Table of Contents
Internal AI platform — vLLM + observability + auth + routing — is a real investment. The build-vs-buy question shifts as your scale grows. The right answer changes between seed and Series B.
Build when: cost-anchored at scale, strict residency, specific custom needs (multi-LoRA per tenant, particular model). Buy when: under Series A, experimentation phase, no AI infra team. Hybrid: many teams use Databricks for fine-tuning + self-hosted vLLM for inference. Most production teams in 2026 build the inference platform; buy training / fine-tuning tooling.
When to build
- Cost economics: above ~30M tokens/month, self-hosted is meaningfully cheaper
- Strict residency: regulated data + UK / EU residency requirement
- Custom serving needs: multi-LoRA per tenant, specific model, custom routing logic
- You have AI infra team: 2+ engineers can build + maintain
When to buy
- Pre-Series-A: focus on product, not infrastructure
- Bursty / experimental: usage doesn't justify dedicated capacity
- No AI infra team: Databricks Mosaic / Vertex AI / SageMaker abstract operational complexity
- Need frontier-class quality: hosted API for hardest cases
Hybrid
Common 2026 pattern:
- Inference: self-hosted vLLM on dedicated GPU
- Fine-tuning / training: Databricks Mosaic / SageMaker / Vertex AI for one-off heavy training runs
- Frontier fallback: hosted API (Claude / GPT-4o) for hardest cases
- Prompt management: PromptLayer / Braintrust / Helicone (specialist platform)
- Eval: RAGAS / custom in-house
Verdict
For inference platform, build is the right call above ~Series A scale. For fine-tuning / training, buy the specialist platform unless you're doing it monthly+. For prompt / eval / monitoring, hybrid — combine open-source primitives (LangChain / RAGAS / Grafana) with specialist platforms where they save real time.
Bottom line
Build inference; buy training. Hybrid prompt / eval. See stack blueprint.
