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LLMArchitecture

Three Ways to Wire LLMs into Your Product: Direct, Gateway, Private

Direct API access, an API gateway, or self-hosted open models — which route fits which stage of a team? A comparison across cost, reliability and compliance.

The first decision in adopting large language models is not the prompt — it is the wiring. Teams we work with almost always end up on one of three routes.

Route 1: Direct to the provider

Sign up with OpenAI or Anthropic, grab a key, call the API.

  • Upside: the shortest path, day-one access to new models, no intermediary to trust.
  • Cost: every extra provider means another account, another invoice, another SDK quirk to absorb; some regions add network and payment friction on top.
  • Fits: a single model, a single team, the validation stage.

Route 2: Through an API gateway

Put an OpenAI-compatible gateway (such as our own product, Taylent AI) between your services and the model vendors, collapsing many providers into one key and one bill.

  • Upside: switching models becomes a parameter change; usage and billing live in one dashboard; the gateway layer can handle load balancing, retries and concurrency.
  • Cost: you introduce a middle party, so its reliability and data handling deserve scrutiny.
  • Fits: organizations juggling several models, watching costs, or distributing quota to multiple teams and customers.

Route 3: Self-hosted open models

Run Qwen, DeepSeek or Llama on your own GPUs.

  • Upside: data never leaves your network — the lightest compliance burden; at sustained high volume, marginal cost drops below API pricing.
  • Cost: heavy upfront investment in hardware, operations and tuning; open models usually trail the closed frontier by a margin.
  • Fits: finance, healthcare and government scenarios with hard compliance walls, or mature workloads with large, stable volume.

Our take

These routes are not mutually exclusive. The sensible progression for most teams: start direct or on a gateway while validating → consolidate behind a gateway at scale → carve out private deployment for the sensitive slice. The one mistake to avoid is committing to a heavy self-hosted build before your volume and compliance requirements are actually known.

Facing this decision right now? Talk to us — with the business context on the table, thirty minutes is usually enough to pick a direction.