9. Bake-Offs, Pilots, and Reality Checks
Up to this point, everything we’ve done has been “paper good”:
Requirements, constraints, and assumptions.
Decision matrices and QFD mappings.
Pugh comparisons against your current state.
TCO and risk modeling.
A trade study that reads like a proper engineering decision.
All of that is essential, but it’s still theory.
In the real world, there’s a second phase you can’t skip:
Prove it. In your lab. In your network. Under your constraints.
That’s where bake-offs, pilots, and reality checks come in. This is the part where you take your beautifully reasoned options and find out which ones survive contact with reality.
Bake-Offs: Let the Devices Fight Under Your Rules
In networking, a bake-off is a controlled competition between vendors or architectures in a lab or staging environment. You’re not reading brochures anymore; you’re watching boxes sweat.
You define a test plan rooted in your actual needs, not vendor demos. For each candidate router, firewall, switch, or controller, you test:
Control plane scaling
Maximum number of BGP sessions, VRFs, EVPN routes, LSPs, etc., under stable and churn conditions.
How CPU/memory behave as tables grow and churn hits.
Convergence under failure
Simulate link failures, node failures, RR failures, and fabric module failures.
Measure:
Time until traffic reroutes (data plane).
Time until the control plane converges (routing tables stable).
Look for micro-loops, black holes, and transient errors.
RFC compliance and protocol behavior
Does their BGP actually behave the way your other boxes expect it to?
How do they handle edge cases, timers, capabilities, and weird attribute combinations?
Multi-vendor interoperability
Pair each candidate with your existing platforms.
Mix MPLS labels, EVPN routes, SR labels, or IPsec tunnels between them.
Check that everything actually works as “standards-compliant” suggests.
Streaming telemetry & observability
Validate gNMI/NETCONF/gRPC streaming.
Ensure your collectors can ingest it.
Check for useful data models, consistent field names, and reasonable update frequencies.
ACL/policy scale and performance
Load realistic ACLs and policies (hundreds or thousands of entries).
Verify:
Compilation times.
Application behavior.
Impact on forwarding performance.
This is not the vendor’s scripted demo in their lab. This is your test plan, your traffic patterns, and your failure scenarios.
Extending the Decision Matrix with Real Numbers
In earlier sections, we used qualitative scores:
“Convergence: 3 vs 4 vs 5.”
“Scale: 2 vs 4 vs 5.”
After a bake-off, you can replace opinion with measurement.
For example, in your decision matrix, you might have a criterion:
Convergence behavior – heavily weighted because your SLOs demand fast, predictable failover.
Before the bake-off, you might estimate:
Vendor A: 4 (strong implementation, good reputation).
Vendor B: 3 (okay, less proven).
Vendor C: 2–3 (uncertain, more complex).
After the bake-off, you can put in the data:
“Under link failure at load X, Vendor A reconverges 95% of flows in 150 ms, full control-plane convergence in 800 ms.”
“Vendor B: 500 ms and 2.5 seconds.”
“Vendor C: 1–3 seconds with occasional transient blackholes under scenario Y.”
You can now:
Adjust convergence scores to reflect measured behavior.
Annotate your matrix with real numbers:
Convergence time (P50/P95).
CPU usage under churn.
FIB utilization at given route counts.
Policy compile/apply times.
Telemetry bandwidth overhead.
The decision matrix is no longer “what we think will happen.” It becomes:
“Here is what we measured in our bake-off, under scenarios that match our production reality.”
That alone is a huge credibility upgrade when you present to leadership or other senior engineers.
Limited-Scope Pilots: Reality in Production, Without Betting the Farm
Even the best lab can’t perfectly replicate production. You’re still operating in a vacuum: synthetic tests, synthetic traffic, simulated failures.
The next level of reality is a pilot:
Deploy the chosen architecture or platform in a constrained slice of production, with carefully defined boundaries and guardrails.
Some examples:
New firewall architecture
Deploy it first in one region only.
Or first for internal services instead of external customer flows.
Or first for a single “friendly” tenant or business unit.
New DC fabric design
Deploy it in a single pod or Availability Zone.
Migrate a subset of non-critical workloads.
Validate routing, failover, automated changes, and observability before scaling out.
New WAN core architecture
Light it up between 2–3 POPs that mostly carry internal or mirrored traffic.
Observe how it behaves under real traffic patterns and failure scenarios.
Start with secondary paths rather than primary.
Pilots are not random experiments. They are structured reality checks driven by explicit success and failure criteria.
Define Success/Failure Criteria Up Front
A pilot without criteria is just “vibes in production.”
You define your criteria using the same tools as before:
Working Backwards – what is the “press release” for a successful pilot?
FMEA – which failure modes are unacceptable, and how will we detect them?
You answer questions like:
What metrics must improve or at least not regress?
Convergence times must be ≤ current solution or better.
Incident rate must not increase; ideally, it decreases.
CPU/memory utilization must remain within safe margins.
Automation coverage (the number of changes made via automation) must improve.
What failure modes are unacceptable?
Any failure leading to loss of isolation between tenants: hard no.
Any bug that causes widespread blackholing beyond the pilot boundary: hard no.
Any systemic observability regression that blinds you to issues: hard no.
What signs count as “yellow flags” vs “red flags”?
Yellow: one-off bug with a clear vendor fix and low RPN.
Red: repeated stability issues, poor TAC responses, or systemic architectural gaps.
You write this down before the pilot, so you can’t move the goalposts later to justify sunk costs.
Then, during the pilot:
You monitor those metrics like a hawk.
You rehearse responses to the failure modes you identified.
You validate that your mitigations (rollback, failover, guardrails) actually work.
At the end, the pilot isn’t judged by feelings. It’s judged against the criteria you set.
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