Open Source vs Proprietary LLMs: Making the Right Choice

The choice between open-source and proprietary LLMs is one of the most important architectural decisions for AI projects. Let’s analyze the tradeoffs systematically.

Decision Framework

from dataclasses import dataclass
from enum import Enum
from typing import List

class Priority(Enum):
    COST = "cost"
    PERFORMANCE = "performance"
    CUSTOMIZATION = "customization"
    DATA_PRIVACY = "data_privacy"
    TIME_TO_MARKET = "time_to_market"
    MAINTENANCE = "maintenance"

@dataclass
class ModelOption:
    name: str
    type: str  # "open_source" or "proprietary"
    scores: dict  # Priority -> score (1-10)

def recommend_model(priorities: List[Priority], options: List[ModelOption]) -> ModelOption:
    """Score models based on weighted priorities"""
    weights = {p: 1.0 / (i + 1) for i, p in enumerate(priorities)}

    best_score = 0
    best_option = None

    for option in options:
        score = sum(weights.get(p, 0) * option.scores.get(p.value, 5)
                   for p in priorities)
        if score > best_score:
            best_score = score
            best_option = option

    return best_option

# Define options
options = [
    ModelOption(
        name="GPT-4 Turbo",
        type="proprietary",
        scores={
            "cost": 4,
            "performance": 9,
            "customization": 3,
            "data_privacy": 5,
            "time_to_market": 10,
            "maintenance": 10
        }
    ),
    ModelOption(
        name="Claude 3 Opus",
        type="proprietary",
        scores={
            "cost": 3,
            "performance": 9,
            "customization": 3,
            "data_privacy": 6,
            "time_to_market": 10,
            "maintenance": 10
        }
    ),
    ModelOption(
        name="Llama 2 70B",
        type="open_source",
        scores={
            "cost": 8,
            "performance": 7,
            "customization": 9,
            "data_privacy": 10,
            "time_to_market": 5,
            "maintenance": 4
        }
    ),
    ModelOption(
        name="Mistral 7B",
        type="open_source",
        scores={
            "cost": 10,
            "performance": 6,
            "customization": 9,
            "data_privacy": 10,
            "time_to_market": 6,
            "maintenance": 5
        }
    )
]

# Startup prioritizing time to market
startup_priorities = [
    Priority.TIME_TO_MARKET,
    Priority.PERFORMANCE,
    Priority.COST
]
print(f"Startup: {recommend_model(startup_priorities, options).name}")

# Enterprise prioritizing data privacy
enterprise_priorities = [
    Priority.DATA_PRIVACY,
    Priority.CUSTOMIZATION,
    Priority.PERFORMANCE
]
print(f"Enterprise: {recommend_model(enterprise_priorities, options).name}")

Cost Analysis Calculator

from dataclasses import dataclass
from typing import Optional

@dataclass
class UsagePattern:
    daily_requests: int
    avg_input_tokens: int
    avg_output_tokens: int
    peak_concurrent: int

@dataclass
class CostEstimate:
    monthly_compute: float
    monthly_inference: float
    setup_cost: float
    monthly_ops: float

    @property
    def total_monthly(self) -> float:
        return self.monthly_compute + self.monthly_inference + self.monthly_ops

    @property
    def total_first_year(self) -> float:
        return self.setup_cost + (self.total_monthly * 12)

def estimate_proprietary_cost(usage: UsagePattern) -> CostEstimate:
    """Estimate cost for proprietary API (GPT-4 Turbo)"""
    input_price_per_1m = 10.0
    output_price_per_1m = 30.0

    monthly_input_tokens = usage.daily_requests * usage.avg_input_tokens * 30
    monthly_output_tokens = usage.daily_requests * usage.avg_output_tokens * 30

    monthly_cost = (
        (monthly_input_tokens / 1_000_000) * input_price_per_1m +
        (monthly_output_tokens / 1_000_000) * output_price_per_1m
    )

    return CostEstimate(
        monthly_compute=0,
        monthly_inference=monthly_cost,
        setup_cost=0,
        monthly_ops=0
    )

def estimate_open_source_cost(usage: UsagePattern) -> CostEstimate:
    """Estimate cost for self-hosted Llama 2 70B on Azure"""
    # 4x A100 80GB instance
    hourly_compute = 27.20
    monthly_compute = hourly_compute * 24 * 30

    # Engineering time for setup (40 hours at $150/hr)
    setup_cost = 40 * 150

    # Monthly ops (monitoring, updates, etc.)
    monthly_ops = 20 * 150  # 20 hours/month

    return CostEstimate(
        monthly_compute=monthly_compute,
        monthly_inference=0,
        setup_cost=setup_cost,
        monthly_ops=monthly_ops
    )

# Compare costs
usage = UsagePattern(
    daily_requests=10000,
    avg_input_tokens=500,
    avg_output_tokens=1000,
    peak_concurrent=50
)

proprietary = estimate_proprietary_cost(usage)
open_source = estimate_open_source_cost(usage)

print(f"Proprietary monthly: ${proprietary.total_monthly:,.0f}")
print(f"Open source monthly: ${open_source.total_monthly:,.0f}")
print(f"Break-even at: {open_source.setup_cost / (proprietary.total_monthly - open_source.total_monthly):.1f} months")

Capability Comparison Matrix

import pandas as pd

capabilities = {
    "Feature": [
        "Maximum context",
        "Fine-tuning",
        "Function calling",
        "Vision support",
        "Streaming",
        "Self-hosting",
        "Data privacy",
        "Latency control",
        "Custom tokenizer"
    ],
    "GPT-4 Turbo": [
        "128K", "Limited", "Yes", "Yes", "Yes",
        "No", "API only", "Limited", "No"
    ],
    "Claude 3 Opus": [
        "200K", "No", "Yes", "Yes", "Yes",
        "No", "API only", "Limited", "No"
    ],
    "Llama 2 70B": [
        "4K", "Full", "Via prompting", "No", "Yes",
        "Yes", "Full control", "Full control", "Yes"
    ],
    "Mixtral 8x7B": [
        "32K", "Full", "Via prompting", "No", "Yes",
        "Yes", "Full control", "Full control", "Yes"
    ]
}

df = pd.DataFrame(capabilities)
print(df.to_string(index=False))

Hybrid Architecture Pattern

from abc import ABC, abstractmethod
from typing import Dict, Any

class LLMBackend(ABC):
    @abstractmethod
    def complete(self, prompt: str, **kwargs) -> str:
        pass

class ProprietaryBackend(LLMBackend):
    """For complex tasks requiring best quality"""
    def __init__(self, model: str = "gpt-4-turbo"):
        from openai import OpenAI
        self.client = OpenAI()
        self.model = model

    def complete(self, prompt: str, **kwargs) -> str:
        response = self.client.chat.completions.create(
            model=self.model,
            messages=[{"role": "user", "content": prompt}],
            **kwargs
        )
        return response.choices[0].message.content

class OpenSourceBackend(LLMBackend):
    """For high-volume, simpler tasks"""
    def __init__(self, endpoint_url: str, api_key: str):
        self.endpoint_url = endpoint_url
        self.api_key = api_key

    def complete(self, prompt: str, **kwargs) -> str:
        import requests
        response = requests.post(
            self.endpoint_url,
            headers={"Authorization": f"Bearer {self.api_key}"},
            json={"prompt": prompt, **kwargs}
        )
        return response.json()["text"]

class HybridRouter:
    def __init__(self):
        self.proprietary = ProprietaryBackend()
        self.open_source = OpenSourceBackend(
            "https://llama-endpoint.inference.ml.azure.com",
            "your-key"
        )

    def route(self, task_type: str, prompt: str) -> str:
        # Route based on task complexity
        complex_tasks = {"analysis", "reasoning", "creative"}

        if task_type in complex_tasks:
            return self.proprietary.complete(prompt)
        else:
            return self.open_source.complete(prompt)

# Usage
router = HybridRouter()
result = router.route("classification", "Classify this text: ...")

Migration Strategy

# Phased migration from proprietary to open source

class MigrationManager:
    def __init__(self):
        self.traffic_split = {"proprietary": 100, "open_source": 0}
        self.quality_threshold = 0.85

    def update_split(self, open_source_quality: float):
        """Gradually shift traffic based on quality metrics"""
        if open_source_quality >= self.quality_threshold:
            new_os_pct = min(self.traffic_split["open_source"] + 10, 100)
            self.traffic_split["open_source"] = new_os_pct
            self.traffic_split["proprietary"] = 100 - new_os_pct

    def get_backend(self) -> str:
        import random
        if random.randint(1, 100) <= self.traffic_split["open_source"]:
            return "open_source"
        return "proprietary"

Conclusion

The choice isn’t binary - most production systems benefit from a hybrid approach that leverages the strengths of both open-source and proprietary models while managing costs and maintaining quality.