Inference Benchmarking Examples: Cost-Per-Request Comparisons That Actually Decide
Jun 12, 2026
How to benchmark LLM inference serving configs on cost-per-request and p95 latency, not tokens-per-second, so the comparison maps to margin.
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AI Engineering Services
Five productised packs across four pillars. Each one starts from a symptom you can name, ends in something your team keeps and can re-run, and is priced against the result, not engineer-weeks against a backlog.
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Tell us the problem
How This Works
Most teams don't arrive asking for a service. They arrive with a symptom: an inference bill climbing faster than usage, a model that passes the demo and then regresses in production, a workload that won't run on the hardware it has to ship on, or an AI feature nobody will sign off because the evidence isn't there.
Our job is to map that symptom to one of four directions production AI work moves in, then to the one pack that closes it. Every pack ends the same way: with something your team keeps and can re-run after we leave.
Step One: The Four Pillars
Four Directions the Work Moves In
Every pack we ship sits primarily under one of these four directions and secondarily under one or two others. Find the one that matches your symptom, then pick the pack underneath it.
Faster and Cheaper to Run
Cost
We profile the workload, fix the bottlenecks that matter, and prove it with a measured before/after on the requests you actually run.
Runs on the Target
Portability
We port what needs porting (native, WASM, WebGPU, embedded, novel silicon) and benchmark on the hardware it has to ship on.
Reliable in Production
Reliability
Eval harnesses, drift checks, and release gates that turn a working demo into a system your on-call can actually defend.
Auditable and Approvable
Trust
The evidence around the model (eval reports, comparisons, readiness scoring against named rubrics) so it gets approved, not just shipped.
Step Two: The Five Packs
- Inference Cost-Cut Pack: Cost. Measured before/after report on a named workload, plus the implemented changes. (4–8 weeks)
- Production AI Monitoring Harness: Reliability. Eval harness, regression suite, and release-gate checklist on a named system. (4–10 weeks)
- AI Porting & Deployment Pack: Portability. Working workload, benchmark report, and deployment runbook on a named target. (2–4 wk feasibility, then 4–10 wk porting)
- LLM Selection Pack: Trust. Eval suite, risk-and-comparison report, and a re-run script on named LLM candidates. (3–6 weeks)
- AI Readiness Scorecard: Trust. Filled scorecard against a named rubric, evidence map, and remediation backlog. (2–5 weeks)
Step Three: What You Keep From Every Engagement
Measured before/after report
Re-runnable eval harness
Deployment runbook and benchmark
Reproducible re-run script
Evidence map against a named rubric
Where the Packs Meet
Three kinds of work sit across pillars on purpose. They show up on both sides, and which pack owns them depends on the question you're asking, not the underlying engineering:
- Porting for performance. Start with the AI Porting & Deployment Pack when the AI path doesn't run on the target yet. Start with the Inference Cost-Cut Pack when the target is mature and the question is cost, not initial porting.
- Shelf and store analytics. Start with the Inference Cost-Cut Pack when the question is cost-per-throughput at retail scale. Start with the Production AI Monitoring Harness when the question is alert quality or false-positive economics.
- MLOps hardening. Start with the Production AI Monitoring Harness for the harness itself. Start with the AI Readiness Scorecard when the question is scoring the programme against ML Test Score or NIST AI RMF.
Not Sure Which Pack?
Match the symptom you can name to the pack that closes it:
- “Our inference bill is growing faster than our usage.” → Inference Cost-Cut Pack
- “It passes our demo but regresses in production.” → Production AI Monitoring Harness
- “We need this on hardware, a browser, or silicon it doesn't run on yet.” → AI Porting & Deployment Pack
- “Procurement needs evidence to approve an LLM for our task.” → LLM Selection Pack
- “Score us against NIST AI RMF, ML Test Score, or a readiness rubric.” → AI Readiness Scorecard
- “Certify us.” → Out of scope, we'll refer you to legal and regulatory partners.
By Industry
The pack catalogue is industry-agnostic by design. If you know your vertical, the industry pages pre-filter to the work that matters there and route each piece to its owning pack:
- AI-infrastructure / SaaS: inference cost, MLOps hardening, porting, LLM evals.
- Life sciences: medical-imaging validation, HIPAA / GxP boundary work. No clinical-decision claims; no regulatory sign-off.
- Manufacturing & automotive: industrial CV inspection, automotive perception for civilian vehicles. No autonomous-targeting work.
- Media & telecom: video pipeline cost-cut, content moderation, operational anomaly detection on your own infrastructure.
- Retail: shelf-execution validation, visual-search cost-cut. Scoped to stock and catalogue, not shopper tracking.
Client Testimonials
TechnoLynx delivered the project on time and provided quality outputs that met the client's expectations. The team was proactive in providing ideas and suggestions, and they were careful at properly planning the tasks. The client also praised the team's expertise in GPU programming and AI.
TechnoLynx's skill in low-level software development was impressive. TechnoLynx was able to create four prototypes with common components and an interface for easy maintenance. The client was extremely happy with the solution's speed. Moreover, their communication was seamless and straightforward.
TechnoLynx's unique aspect is that they're able to transform complex theories into practicable and applicable results. TechnoLynx provides research reports and architecture planning documents. The team is able to transform complex theories into practicable and applicable results. TechnoLynx's project management is strong and delivers work on time without hardware issues, being responsive through virtual meetings.
I’m delighted with our collaboration with their team. Thanks to TechnoLynx's work, the client has been able to co-author two patents. They lead responsive project management to solve problems quickly. The team also praises their skilled and knowledgeable team.
We had high-efficiency meetings. TechnoLynx’s work resulted in a successful breakthrough, and their input improved the client’s app. Their flexible and organised project management cultivated a healthy collaboration experience. Ultimately, their professionalism and commitment were impressive.
Featured Articles
One read per pillar (cost, portability, reliability, and trust) on the engineering thinking behind the packs.
Porting AI Inference: How Runtime and Hardware Porting Cuts Cost Without a Model Swap
Jun 12, 2026
Porting moves a model to a faster runtime, recompiled kernels, or new hardware — often a cheaper fix than replacing a model that was never the bottleneck.
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What a Production AI Monitoring Harness Actually Contains
Jun 12, 2026
A production AI monitoring harness is a signable deliverable: eval suites, regression tests, drift telemetry, alert-quality work, release gates.
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Procurement-Grade LLM Evaluation Evidence — The Artefact That Survives an Approval Committee
Jun 12, 2026
A procurement-grade LLM evaluation evidence pack answers the approval committee's real questions — task accuracy, failure modes, cost-per-decision, drift.
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Start a Conversation
If one of the five packs matches the question you're trying to close, the named pack page above is the right entry point. If you're not sure, tell us the symptom and we'll route you to the pack, or tell you honestly if the work falls outside what we take on. Where we draw that line, and why, is published on our values page.
Keystone deep dives
- MLOps: The Operating Model That Keeps Production Machine Learning Healthy
- MLOps vs DevOps: Where the Two Operating Models Diverge and Why It Matters
- Why Most Enterprise AI Projects Fail — and the Root Causes No One Addresses
- What to Look for When Evaluating AI Consulting Firms
- LLMOps vs MLOps: Where Operationalising Large Language Models Actually Diverges
- How an AI Inference Cost Audit Finds the Real Bottleneck Before You Replace the Model
- What a Production AI Reliability Audit Actually Tests (Evals, Drift, Rollout, Ownership)
- What a Performance and Porting Assessment Tells You Before You Commit to a Migration
- Profiling AI Inference: How It Works and What the Numbers Mean in Practice
- Application Performance Management Tools for AI Inference: What They Show and What They Miss
- Performance Tuning for AI Inference: What It Actually Means in Practice
- Porting AI Inference: How Runtime and Hardware Porting Cuts Cost Without a Model Swap
- Profiling Tools for AI Inference: What They Measure and How to Read the Output
- Data Drift vs Model Drift: What Each Means and How They Change Your AI Reliability Response
- Performance Engineering for Production AI: Latency, Throughput, and Reliability Under Load
Related TechnoLynx areas
- Computer VisionProduction CV pipelines from edge perception to cloud analytics.
- Generative & Agentic AILLMs, agents, and synthesis systems engineered for reliability.
- GPU Performance EngineeringThroughput, latency, and cost wins from kernel up to cluster.
- LynxBenchAIBenchmarking methodology and published results for AI hardware.