Right-Sizing Your Model Mix Across Use Cases

Table of Contents

1. Why Model Mix Matters
2. The Core Framework
3. Assessing Your Current State
4. Latency vs. Accuracy Trade-Offs
5. Cost-Per-Inference Modelling
6. Throughput and Concurrency Patterns
7. Compliance and Data Residency
8. Building Your Model Mix Decision Matrix
9. Re-Running the Framework Between Model Releases
10. Next Steps and Quick Wins

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Why Model Mix Matters

Every AI team at scale faces the same problem: you have 15+ viable models available, each with different performance characteristics, pricing, and deployment footprints. Claude 3.5 Sonnet costs 3x GPT-4o but delivers better reasoning. Llama 3.1 runs on your own infrastructure but needs 70B parameters for competitive quality. Mixtral 8x22B offers sparse routing but requires careful batching. Gemini 1.5 Pro handles 1M-token windows but locks you into Google’s ecosystem.

The question isn’t which model is “best.” The question is: which model is best for this specific use case, given your constraints right now?

Right-sizing your model mix isn’t a one-time decision. It’s a repeatable process you’ll run every 8–12 weeks as new models ship, pricing drops, and your own traffic patterns shift. Teams that get this wrong either:

• Overspend by 40–60% by routing simple queries to frontier models
• Degrade user experience by routing complex reasoning to cheap models that can’t deliver
• Miss compliance deadlines by choosing models that don’t meet data residency or audit requirements
• Waste engineering effort on custom fine-tuning when off-the-shelf models would suffice

This guide gives you a concrete, repeatable framework to right-size your model mix across all use cases. We’ve built this for teams shipping agentic AI, customer-facing AI, and internal automation at PADISO, and it works whether you’re running 3 models or 30.

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The Core Framework

Right-sizing your model mix rests on five dimensions. You’ll score each use case against each dimension, then map that score to a model candidate.

The Five Dimensions

1. Latency Budget (milliseconds)

How fast does the response need to be? A chatbot needs sub-2-second end-to-end response time. A batch content-generation job can tolerate 30 seconds. An async email classification can wait 5 minutes.

Latency directly constrains model size and inference method. A 70B model running on CPU will never hit sub-500ms latency. A 7B model on quantised inference might hit 200ms.

2. Accuracy Floor (task-specific)

What does “good enough” mean for this use case? Classification tasks need 95%+ accuracy. Summarisation can tolerate 80% semantic preservation. Brainstorming prompts need zero accuracy constraint—creativity matters more.

Accuracy floors determine whether you can use smaller, faster models or need frontier-grade reasoning.

3. Cost Per Inference

How many times will this model run per day, week, month? A model called 1 million times daily at $0.001 per call costs $1,000/day. The same model called 100 times daily costs $0.10/day.

High-volume use cases (customer support, content moderation) justify aggressive cost optimisation. Low-volume use cases (board-level strategic analysis) can afford premium models.

4. Data Sensitivity and Compliance

Does this use case touch personally identifiable information (PII), regulated data (financial, health, insurance), or trade secrets?

Data sensitivity determines whether you can use third-party APIs (OpenAI, Anthropic, Google) or must run models on your own infrastructure or trusted regional partners. It also shapes audit-readiness requirements—if you’re pursuing SOC 2 compliance or ISO 27001 certification via Vanta, model choice cascades into infrastructure and logging choices.

5. Reasoning Depth Required

Does the task require multi-step reasoning, code generation, or novel problem-solving? Or is it pattern-matching over known data?

Reasoning depth determines whether you can use smaller models, quantised versions, or mixture-of-experts architectures, or whether you need frontier models.

Scoring the Dimensions

For each use case, score each dimension on a 1–5 scale:

Dimension	Score 1	Score 3	Score 5
Latency	>10s acceptable	500ms–2s required	<100ms required
Accuracy	50%+ acceptable	85%+ required	97%+ required
Cost/Inference	<$0.0001 (high volume)	$0.001–$0.01 (medium)	>$0.1 (low volume)
Data Sensitivity	Public data only	Internal data, no PII	PII, regulated data
Reasoning Depth	Lookup/retrieval	Single-step reasoning	Multi-step, novel reasoning

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Assessing Your Current State

Before you can right-size, you need a baseline. Most teams we work with at PADISO don’t have this visibility.

What to Measure

Current Model Allocation

List every model you’re currently using in production:

• Model name and version
• Which use cases it powers
• Monthly API spend or infrastructure cost
• P50, P95, P99 latencies
• Accuracy or quality metrics (if measured)
• Daily/weekly/monthly call volume

Traffic Distribution

Most teams discover that 20% of use cases drive 80% of cost and latency impact. Map your traffic:

• Top 10 use cases by call volume
• Top 10 use cases by cost
• Top 10 use cases by latency sensitivity

Compliance and Infrastructure Constraints

Do you have any hard constraints?

• Must data stay in Australia? (Rules out US-based API providers for certain workloads)
• Do you need SOC 2 or ISO 27001 audit-readiness? (Shapes logging, data residency, and vendor selection)
• Are there regulatory requirements? (Financial services teams pursuing APRA CPS 234 compliance, insurance teams managing conduct risk, or healthcare teams handling HIPAA data all have specific model and infrastructure choices)

Quick Audit: The AI Quickstart Approach

If you don’t have this baseline, PADISO’s AI Quickstart Audit gives you a fixed-scope, two-week diagnostic. We map your current model mix, identify cost leakage, flag compliance gaps, and tell you what to ship first. It’s AU$10K fixed fee—worth it if you’re spending >$5K/month on model APIs and don’t have visibility.

Alternatively, run a lightweight audit yourself:

1. Export 30 days of API logs from OpenAI, Anthropic, Google, or your inference provider
2. Group by model, use case, and latency bucket
3. Calculate cost per use case
4. Identify outliers (high-cost, low-volume use cases; low-cost, high-volume use cases)

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Latency vs. Accuracy Trade-Offs

This is where most teams get stuck. Latency and accuracy are inversely correlated across most model families, but the relationship isn’t linear—and it changes every 8 weeks.

Understanding the Latency Curve

As you move from frontier models (Claude 3.5 Sonnet, GPT-4o) to smaller models (Llama 3.1 8B, Mistral 7B) to quantised versions (GGUF, int8), latency drops exponentially but accuracy degrades.

The trick is finding the “knee” of the curve—the point where accuracy loss accelerates relative to latency gain.

Frontier Models (70B+ parameters)

• Latency: 2–10 seconds (depending on batch size and hardware)
• Accuracy: 95%+ on reasoning tasks
• Cost: $0.01–$0.10 per 1K tokens
• Use when: reasoning depth is high, latency budget is >2s, or accuracy floor is >90%

Mid-Size Models (13B–34B parameters)

• Latency: 500ms–2s (on GPU)
• Accuracy: 85–92% on most tasks
• Cost: $0.001–$0.01 per 1K tokens
• Use when: reasoning depth is moderate, latency budget is 500ms–2s, accuracy floor is 80–90%

Small Models (7B parameters and below)

• Latency: 100–500ms (on GPU or quantised CPU)
• Accuracy: 70–85% on most tasks
• Cost: $0.0001–$0.001 per 1K tokens
• Use when: latency budget is <500ms, accuracy floor is 70–80%, cost is critical

Quantised Versions (any size, reduced precision)

• Latency: 50–300ms (depending on base size)
• Accuracy: 2–8% loss vs. full precision
• Cost: 30–50% lower than full precision
• Use when: latency is critical, accuracy loss is acceptable, you can run on your own infrastructure

Measuring Accuracy in Your Domain

Published benchmarks (MMLU, HellaSwag, TruthfulQA) are useful but don’t reflect your specific use cases. You need domain-specific accuracy measurement.

For Classification Tasks

Build a gold-standard test set of 100–500 examples. Score each model’s output against the gold standard. Calculate precision, recall, and F1.

For Generation Tasks

Use a combination of:

• Exact match (does the output match expected output exactly?)
• Semantic similarity (does the output convey the same meaning?)
• Human evaluation (does a human expert rate the output as acceptable?)

For high-stakes tasks (medical, financial, legal), always include human evaluation.

For Retrieval-Augmented Generation (RAG)

Measure:

• Retrieval accuracy (does the model retrieve the right documents?)
• Answer accuracy (given the right documents, does the model generate the right answer?)
• End-to-end accuracy (does the full pipeline produce the right answer?)

Most RAG failures are retrieval failures, not generation failures. A cheaper model with better retrieval will outperform a frontier model with poor retrieval.

The Latency-Accuracy Decision Tree

Use this tree to narrow your model candidates:

Start: What's your latency budget?
├─ <200ms → Small models or quantised inference
│  ├─ Accuracy floor >85%? → Quantised mid-size (Llama 3.1 13B GGUF)
│  └─ Accuracy floor <80%? → Small model (Mistral 7B, Phi 3.5)
├─ 200–1000ms → Mid-size models or quantised frontier models
│  ├─ Accuracy floor >90%? → Quantised frontier (Claude 3 Opus GGUF, GPT-4o mini)
│  └─ Accuracy floor <85%? → Mid-size model (Llama 3.1 34B, Mixtral 8x7B)
└─ >1000ms → Frontier models, batch processing, or async pipelines
   ├─ Accuracy floor >95%? → Frontier model (Claude 3.5 Sonnet, GPT-4o)
   └─ Accuracy floor <90%? → Mid-size model (Llama 3.1 70B)

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Cost-Per-Inference Modelling

Cost is the most concrete dimension, but most teams calculate it wrong.

The Full Cost Equation

Cost per inference isn’t just API pricing. It includes:

1. Model API Cost

• Input tokens × input price per 1K tokens
• Output tokens × output price per 1K tokens

Example: Claude 3.5 Sonnet at $3/1M input tokens, $15/1M output tokens. For a 1,000-token input and 500-token output:

• Input cost: (1,000 / 1,000,000) × $3 = $0.000003
• Output cost: (500 / 1,000,000) × $15 = $0.0000075
• Total: $0.0000105 per inference

2. Infrastructure Cost (if self-hosted)

• GPU rental: $0.20–$2.00 per hour (depending on GPU type and cloud provider)
• For a 70B model on an H100, you might pay $2/hour for 1,000 inferences/hour = $0.002 per inference
• Add 20–30% for networking, storage, and monitoring

3. Orchestration and Routing Overhead

• Load balancing: 1–3% overhead
• Observability and logging: 2–5% overhead
• Fallback and retry logic: 5–10% overhead
• Total: 8–18% overhead on top of base cost

4. Fine-Tuning and Maintenance

• If you’re fine-tuning, amortise the fine-tuning cost across expected inferences
• Example: $1,000 fine-tuning cost, 100,000 expected inferences = $0.01 per inference

Modelling Cost Across Volume Tiers

Most API providers offer volume discounts. Build a model:

Monthly Volume	Claude 3.5 Sonnet Cost	GPT-4o Cost	Llama 3.1 70B (Self-Hosted)
1M inferences	$30	$25	$60 (GPU rental)
10M inferences	$300	$250	$300 (GPU rental)
100M inferences	$2,500	$2,000	$1,500 (GPU rental + amortised infra)
1B inferences	$20,000	$16,000	$8,000 (GPU rental + amortised infra)

At 100M inferences/month, self-hosting Llama becomes cheaper than API-based frontier models. At 1B inferences/month, it’s 4–5x cheaper.

Cost Optimisation Levers

1. Caching and Prompt Optimisation

If you’re sending the same system prompt to every inference, cache it. Most APIs (OpenAI, Anthropic, Google) now support prompt caching at 50–90% discount.

Example: A customer service bot sends a 2,000-token system prompt to every inference. Caching saves 90% on that prompt cost.

2. Batch Processing

If latency budget allows, batch inferences. OpenAI Batch API costs 50% less than real-time API.

Example: Content moderation running on 1M user-generated posts. Use Batch API, run overnight, save 50% on model cost.

3. Smaller Input and Output

Every token costs money. Optimise prompts and outputs:

• Use structured output (JSON) instead of free-form text to reduce output tokens
• Use few-shot examples instead of long system prompts
• Truncate context windows to what’s actually needed

4. Model Cascading

Route simple queries to cheap models, complex queries to frontier models:

• Classify query complexity (cheap, 7B model)
• If simple, use cheap model for answer
• If complex, use frontier model
• Saves 60–80% on total cost if 70%+ of queries are simple

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Throughput and Concurrency Patterns

Latency and cost are pointless if your system can’t handle the throughput you need.

Understanding Throughput Constraints

Throughput depends on:

1. Model Inference Throughput

• How many tokens per second can the model generate?
• A 70B model on H100 GPU: ~200 tokens/second
• A 7B model on A100 GPU: ~500 tokens/second
• A quantised 7B model on CPU: ~50 tokens/second

2. Concurrency

• How many requests can you handle in parallel?
• A single GPU can handle 1–4 concurrent requests (depending on batch size and memory)
• A cluster of 10 GPUs can handle 10–40 concurrent requests

3. Queue Depth and Latency

• If you have 100 concurrent requests but only 4 slots on your GPU, 96 requests queue
• Each request waits (queue depth) × (average latency) before execution
• Queue wait can exceed model latency by 10–100x

Calculating Required Throughput

Start with your traffic:

Peak Requests Per Second (RPS)

• Daily volume: 1M inferences
• Peak hour: 10% of daily volume = 100K inferences
• Peak second: 100K / 3,600 = ~28 RPS
• Add 50% headroom for spikes: 42 RPS

Model Latency and Tokens Per Request

• Average request: 500 input tokens, 200 output tokens = 700 tokens
• Model throughput: 200 tokens/second
• Time per request: 700 / 200 = 3.5 seconds

Required Concurrency

• Concurrency = RPS × latency
• 42 RPS × 3.5s = 147 concurrent slots
• On H100 GPUs with batch size 4: 147 / 4 = 37 GPUs

Throughput Optimisation Strategies

1. Batching

If you have queue depth, batch requests:

• Batch 32 requests together
• Model processes 32 in parallel
• Throughput increases 8–16x (depending on batch efficiency)

2. Smaller Models

Smaller models have higher per-second throughput:

• 7B model: 500 tokens/second
• 70B model: 200 tokens/second
• 4x throughput gain by switching to smaller model

If you can route 80% of traffic to a small model and 20% to a frontier model, throughput increases 3–4x.

3. Quantisation

Quantised models run faster on cheaper hardware:

• Full precision 70B: 200 tokens/second on H100
• Int8 70B: 300 tokens/second on H100 (30% gain)
• Int4 70B: 400 tokens/second on H100 (2x gain) but with 2–5% accuracy loss

4. Speculative Decoding

Use a small model to draft tokens, then a frontier model to verify. Speeds up generation 2–4x with minimal accuracy loss.

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Compliance and Data Residency

For teams in Australia or handling regulated data, compliance isn’t optional—it shapes your entire model mix.

Data Residency Constraints

Australian Data Must Stay in Australia

If you’re handling Australian customer data, health data, or financial data, many use cases require data residency in Australia. This rules out:

• OpenAI (US-only)
• Anthropic (US-only)
• Google Gemini API (US/EU, not AU)

Your options:

1. Use Australian-based inference providers (limited; most are resellers)
2. Self-host models on Australian infrastructure (AWS Sydney, Azure Australia, or on-premise)
3. Use models that support regional endpoints (some providers offer AU endpoints, but not all models)

Regulatory Compliance

Financial Services (APRA CPS 234)

If you’re building AI for banks, wealth managers, or lenders, APRA CPS 234 requires:

• Explainability: can you explain the model’s decision?
• Governance: do you have audit trails and approval workflows?
• Risk management: do you monitor for model drift and bias?

Frontier models (Claude, GPT-4o) are more explainable than smaller models. But you also need infrastructure that logs decisions, tracks model versions, and supports audit.

Insurance (ASIC RG 271, LIF Conduct Risk)

Insurance teams using AI for claims, underwriting, or conduct risk monitoring need:

• Audit trails: every decision logged
• Fairness monitoring: no discriminatory outcomes
• Model stability: drift detection and retraining triggers

Healthcare and Life Sciences (HIPAA, GxP)

If you’re handling health data or running models in GxP environments, you need:

• Data encryption in transit and at rest
• Access controls and audit logging
• Validated models (if GxP)

Most third-party APIs don’t meet GxP requirements. You’ll need self-hosted, validated infrastructure.

SOC 2 and ISO 27001 Audit-Readiness

If you’re pursuing SOC 2 compliance or ISO 27001 certification via Vanta, model choice cascades into infrastructure and logging choices:

Third-Party APIs (OpenAI, Anthropic, Google)

• Data flows to US infrastructure
• You need vendor risk assessments and data processing agreements (DPAs)
• Vanta can check for SOC 2 Type II reports from vendors
• Audit-ready if vendors have SOC 2 reports (most do)

Self-Hosted Models

• Data stays on your infrastructure
• You control logging, access, and encryption
• Audit-ready if you have proper logging, access controls, and encryption
• More work upfront, but simpler compliance story

Hybrid (Mix of APIs and Self-Hosted)

• Most complex compliance story
• You need separate audit trails for each path
• But offers flexibility: use APIs for non-sensitive data, self-host for sensitive data

Right-Sizing for Compliance

When compliance is a constraint, your model mix decision tree changes:

Start: Is this use case handling regulated or sensitive data?
├─ No → Use any model (API or self-hosted)
├─ Yes, must stay in Australia → Self-host on AU infrastructure
│  ├─ Latency <500ms required? → Quantised small model (Mistral 7B GGUF)
│  └─ Latency >1s acceptable? → Any model (Llama 3.1 70B, Mixtral 8x22B)
└─ Yes, needs audit trail → Self-host or use API with detailed logging
   ├─ Explainability required (APRA, ASIC)? → Frontier model + explainability layer
   └─ Fairness monitoring required? → Smaller, interpretable model

For teams pursuing compliance via Vanta, PADISO’s Security Audit service maps your model mix to audit-readiness requirements and identifies gaps.

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Building Your Model Mix Decision Matrix

Now you’ll synthesise all five dimensions into a single decision matrix.

Step 1: List Your Use Cases

For each major use case, create a row:

Use Case	Latency	Accuracy	Cost/Inf	Data Sens	Reasoning	Score
Customer support Q&A	3	4	4	4	3	18
Content moderation	2	5	5	2	2	16
Email classification	1	4	5	3	1	14
Report generation	4	3	2	3	4	16
Fraud detection	2	5	4	5	3	19

Step 2: Score Each Dimension

For each use case, score each dimension 1–5:

Latency Score

• 5: <100ms required (real-time UI, search)
• 4: 100–500ms required (interactive chatbot)
• 3: 500ms–2s acceptable (batch processing, async)
• 2: 2–10s acceptable (background jobs)
• 1: >10s acceptable (overnight batch)

Accuracy Score

• 5: >97% required (fraud, medical, legal)
• 4: 90–97% required (customer-facing, financial)
• 3: 80–90% acceptable (content, support)
• 2: 70–80% acceptable (brainstorming, ideation)
• 1: >50% acceptable (exploratory, R&D)

Cost/Inference Score

• 5: <$0.0001 (high volume, >1M/day)
• 4: $0.0001–$0.001 (medium volume, 100K–1M/day)
• 3: $0.001–$0.01 (low-medium volume, 10K–100K/day)
• 2: $0.01–$0.1 (low volume, 1K–10K/day)
• 1: >$0.1 (very low volume, <1K/day)

Data Sensitivity Score

• 5: PII, regulated data, trade secrets (must self-host or use trusted regional provider)
• 4: Internal data, no PII (can use APIs with DPA)
• 3: Mixed public and internal (use APIs, but with logging)
• 2: Mostly public (any model)
• 1: Public only (any model)

Reasoning Depth Score

• 5: Multi-step reasoning, novel problem-solving (frontier models only)
• 4: Single-step reasoning, code generation (mid-size or frontier)
• 3: Moderate reasoning, pattern-matching (mid-size)
• 2: Simple pattern-matching (small models)
• 1: Lookup, retrieval only (embeddings, BM25)

Step 3: Calculate Total Score and Map to Models

Total score = sum of all five dimension scores (5–25 range).

Score 20–25: Frontier Models

• Claude 3.5 Sonnet, GPT-4o, Gemini 1.5 Pro
• Use when: accuracy and reasoning are critical, cost is secondary

Score 15–19: Mid-Size Models

• Llama 3.1 34B–70B, Mixtral 8x22B, Claude 3 Opus
• Use when: reasoning is important, but cost and latency matter

Score 10–14: Small Models

• Llama 3.1 8B, Mistral 7B, Phi 3.5
• Use when: latency and cost are critical, reasoning is simple

Score <10: Specialized Models or Non-LLM

• Embeddings, BM25, domain-specific models
• Use when: you don’t need language generation

Step 4: Validate Against Constraints

Before you commit, validate:

1. Data Sensitivity: Does the model choice respect your compliance constraints?
2. Throughput: Can you achieve the required RPS with this model?
3. Cost: Is the total monthly cost acceptable?
4. Latency: Does the model meet your latency SLA?
5. Accuracy: Have you tested on your domain data?

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Re-Running the Framework Between Model Releases

Your model mix isn’t static. Every 8–12 weeks, new models ship with better performance, lower pricing, or new capabilities. You need a repeatable process to re-evaluate.

Quarterly Model Review Cadence

Month 1: Baseline

• Measure current model performance, latency, and cost
• Document decision rationale for each use case
• Set target KPIs (cost reduction, latency improvement, accuracy gain)

Month 2: New Model Evaluation

• When new models ship (Claude 3.6, GPT-5, Llama 4, etc.), benchmark them
• Test on your domain data (not published benchmarks)
• Calculate cost per inference and latency
• Run through the decision matrix for each use case

Month 3: Migration Planning

• Identify use cases where model switch improves cost, latency, or accuracy by >10%
• Plan migration: update prompts, test in staging, monitor in production
• Measure impact: did you hit your target KPIs?
• Document learnings for next quarter

What to Benchmark

When a new model ships, benchmark:

1. Accuracy on Your Domain Data

• Use your gold-standard test set (100–500 examples)
• Compare new model vs. current model
• Calculate delta in F1, precision, recall, or semantic similarity

2. Latency

• P50, P95, P99 latency
• Batch size and throughput
• Cost per inference at your expected batch size

3. Cost Per Inference

• Input token cost
• Output token cost
• Total cost per inference at your expected input/output distribution

4. Compliance and Data Residency

• Is the model available in your region?
• Does it meet your audit and compliance requirements?

Building a Model Benchmark Suite

Create a reusable benchmark suite for your domain:

# Pseudocode: benchmark_models.py

models = [
    "claude-3.5-sonnet",
    "gpt-4o",
    "llama-3.1-70b",
    "gemini-1.5-pro",
    # Add new models as they ship
]

for model in models:
    for use_case in use_cases:
        results = benchmark(
            model=model,
            test_set=use_case.gold_standard_data,
            metrics=["accuracy", "latency", "cost"]
        )
        log_results(model, use_case, results)

# Regenerate decision matrix
update_model_mix_matrix(results)

Run this suite every time a new model ships. You’ll quickly see which use cases benefit from the new model.

Automation and Monitoring

For high-volume use cases, automate the comparison:

1. Shadow Traffic: Route 5–10% of production traffic to the new model in parallel
2. Measure: Compare accuracy, latency, and cost in production
3. Decide: If new model wins on your KPIs, gradually shift traffic
4. Monitor: Track model performance over time (drift detection)

Tools like Datadog Cloud Cost Management and Kubernetes resource management can help you track and optimise across your model fleet.

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Next Steps and Quick Wins

You now have a framework. Here’s how to start:

Week 1: Baseline Assessment

1. Export your last 30 days of model API logs
2. Group by model, use case, latency, and cost
3. Identify your top 10 use cases by volume and cost
4. For each, score the five dimensions (latency, accuracy, cost, sensitivity, reasoning)
5. Map to the decision matrix

If you don’t have this data, PADISO’s AI Quickstart Audit gives you the baseline in two weeks, AU$10K fixed fee.

Week 2: Quick Wins

Look for immediate optimisations:

1. Prompt Caching: Are you sending the same system prompt repeatedly? Cache it. Save 50–90% on prompt cost.
2. Model Cascading: Are you routing simple queries to frontier models? Build a classifier, route simple queries to small models. Save 60–80% on cost.
3. Batch Processing: Can any use case tolerate 1–24 hour latency? Use batch APIs. Save 50% on cost.
4. Quantisation: For self-hosted models, try int8 or int4 quantisation. Save 30–50% on infrastructure cost with <5% accuracy loss.

Week 3: Build Your Decision Matrix

1. List all use cases
2. Score each on the five dimensions
3. Map to model candidates
4. Validate against compliance and throughput constraints
5. Document decision rationale

Week 4: Implement and Monitor

1. Update your routing logic to match the new model mix
2. Monitor accuracy, latency, and cost in production
3. Set up alerts for model drift or SLA violations
4. Plan quarterly reviews to re-evaluate as new models ship

Ongoing: Quarterly Model Reviews

Every quarter, when new models ship:

1. Benchmark new models on your domain data
2. Update the decision matrix
3. Identify use cases where model switch improves cost or accuracy by >10%
4. Plan and execute migrations
5. Measure impact and document learnings

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Conclusion: Right-Sizing is a Process, Not a Decision

Right-sizing your model mix isn’t a one-time exercise. It’s a repeatable process you’ll run every 8–12 weeks as new models ship, pricing changes, and your traffic patterns evolve.

The framework—latency, accuracy, cost, data sensitivity, reasoning depth—gives you a structured way to make trade-offs. The decision matrix translates that structure into concrete model choices. And the quarterly review cadence keeps your mix aligned with the latest models and your business constraints.

Teams that get this right see:

• 30–50% cost reduction by routing simple queries to cheap models
• 2–5x throughput improvement by optimising batch size and model selection
• Faster compliance timelines by choosing models that fit audit requirements from day one
• Faster shipping by avoiding over-engineering (using frontier models when small models suffice) and under-engineering (using small models when reasoning is critical)

If you’re shipping agentic AI, customer-facing AI, or internal automation at scale, this framework will save you thousands of dollars per month and weeks of engineering time.

Ready to right-size your model mix? Start with the baseline assessment. If you need help, PADISO’s AI Quickstart Audit gives you a two-week diagnostic with concrete recommendations. Or explore PADISO’s AI & Agents Automation service to co-build your AI stack with fractional CTO leadership and platform engineering expertise.

For teams in Australia, PADISO’s Sydney-based AI advisory includes right-sizing guidance as part of broader AI strategy and delivery. We also specialise in compliance-aware AI for financial services, insurance, and regulated industries—ensuring your model mix meets APRA, ASIC, and audit requirements from day one.

Start with your top 3 use cases. Score them. Map them to models. Measure the impact. Then repeat every quarter as the model landscape shifts.