Frontier-to-Open-Source Migration Checklist
Moving an LLM workload off a frontier API and onto self-hosted open weights is not one decision, it is six phases of them. This interactive checklist walks you through all six: assess what you have, select a candidate model, prove it on a pilot, serve it on real infrastructure, operate it safely, and decide with the total cost of ownership in front of you. Tick items as you complete them. Your progress saves in this browser, and you can print the whole thing or save it as a PDF to share with your team.
Read this first: migration is not always the right answer. Self-hosting swaps a per-token bill for a fixed infrastructure bill plus a payroll bill, and it only pays off at high, steady volume with in-house operations talent. The final phase, and the "keep on frontier when" section below it, exist to talk you out of it when the numbers say so.
Free version. Figures reused from our verified Open Source series (verified June 30, 2026). Living-data: re-checked quarterly or on a major open-model release.
Frontier-to-Open-Source Migration Checklist · Tech Jacks Solutions · techjacksolutions.com/ai-tools/open-source/
- Map your workloads. Separate routine, high-volume tasks from high-stakes reasoning. Only the routine slice is a strong migration candidate; the hard slice may stay on frontier.
- Measure current frontier spend. Record your monthly token volume and dollar cost. These are the inputs to every break-even calculation later.
- Gauge lock-in exposure. List every workflow bound to a single provider's API, and the deprecation or policy risk if that model changes under you.
- Define compliance and sovereignty needs. Flag any data-residency or on-prem mandate. Self-hosting keeps prompts inside your network, which can offset the cost premium.
- Confirm ML operations capacity. You have, or can hire, staff to run GPUs, drivers, a serving engine, and updates. Without that team, a self-build carries real risk of stalling before it ever reaches production.
- Shortlist by size class. Match model size to hardware: 1B to 7B on consumer GPUs, 7B to 70B on mid-tier cards like an A10 or L4, 70B and up on A100 or H200 class.
- Run the licensing check. Open-weight is not the same as open-source. Apache 2.0 and MIT give commercial freedom; some flagship models ship under restrictive community licenses; a few weights are source-available or research-only. Read the model card.
- Match capability to the hardest task. Size the model to the toughest job it must do reliably, not the average one.
- Sanity-check the small-model bet. Confirm a 7B to 8B model plus RAG can plausibly match a larger model on your task before you commit; a 7-8B model with RAG can rival a 13B one.
- Build an evaluation set from real tasks. Score the open candidate against your current frontier output as the baseline, on inputs that look like production.
- Close the gap with fine-tuning and RAG. A fine-tuned small model (LoRA) grounded with retrieval over your own data is the pattern that reaches frontier-adjacent quality, not the base model alone.
- Run a parallel pilot. Serve a slice of production traffic through the open path alongside frontier so you can compare on live inputs before any cutover.
- Set an explicit quality threshold. Decide the score the open model must clear to cut over, and the fallback if it does not. Write it down before the pilot, not after.
- Size the hardware. Weight memory in GB is roughly the parameter count in billions times bytes per parameter (about 2 at 16-bit, about 0.5 at 4-bit) plus 20 to 30 percent for KV cache and overhead. A 70B model wants roughly 140GB at 16-bit or 35GB at 4-bit.
- Pick a serving engine. Ollama for a single box or dev, vLLM for production throughput (PagedAttention plus continuous batching, OpenAI-compatible), TGI as an HF-native option, TensorRT-LLM for 2x to 3x throughput on NVIDIA.
- Quantize to fit memory. Moving from FP32 to INT8 or FP16 cuts memory 2x to 4x with minimal quality loss, and quantization plus serverless has cut inference cost about 40 percent.
- Plan tokens and throughput. Use continuous batching to lift GPU utilization from under 30 percent to 70 to 90 percent, trim output length (decode is the slow phase), and cache stable prompt prefixes.
- Put a gateway in front. A routing layer gives you fallback, rate limiting, observability, and the ability to send only the hard queries to a frontier API.
- Stand up monitoring. Track GPU utilization, latency, cost per request, and KV cache pressure. The KV cache alone can consume 40 to 60 percent of GPU memory on long-context work.
- Set an eval cadence. Re-evaluate on every model update and whenever your data drifts. Open models update often, and a quantization that looked fine in a demo can underperform under real traffic.
- Harden the self-hosted attack surface. Apply least privilege (about 90 percent of deployed agents are over-permissioned), sandbox tools, and patch the serving proxy. Self-hosted LLM proxies have shipped critical CVEs, including remote code execution.
- Own the lifecycle. Plan model updates, re-validate quality after every re-quantization, and keep a tested rollback path.
- Compute the TCO break-even. Compare fixed infrastructure plus marginal token cost against your frontier per-token bill. Use our TCO breakdown and calculator for the full math.
- Add the hidden costs. The compute math misses the 60 to 80 percent idle penalty on bursty traffic, operations headcount, and a risk reserve for the more than 40 percent of agentic projects that fail.
- Keep frontier where it wins. Low or bursty volume, no ML operations staff, top-end reasoning, and frontier-only multimodal all still favor a managed API. See the section below.
- Prefer hybrid over all-or-nothing. Route cheap, routine traffic to the open model and reserve frontier for the hard slice. Tiered routing reports 40 to 85 percent bill cuts.
Keep On Frontier When
Finishing this checklist does not mean you should migrate. A frontier API is often the lower total cost of ownership. Before you cut over, check whether any of these describe you, because each one is a reason to stay put or to keep that slice of traffic on a managed model.
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This free checklist gets you readiness. The premium playbook gets you through the rollout: the deeper, worked version of every phase, plus the templates a team actually needs to run the migration across an organization without reinventing them.
- Worked TCO break-even model with your utilization and ops headcount
- Model selection scorecard and licensing decision tree
- Pilot evaluation rubric and cutover go / no-go gate
- Serving and quantization runbook per model size class
- Self-hosted security hardening baseline and review cadence
- Stakeholder briefing deck and phased rollout timeline
NVIDIA, H100, A100, and H200 are trademarks of NVIDIA Corporation. Llama is a trademark of Meta. Ollama, vLLM, and Hugging Face TGI are the property of their respective owners. All product names and brand identifiers are the property of their respective owners. Tech Jacks Solutions has no commercial relationship with the vendors named. This tool is editorially independent and its figures are sourced, not estimated.