Inference Cost Baseline Before Gateway Migration (Onboarding)

Inference Cost Baseline Before Gateway Migration (Onboarding)

1. 01

   Instrument current per-task token spend across every client workflow before touching a base URL

   Pull 30 days of usage logs from the incumbent provider and tag each call by client, workflow, and model. Forrester's tokenomics analysis warns that agentic workflows billed by consumption can erode margin when nobody audits them at design stage, so the baseline has to exist before the swap.

2. 02

   Convert raw token counts into a cost-per-deliverable figure the account team can defend

   Express spend as cost per landing page draft, per support macro, per research brief. A 50 percent prompt-level reduction is meaningless to a client unless it maps to a line item on their retainer.

3. 03

   Classify each workflow by determinism requirement and context length

   Reproducible outputs (regression tests, structured extraction, client-facing QA checks) belong on a host that guarantees byte-identical responses. Creative drafting tolerates variance and can sit on a cheaper tier.

4. 04

   Map the open-weight model candidates against each workflow's context ceiling

   A 256k window covers long client documents; a 128k window with a two-session-per-key cap will break batch jobs that fan out in parallel. Note where a 27B-class open model is sufficient and where a frontier model still earns its price.

5. 05

   Price two billing shapes side by side: metered per-token and fixed-duration passes

   Time-boxed unlimited passes (1 hour to 3 days) suit bursty delivery weeks like campaign launches and audit sprints. Steady always-on client agents usually cost less under metered billing. Run both numbers against the baseline from step one.

6. 06

   Write the swap contract: base URL, SDK version, auth header, and rollback path in one document

   The whole premise of an OpenAI-compatible endpoint is that the client integration survives the move. Record the exact environment variables and the one-line revert so a junior engineer can execute it at 2am.

7. 07

   Get written client sign-off on data residency and model provenance before the first production call

   Open-weight hosting means the weights and the GPUs are someone else's operational problem. Clients with regulated data will ask where inference runs and who can read prompts, so answer that in the migration ticket, not after go-live.