Open-Source AI vs Commercial AI: What Developers Should Know in 2026

By Fadi Updated May 22, 2026 15 min read Dev Fundamentals

TL;DR — Open vs commercial: constraints decision, not capability. Areebi: "Capability gap narrowed. Deciding factors: data residency, fine-tuning rights, audit access, EU AI Act compliance." JobsByCulture: "Open-weight matches frontier within single-digit percent at 4-10x lower cost. Self-host only when regulated data, fine-tuning, or extreme volume justify operational cost." AITENCY: "No single provider wins every axis. Multi-provider architecture avoids lock-in." LetsDataScience: "MIT/Apache 2.0 fully permissive. Llama Community has 700M MAU limit. Breakeven self-hosting at 5-10M tokens/month." Learn more with choose LLM, RAG vs fine-tuning, cost estimation, and Docker deployment.

Areebi frames the 2026 reality: "By mid-2026 the open-source vs proprietary LLM debate has moved past leaderboard rankings into the procurement and governance review. The capability gap between top open-weight models and proprietary frontier models has narrowed enough that the deciding factors are now legal, structural, and operational — data residency, fine-tuning rights, audit access, transparency for ISO 42001 and SOC 2, and how each licence interacts with EU AI Act Articles 50 and 53."

JobsByCulture simplifies: "In 2026, leading open-weight models match frontier closed models within single-digit percentages on most everyday production tasks, at 4-10x lower per-token cost. The open vs closed decision is now a constraints decision, not a capability decision."

Open-Source vs Commercial AI Architecture

flowchart TD subgraph Decision["Constraint-Based Decision"] Q1{"Data must stay
on your network?"} Q2{"Need full
fine-tuning on
proprietary data?"} Q3{"Volume >10M
tokens/month?"} Q4{"Need frontier
reasoning or
multimodal?"} Q5{"Vendor lock-in
is strategic risk?"} end subgraph Open["Open-Source Path"] SelfHost["Self-Hosted Open Weights
data never crosses boundary
full fine-tuning access
cost predictable at scale
MLOps engineering required"] HostedOpen["Hosted Open API
Together, Fireworks, Groq
4-10x cheaper than frontier
no infra to manage
permissive licensing"] end subgraph Commercial["Commercial Path"] Enterprise["Enterprise API Tier
zero data retention
SOC 2, HIPAA compliance
no training on your data
managed infrastructure"] Frontier["Frontier Closed API
GPT-5, Claude Opus, Gemini Pro
top-tier reasoning
multimodal breadth
API-only, no weights"] end subgraph Hybrid["Hybrid Multi-Provider"] Three["Three-Tier Strategy
top: commercial frontier for
customer-facing and reasoning
middle: open-weight for
internal RAG and processing
bottom: small OSS for
sensitive PII/PHI"] MCP["Open Standards
Model Context Protocol
loosely coupled models
avoid orchestration lock-in
swap providers freely"] end subgraph Licensing["License Spectrum"] MIT["MIT: DeepSeek R1, Phi-4
no restrictions"] Apache["Apache 2.0: Qwen 3.5,
Mistral Large 3
no restrictions"] Llama["Llama Community:
Llama 4, Llama 3.x
700M MAU limit, attribution"] Gemma["Gemma ToU: Gemma 3
Google can restrict usage"] Proprietary["Proprietary: GPT-5,
Claude 4, Gemini 3
API-only, no weights"] end Q1 -->|Yes| SelfHost Q1 -->|No| Q2 Q2 -->|Yes| SelfHost Q2 -->|No| Q3 Q3 -->|Yes| HostedOpen Q3 -->|No| Q4 Q4 -->|Yes| Frontier Q4 -->|No| Q5 Q5 -->|Yes| HostedOpen Q5 -->|No| Enterprise SelfHost --> Three HostedOpen --> Three Enterprise --> Three Frontier --> Three Three --> MCP SelfHost --> Licensing HostedOpen --> Licensing Licensing --> MIT Licensing --> Apache Licensing --> Llama Licensing --> Gemma Licensing --> Proprietary style Open fill:#39FF14,color:#000 style Commercial fill:#4169E1,color:#fff style Hybrid fill:#2D1B69,color:#fff style Licensing fill:#FF6B6B,color:#fff

Open-Source vs Commercial Comparison

Dimension	Open-Source (Self-Hosted)	Open-Source (Hosted API)	Commercial (Enterprise API)
Cost per token	Electricity + GPU depreciation	4-10x cheaper than frontier	Premium per-token pricing
Break-even	5-10M tokens/month	N/A (pay per use)	N/A (pay per use)
Data sovereignty	Full — never crosses boundary	Varies by provider	Enterprise zero-retention tier
Fine-tuning	Full weight access	Limited or none	Limited, provider-hosted
Capability	Within single-digit % of frontier	Within single-digit % of frontier	Frontier reasoning, multimodal
Infrastructure	GPU + MLOps required	None	None
Licensing	MIT, Apache 2.0, Llama Community	Per provider terms	Proprietary, API-only
Compliance	Full audit access	Varies	SOC 2, HIPAA, ISO 42001
Vendor lock-in	None	Low	High if not abstracted
Model versioning	Freeze any version	Provider-controlled	Provider-controlled

Implementation

from dataclasses import dataclass
from typing import Optional
from enum import Enum

class ProviderType(Enum):
    OPEN_SELF_HOSTED = "open_self_hosted"
    OPEN_HOSTED_API = "open_hosted_api"
    COMMERCIAL_ENTERPRISE = "commercial_enterprise"
    COMMERCIAL_FRONTIER = "commercial_frontier"

@dataclass
class OpenSourceVsCommercialGuide:
    """Open-source vs commercial AI decision guide."""

    def get_multi_provider(self) -> str:
        """Multi-provider architecture."""
        return (
            "# === MULTI-PROVIDER ARCHITECTURE ===\n"
            "from abc import ABC, abstractmethod\n"
            "import httpx, time\n"
            "\n"
            "class LLMProvider(ABC):\n"
            "    '''Abstract provider interface.''' \n"
            "    @abstractmethod\n"
            "    async def generate(\n"
            "        self, prompt: str,\n"
            "        system: str = None,\n"
            "        temperature: float = 0.3\n"
            "    ) -> dict:\n"
            "        pass\n"
            "\n"
            "class OllamaProvider(LLMProvider):\n"
            "    '''Self-hosted open model.''' \n"
            "    def __init__(self,\n"
            "                 model='llama3.2'):\n"
            "        self.model = model\n"
            "    \n"
            "    async def generate(\n"
            "        self, prompt, system=None,\n"
            "        temperature=0.3):\n"
            "        msgs = []\n"
            "        if system:\n"
            "            msgs.append({\n"
            "              'role':'system',\n"
            "              'content':system})\n"
            "        msgs.append({\n"
            "          'role':'user',\n"
            "          'content':prompt})\n"
            "        async with httpx.AsyncClient(\n"
            "            timeout=300.0\n"
            "        ) as c:\n"
            "            r = await c.post(\n"
            "                'http://localhost:11434'\n"
            "                '/api/chat',\n"
            "                json={'model':self.model,\n"
            "                      'messages':msgs,\n"
            "                      'options':{\n"
            "                        'temperature':\n"
            "                          temperature}})\n"
            "            d = r.json()\n"
            "            return {\n"
            "                'content': d['message']\n"
            "                  ['content'],\n"
            "                'tokens': d.get(\n"
            "                    'eval_count',0),\n"
            "                'model': self.model,\n"
            "                'provider': 'ollama'}\n"
            "\n"
            "class DeepSeekProvider(LLMProvider):\n"
            "    '''Hosted open API.''' \n"
            "    def __init__(self,\n"
            "                 model='deepseek-chat'):\n"
            "        self.model = model\n"
            "        self.api_key = (\n"
            "          os.environ[\n"
            "            'DEEPSEEK_API_KEY'])\n"
            "    \n"
            "    async def generate(\n"
            "        self, prompt, system=None,\n"
            "        temperature=0.3):\n"
            "        # ... API call\n"
            "        pass\n"
            "\n"
            "# Multi-provider router\n"
            "class ModelRouter:\n"
            "    '''Route by task type.''' \n"
            "    def __init__(self):\n"
            "        self.providers = {\n"
            "            'sensitive': (\n"
            "              OllamaProvider(\n"
            "                'llama3.2:1b')),\n"
            "            'internal': (\n"
            "              OllamaProvider(\n"
            "                'qwen2.5:32b')),\n"
            "            'customer': (\n"
            "              DeepSeekProvider()),\n"
            "        }\n"
            "    \n"
            "    async def generate(\n"
            "        self, prompt, task_type,\n"
            "        system=None):\n"
            "        provider = (\n"
            "            self.providers[task_type])\n"
            "        return await (\n"
            "          provider.generate(\n"
            "            prompt, system))\n"
            "\n"
            "# Swap providers without\n"
            "#   rewriting business logic\n"
            "router = ModelRouter()\n"
            "# result = await router.generate(\n"
            "#     prompt, 'sensitive')"
        )

    def get_three_tier(self) -> str:
        """Three-tier hybrid strategy."""
        return (
            "# === THREE-TIER HYBRID ===\n"
            "# Based on TimeWell recommendation:\n"
            "# Top: commercial frontier for\n"
            "#   customer-facing work\n"
            "# Middle: open-weight for\n"
            "#   internal RAG and processing\n"
            "# Bottom: small OSS for\n"
            "#   sensitive PII/PHI\n"
            "\n"
            "THREE_TIER = {\n"
            "    'tier_1_customer': {\n"
            "        'use': 'customer-facing,'\n"
            "          ' reasoning, decisions',\n"
            "        'model': 'claude-sonnet',\n"
            "        'type': 'commercial',\n"
            "        'cost': 'premium',\n"
            "        'data': 'enterprise tier,'\n"
            "          ' zero retention'},\n"
            "    'tier_2_internal': {\n"
            "        'use': 'internal RAG,'\n"
            "          ' classification,'\n"
            "          ' structured extraction',\n"
            "        'model': 'qwen2.5:32b',\n"
            "        'type': 'open-self-hosted',\n"
            "        'cost': 'electricity',\n"
            "        'data': 'on-prem'},\n"
            "    'tier_3_sensitive': {\n"
            "        'use': 'PHI, PII,'\n"
            "          ' sensitive data',\n"
            "        'model': 'llama3.2:1b',\n"
            "        'type': 'open-self-hosted',\n"
            "        'cost': 'minimal',\n"
            "        'data': 'fully closed,'\n"
            "          ' air-gapped'},\n"
            "}\n"
            "\n"
            "# Route by data sensitivity\n"
            "def select_tier(\n"
            "    data_sensitivity: str) -> dict:\n"
            "    '''Select tier by sensitivity.''' \n"
            "    if data_sensitivity == 'pii':\n"
            "        return THREE_TIER[\n"
            "            'tier_3_sensitive']\n"
            "    elif data_sensitivity == (\n"
            "        'internal'):\n"
            "        return THREE_TIER[\n"
            "            'tier_2_internal']\n"
            "    else:\n"
            "        return THREE_TIER[\n"
            "            'tier_1_customer']"
        )

    def get_license_check(self) -> str:
        """License compliance check."""
        return (
            "# === LICENSE COMPLIANCE ===\n"
            "\n"
            "MODEL_LICENSES = {\n"
            "    'deepseek-r1': 'MIT',\n"
            "    'phi-4': 'MIT',\n"
            "    'qwen-3.5': 'Apache-2.0',\n"
            "    'mistral-large-3': (\n"
            "        'Apache-2.0'),\n"
            "    'llama-4': (\n"
            "        'Llama-Community'),\n"
            "    'llama-3.3': (\n"
            "        'Llama-Community'),\n"
            "    'gemma-3': 'Gemma-ToU',\n"
            "    'gpt-5': 'Proprietary',\n"
            "    'claude-4': 'Proprietary',\n"
            "    'gemini-3': 'Proprietary',\n"
            "}\n"
            "\n"
            "RESTRICTIONS = {\n"
            "    'MIT': 'none',\n"
            "    'Apache-2.0': 'none',\n"
            "    'Llama-Community': (\n"
            "        '700M MAU limit, '\n"
            "        'attribution required, '\n"
            "        'AUP restrictions'),\n"
            "    'Gemma-ToU': (\n"
            "        'Google can remotely '\n"
            "        'restrict usage'),\n"
            "    'Proprietary': (\n"
            "        'API-only, no weight '\n"
            "        'access, vendor terms'),\n"
            "}\n"
            "\n"
            "def check_license(\n"
            "    model: str,\n"
            "    mau: int = 0) -> dict:\n"
            "    '''Check license compliance.''' \n"
            "    license = MODEL_LICENSES.get(\n"
            "        model, 'unknown')\n"
            "    restrictions = RESTRICTIONS.get(\n"
            "        license, 'unknown')\n"
            "    \n"
            "    compliant = True\n"
            "    warnings = []\n"
            "    \n"
            "    if (license ==\n"
            "        'Llama-Community'\n"
            "        and mau > 700_000_000):\n"
            "        compliant = False\n"
            "        warnings.append(\n"
            "            '700M MAU limit '\n"
            "            'exceeded — need '\n"
            "            'Meta permission')\n"
            "    \n"
            "    if license == 'Gemma-ToU':\n"
            "        warnings.append(\n"
            "            'Google can remotely '\n"
            "            'restrict usage')\n"
            "    \n"
            "    return {\n"
            "        'model': model,\n"
            "        'license': license,\n"
            "        'restrictions': (\n"
            "            restrictions),\n"
            "        'compliant': compliant,\n"
            "        'warnings': warnings,\n"
            "    }\n"
            "\n"
            "# For commercial products,\n"
            "#   prefer MIT or Apache 2.0\n"
            "# check_license('deepseek-r1')\n"
            "#   -> MIT, no restrictions\n"
            "# check_license('qwen-3.5')\n"
            "#   -> Apache-2.0, no restrictions\n"
            "# check_license('llama-4',\n"
            "#   mau=800_000_000)\n"
            "#   -> not compliant"
        )

    def get_production_tips(self) -> dict:
        """Production tips."""
        return {
            "constraints_not_capability": "Open vs closed is a constraints decision, not a capability decision. Open models match frontier within single-digit percent on most tasks.",
            "data_sovereignty": "Self-hosted open weights: only path where data literally never crosses your network boundary. For defense, healthcare, classified data.",
            "cost_4_10x": "Open-weight via hosted providers runs 4-10x cheaper than frontier closed APIs. DeepSeek V4 Flash: $0.14/$0.28 vs GPT-5.5: $5/$30.",
            "breakeven_5_10m": "Self-hosting break-even at 5-10M tokens/month. Below: APIs simpler. Above: save 50-90% annually.",
            "license_matters": "MIT and Apache 2.0 fully permissive. Llama Community has 700M MAU limit and attribution. Gemma ToU: Google can restrict. For commercial: prefer MIT/Apache.",
            "multi_provider": "No single provider wins every axis. Use multi-provider architecture. Route by task type and data sensitivity.",
            "avoid_lock_in": "Abstract model interface. Use open standards like MCP. Avoid proprietary agent frameworks. Keep models loosely coupled.",
            "three_tier": "Three-tier: commercial frontier for customer-facing, open-weight for internal RAG, small OSS for sensitive PII/PHI.",
            "enterprise_tier": "Commercial enterprise tiers: zero data retention, SOC 2, HIPAA. Sufficient for most regulated industries. Contractual guarantees.",
            "fine_tuning_open": "Full fine-tuning only with open models. LoRA recovers 90-95% quality at 0.1-1% parameters. QLoRA on single GPU. Closed APIs: limited, provider-hosted.",
        }

Open-Source vs Commercial Checklist

[ ] Determine if open vs closed is a constraints decision, not a capability decision — gap is single-digit percent
[ ] Check data sovereignty: if data cannot leave network, self-host open-weight model is the only path
[ ] Check fine-tuning needs: full weight access only with open models — closed APIs offer limited, provider-hosted fine-tuning
[ ] Check volume: above 10M tokens/month, self-hosting or hosted open APIs are dramatically cheaper
[ ] Check vendor lock-in risk: if strategic risk, use multi-provider architecture with abstract interfaces
[ ] Check if you need to freeze a specific model version long-term — only possible with self-hosted open weights
[ ] Open-source self-hosted: data never crosses boundary, full fine-tuning, cost predictable at scale, MLOps required
[ ] Open-source hosted API: Together, Fireworks, Groq, Replicate offer Qwen, DeepSeek, GLM, Mistral, Llama at 4-10x discount
[ ] Commercial enterprise tier: zero data retention, SOC 2, HIPAA, no training on your data, contractual guarantees
[ ] Commercial frontier: GPT-5, Claude Opus, Gemini Pro for top-tier reasoning, multimodal, agentic coding
[ ] DeepSeek V4 Flash: $0.14/$0.28 per 1M tokens — order of magnitude cheaper than GPT-5.5 at $5/$30
[ ] Self-hosting break-even: 5-10M tokens/month — below that APIs are simpler and cheaper
[ ] Above break-even: self-hosting saves 50-90% annually
[ ] License: MIT (DeepSeek R1, Phi-4) — no restrictions, maximum legal safety
[ ] License: Apache 2.0 (Qwen 3.5, Mistral Large 3) — no restrictions, maximum legal safety
[ ] License: Llama Community (Llama 4, Llama 3.x) — 700M MAU limit, attribution required, AUP restrictions
[ ] License: Gemma ToU (Gemma 3) — Google can remotely restrict usage
[ ] License: Proprietary (GPT-5, Claude 4, Gemini 3) — API-only, no weight access, vendor terms
[ ] OSI has formally stated Llama is not open source — for commercial products, prefer MIT or Apache 2.0
[ ] Llama 4 multimodal version withheld from EU residents and EU-based companies
[ ] Use multi-provider architecture — no single provider wins on every axis
[ ] Abstract model interface — swap providers without rewriting business logic
[ ] Use open standards like Model Context Protocol (MCP) for agent interoperability
[ ] Avoid proprietary agent frameworks — creates deep vendor lock-in at orchestration layer
[ ] Keep models loosely coupled to agent platform
[ ] Three-tier strategy: commercial frontier for customer-facing, open-weight for internal RAG, small OSS for sensitive PII/PHI
[ ] For EU compliance: Claude via Bedrock/Vertex EU, GPT via Azure OpenAI EU, Gemini via Vertex AI EU, or self-hosted on EU infrastructure
[ ] Enterprise zero-retention tiers sufficient for most regulated industries — data processed, never stored, never trained on
[ ] Self-hosting only path for: defense, certain healthcare, customer data that is the product, R&D with strategic information
[ ] Fine-tuning: LoRA recovers 90-95% of full fine-tuning quality at 0.1-1% parameters
[ ] QLoRA: fine-tune 7B model on single RTX 4090, 70B on single A100
[ ] Tools: Unsloth 2x faster fine-tuning with 70% less VRAM, Axolotl YAML-driven pipeline
[ ] Qwen 3 ships with native MCP support — OSS camp leading on interoperability
[ ] For internal knowledge search and RAG: open-source in the lead (DeepSeek V4-Flash, Qwen 3-30B)
[ ] For Chinese models (DeepSeek, Qwen): use strictly on-prem or in domestic closed environment
[ ] Read choose LLM for model selection
[ ] Read RAG vs fine-tuning for knowledge approach
[ ] Read cost estimation for budget forecasting
[ ] Read Docker deployment for self-hosting setup
[ ] Test: multi-provider router correctly routes by task type and data sensitivity
[ ] Test: model swap works without code changes (abstract interface)
[ ] Test: license compliance check flags restricted models
[ ] Test: self-hosted model data never crosses network boundary
[ ] Test: three-tier routing sends sensitive data to small OSS model
[ ] Test: cost projection matches actual spend across providers
[ ] Document provider choices, license analysis, cost comparison, architecture diagram, compliance matrix

FAQ

What are the key differences between open-source and commercial AI models?

Open-source offers weight access, self-hosting, fine-tuning, and data sovereignty. Commercial offers frontier capability, managed infrastructure, and enterprise compliance. Areebi: "By mid-2026 the capability gap has narrowed enough that deciding factors are legal, structural, and operational — data residency, fine-tuning rights, audit access, transparency for ISO 42001 and SOC 2, and how each licence interacts with EU AI Act." JobsByCulture: "Leading open-weight models match frontier closed models within single-digit percentages on most production tasks at 4-10x lower per-token cost. The open vs closed decision is now a constraints decision, not a capability decision." AITENCY: "Claude leads on reasoning and writing. GPT leads on multimodal. Gemini leads on Google Workspace. Open source leads on data sovereignty and per-token economics at scale." Differences: (1) Open: weight access, self-host, fine-tune, data stays on-prem. (2) Commercial: frontier capability, managed infra, enterprise compliance, no weight access. (3) Cost: open 4-10x cheaper per token. (4) Capability gap: single-digit percentages on most tasks.

When should developers choose self-hosted open-source models?

When data cannot leave your network, you need fine-tuning, volume exceeds 10M tokens/month, or vendor lock-in is a strategic risk. JobsByCulture: "If regulated data — defense, healthcare, classified — self-host open-weight model. Only path where data literally never crosses your network boundary. Pick Qwen 3 or Mistral Large 3 for general work, GLM-5 for code, DeepSeek V4 for cost-sensitive volume." LetsDataScience: "Choose open models self-hosted when: you process more than 10M tokens per month, data must stay on infrastructure, you need to fine-tune on proprietary data, vendor lock-in is strategic risk, you want to freeze a specific model version long-term." TimeWell: "For high-volume workloads over 10M tokens/day, self-hosting marginal cost is roughly electricity and GPU depreciation — dramatically cheaper than per-token API." When: (1) Regulated data that cannot leave network. (2) Need full fine-tuning on proprietary data. (3) Volume >10M tokens/month. (4) Vendor lock-in is strategic risk. (5) Want to freeze model version long-term. (6) Need cost predictability at scale.

What are the licensing differences between open-source AI models?

MIT and Apache 2.0 are fully permissive. Llama Community License has restrictions. LetsDataScience: "Fully permissive MIT: DeepSeek R1, Phi-4 — no restrictions. Fully permissive Apache 2.0: Qwen 3.5, Mistral Large 3 — no restrictions. Conditionally permissive Llama Community: Llama 4, Llama 3.x — 700M MAU limit, attribution required. Restrictive Gemma ToU: Gemma 3 — Google can remotely restrict usage. Closed proprietary: GPT-5.x, Claude 4.x, Gemini 3.x — API-only, no weight access." Areebi: "OSI has issued formal statement that Llama is still not open source. For maximum legal safety in commercial products, MIT-licensed or Apache 2.0 models carry less risk." TimeWell: "Llama 4 community license: companies with more than 700 million MAU need separate license. Rights to multimodal version withheld from EU residents. For commercial products, stick with MIT or Apache 2.0." Licenses: (1) MIT: DeepSeek R1, Phi-4 — no restrictions. (2) Apache 2.0: Qwen 3.5, Mistral Large 3 — no restrictions. (3) Llama Community: 700M MAU limit, attribution, AUP. (4) Gemma ToU: Google can restrict usage. (5) Proprietary: API-only, no weights.

How do you avoid vendor lock-in with AI providers?

Use multi-provider architecture with abstract model interfaces and open standards like MCP. AITENCY: "The real risk is not picking the wrong model — it is architecting your system around a single API in a way that makes switching impossible six months later when prices change, models get deprecated, or compliance team rules out a US provider. Use multi-provider architecture to keep clients out of vendor lock-in." TimeWell: "A common stumble in hybrid strategy is lock-in at the orchestration layer. Riding a commercial vendor proprietary agent framework creates deep vendor lock-in. Center your stack on open standard like Model Context Protocol (MCP) and keep models loosely coupled to agent platform." JobsByCulture: "Most production systems should use more than one provider." Avoid lock-in: (1) Abstract model interface — swap providers without rewriting. (2) Multi-provider: route by task, not by brand. (3) Use open standards: MCP for agent interoperability. (4) Avoid proprietary agent frameworks. (5) Keep models loosely coupled. (6) Design for model swappability from day one. (7) Don't build around single API.

What is the cost difference between open-source and commercial AI?

Open-source models are 4-10x cheaper per token. Self-hosting break-even at 5-10M tokens/month. JobsByCulture: "Open-weight via hosted providers typically runs 4-10x cheaper than frontier closed APIs. DeepSeek V4 Flash is the typical default for cost-sensitive workloads in 2026." LetsDataScience: "Breakeven point between premium closed APIs and self-hosted open models is approximately 5-10 million tokens per month. Below that volume, APIs are simpler and cheaper. Above it, self-hosting saves 50-90% annually." TimeWell: "DeepSeek V4 Pro API pricing is USD 0.14 input / USD 0.28 output for Flash — literally an order of magnitude apart from GPT-5.5 USD 5 / USD 30." Cost: (1) Open via hosted API: 4-10x cheaper than frontier closed. (2) Self-hosted: break-even at 5-10M tokens/month. (3) Above break-even: save 50-90% annually. (4) DeepSeek V4 Flash: $0.14/$0.28 vs GPT-5.5: $5/$30. (5) Below 5M tokens: APIs simpler and cheaper. (6) Above 10M tokens: self-hosting dramatically cheaper.

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