue depths, compressible data, and single-tenant conditions. Your reality is contention and entropy.

8) Can open source be vaporware too?

Yes—especially around “planned” features in issue trackers. The mitigation is the same: run what exists, not what’s proposed.

9) What’s the safest way to adopt a new storage platform with uncertain features?

Start with non-critical workloads, require strong observability, and maintain an exit path. Promote only after months of steady-state and failure testing.

10) If the vendor says “it’s on the roadmap,” what should I ask next?

Ask: What build contains it? What are the semantics? What are the constraints? Can we test it in our POC environment now? If not, treat it as absent.

Conclusion: next steps that prevent the next incident

Vaporware survives because organizations treat announcements like inventory. Don’t. Treat them like weather forecasts: occasionally useful, never load-bearing.

Practical next steps:

1. Convert your top five vendor claims into acceptance tests you can run on demand and on schedule.
2. Refactor requirements into observable behaviors (RPO/RTO, latency percentiles, failure semantics) and stop buying adjectives.
3. Build a “no-roadmap dependencies” rule for foundational architecture. If it’s not shipped and testable, it’s not a dependency.
4. Make telemetry a gate: if you can’t measure it, you can’t operate it.
5. Negotiate delivery in writing, including remedies. Optimism is not enforceable; contracts are.

Do that, and the next time someone waves a press release at your production systems, you’ll have a simple response: “Cool. Show me the command that proves it.”