Open source or commercial LLMs? My 60-second playbook

A 60-second decision framework I rely on

I choose open source when I need:

• Maximum control, strict data residency, and on-prem or VPC deployment.
• The lowest per-inference cost at sustained, predictable volume.
• Deep customization (prompts, adapters, fine-tuning, inference settings).
• In-house engineering to run GPU infrastructure, observability, and MLOps.

I choose commercial when I need:

• The best quality out of the box and the fastest time-to-value.
• Advanced reasoning, long context windows, and structured outputs.
• Enterprise SLAs, safety tooling, privacy commitments, and mature ecosystems.
• Low ops lift, rapid experimentation, and built-in governance features.

In practice, I map "when to choose each" against my context:

• Open source: on-prem/VPC, strict privacy, custom workflows, high steady volume, fine-tuning on private data, strong MLOps maturity.
• Commercial: variable or low volume, tight deadlines, long context, vendor SLAs, minimal ops, fast iteration, built-in guardrails.

What LLMs are - and how I adapt them

LLMs are neural networks trained on text to predict the next token. They power drafting, summarization, extraction, classification, and reasoning. For a succinct explainer, see Cloudflare: What is a large language model?. For background on transformer-based models, this overview is helpful: Springboard on GPT-3 and transformers.

The labels matter:

• Open source: code and weights available under an open license.
• Open weight: weights available but with use restrictions.
• Proprietary: closed models accessed via APIs or managed services.

How I adapt them:

• Full pretraining: rarely feasible for enterprises.
• Fine-tuning: adjust a base model with my data for target tasks.
• Prompt engineering: structure prompts and system messages for consistent behavior.
• RAG: retrieval-augmented generation that injects fresh, private context at query time.

Most enterprise wins combine prompt engineering, RAG, and light fine-tuning. I reserve heavier training for narrow, high-ROI use cases where the lift justifies the added ops and governance.

When open source fits best

Licensing matters

• Permissive (Apache 2.0, MIT): flexible for commercial use.
• Restricted (e.g., community licenses): read terms on redistribution, usage caps, and derivatives. I confirm with legal before rollout.

Pros

• Transparency: I can review what runs in my environment.
• Privacy control: prompts and outputs can stay inside my perimeter.
• Cost efficiency at scale: per-inference cost drops when utilization is high and volume is steady.
• Customization: full control over prompts, adapters, fine-tuning, and inference settings.

Cons

• Infra and ops: GPU capacity planning, autoscaling, observability, patching.
• MLOps maturity: CI for prompts, eval pipelines, canarying, and rollbacks.
• Security hardening: secrets, KMS, network segmentation, supply-chain checks.
• Talent: ML, DevOps, data engineering, and analysts to run evals and watch drift.

Where it shines in B2B services

• On-prem knowledge assistants that must never leave my network.
• PII-safe document processing with in-house redaction and audit logs.
• RFP drafting on private libraries of past bids and win themes.
• Call-note summarization that stays inside my compliance boundary.
• Contract review tied to a proprietary clause library.

Skills and building blocks I plan for

• Skills: DevOps for GPUs/queues, MLOps for evals/versioning, data engineering for RAG pipelines and vector stores, security for IAM and network controls.
• Components: model gateway/router, model server, vector database, guardrails, policy engine, and end-to-end observability (latency, errors, and cost).

When commercial LLMs fit best

What I usually get

• Strong reasoning on complex tasks and long inputs.
• Managed APIs/SDKs with function calling and structured outputs.
• Enterprise SLAs, incident response, and role-based access.
• Zero-retention options and private endpoints to tighten data handling.
• Mature ecosystems for prompt governance, safety filters, and analytics.

Pros

• Fastest time-to-value with polished capabilities.
• Better performance on advanced reasoning and very long context.
• Dedicated support and uptime targets, including latency SLOs.
• Rich safety, content moderation, and monitoring options.

Cons

• Cost at scale if usage grows quickly.
• Limited control over model internals.
• Vendor lock-in risk and migration friction (I reduce this by abstracting interfaces and tracking per-request metrics).

Leading examples (evolve quickly)

• OpenAI GPT-4.1 and GPT-4o.
• Anthropic Claude 3.5 and 3.1.
• Google Gemini 1.5 family.
• Cohere Command R and Command R+.

B2B services use cases I prioritize

• Proposal strategy and executive summaries that benefit from higher-order reasoning.
• Marketing content QA and brand tone checks.
• Multilingual support across client geographies.
• Complex data extraction with structured JSON output.

Privacy and data handling I review

• Zero-retention modes, regional endpoints, and data residency options.
• Terms governing training on my data.
• Private networking paths (e.g., VPC-style peering) for stricter control.

The real economics: TCO and break-even math

I group total cost of ownership into five buckets:

• Build: setup, license review, security architecture, networking, and integrations.
• Run: compute or tokens, hosting, storage, vector search, egress. Open source includes GPU spend or amortized hardware; commercial is usually per-token plus any platform fees.
• People: ML/DevOps, app engineers, prompt engineers, analysts for evals, and project management.
• Governance: audits, eval pipelines, red-teaming, monitoring, documentation, and change control.
• Risk: downtime, model drift, data leakage, and incident response.

A mini-calculator I use

• Tokens per request × requests per month = total tokens.
• Variable cost = total tokens ÷ 1,000,000 × price per million tokens.
• Total monthly TCO = variable cost + monthlyized fixed costs (people, governance, infrastructure).

Sample scenario for a B2B service firm

Assumptions

• 1 million requests/month.
• 1,000 tokens/request on average.
• Total tokens: 1 billion/month.

Commercial model

• Average blended price: 10 dollars per million tokens.
• Variable cost: 1,000 million × 10 = 10,000 dollars/month.
• Fixed ops: minimal - assume 5,000 dollars for integration and monitoring.
• Estimated monthly TCO: about 15,000 dollars.

Open-source model

• Amortized infra and people: 25,000 dollars/month for GPUs, storage, security, and a small team portion.
• Variable inference: 2 dollars per million tokens.
• Variable cost: 1,000 million × 2 = 2,000 dollars/month.
• Estimated monthly TCO: about 27,000 dollars.

Break-even logic

• Fixed cost difference: 25,000 − 5,000 = 20,000 dollars.
• Per-million token difference: 10 − 2 = 8 dollars.
• Break-even volume: 20,000 ÷ 8 = ~2,500 million tokens/month.

My takeaway: at low or spiky volume, commercial pricing is often cheaper and faster to capture. At very high, steady volume with capable in-house teams, open source can win on variable cost - if I keep utilization high and latency within targets. Prices and context usage change, so I re-run this math quarterly.

Security, performance, and a pragmatic hybrid

Compliance framing I align to

• SOC 2 and ISO 27001 for policy, access control, audit logging, change management.
• HIPAA for BAAs, encryption in transit/at rest, and strict PHI handling.
• GDPR/UK GDPR for data residency, consent, right to erasure, DPIAs.
• CCPA for consumer rights, deletion, and restricted sharing.

Controls I plan and document

• Data residency and segregation by client or region.
• Encryption, key management, and rotation schedule.
• Least-privilege access with SSO and step-up auth for sensitive actions.
• Audit logs for prompts, retrieved context, outputs, and actions taken.
• DLP and PII redaction before prompts hit a model.
• Safety filters, content policy enforcement, and periodic red-teaming.
• Model governance: model cards, versioning, approval workflows, and prompt lineage.

How the model path intersects with compliance

• Open source: full control of storage and traffic on-prem/VPC; I own BAAs, DPIAs, and audit evidence.
• Commercial: zero-retention modes, private endpoints, and vendor SLAs; I still own consent, minimization, and access control.

Performance and benchmarking I trust

Evaluate with representative tasks and metrics, then weight by business impact. For deeper comparisons across models, this overview can help: comparison of all major models.

• Representative tasks: summarization, extraction, classification, long-form generation, retrieval-heavy RAG.
• Metrics: accuracy/F1, faithfulness, p95 latency, cost per 1,000 tokens, deflection rate where relevant.
• Weighted scorecard: I weight by business impact, not just raw scores.
• Data: public benchmarks (e.g., MMLU, GSM8K, HellaSwag) for baseline; domain data (contracts, support logs, proposals) for decisions that matter.
• Practices: human review on a sample set, track failures and near misses, monitor drift and trigger re-evals.

A hybrid I use when stakes vary

• Routing: open source for inexpensive, PII-heavy, or latency-critical tasks; commercial for complex reasoning and very long context.
• Fallback: if the primary model errors or exceeds latency SLOs, retry on a backup; if confidence is low, trigger a human review or a second-model vote.
• Budget guardrails: if spend exceeds a threshold, throttle optional tasks or switch routes.

Micro case studies for B2B services

• Support deflection: route common, PII-heavy FAQs to an on-prem open-source LLM with redaction and logging; send complex escalations to a commercial model. Result: lower cost per interaction with higher CSAT.
• Proposal automation: use a commercial model for executive summaries and win themes, paired with an on-prem RAG pipeline that fetches client history and pricing tables. Result: faster proposals with a clean privacy story.
• Research and drafting: open-source LLMs summarize internal notes safely; commercial models handle long-context competitor reports and multilingual checks. Result: fewer manual hours and more strategic focus.

The choice is not binary. I start where I can win in the next quarter, keep my compliance story tight, and design for optionality so I can evolve toward the volume and control I expect later.