--- name: together-cost-tuning-d5a3dec17b71 description: >- Reduce Together AI spend using measured token usage, live per-model prices, cached-input evidence, batch discounts, model evaluation, and dedicated break-even analysis. Use when forecasting or optimizing Together workloads. Trigger with "Together cost", "optimize Together spend", or "Together batch savings". argument-hint: "[repository-path] [usage-window] [budget]" allowed-tools: Read, Glob, Grep, WebFetch, Write, Edit version: 1.9.0 author: Jeremy Longshore license: MIT tags: - saas - together-ai - cost-optimization model: inherit effort: high compatibility: Designed for Claude Code; current estimates require Together AI pricing and usage data --- # Together AI Cost Tuning ## Overview This skill builds a reproducible cost model from actual usage and current pricing rather than embedding a price table that will drift. ## Prerequisites - A representative usage window with input, cached-input, output, and request counts - Current model catalog/pricing and billing analytics access - Quality, latency, context, and availability requirements - A monthly budget and an owner for model or capacity changes ## Tool Discipline Use `Read`, `Glob`, and `Grep` to find model selection, token bounds, caching, batch, and telemetry logic. Use `WebFetch` only for current official pricing and eligibility. Use `Write` or `Edit` for an approved cost model, instrumentation, or configuration change. ## Current Contract - Serverless models bill from current per-model input/output rates; some expose discounted cached input. - Eligible batch work can cost up to 50% less, but eligibility and discount vary by model. - Dedicated Model Inference bills per minute per running replica and hardware, not per token. - Usage and prices change; snapshot retrieval time and source with every forecast. ## Authentication Billing and usage views require authorized Together project access. API workloads use `TOGETHER_API_KEY`; record aggregate usage and project alias only, not the credential or sensitive request content. ## Instructions 1. Group measured requests by model, workload, token class, latency, and success state. 2. Fetch current pricing and batch eligibility, recording retrieval time and source. 3. Reconcile calculated cost with Together billing analytics before proposing savings. 4. Evaluate output bounds, prompt reuse/caching, smaller models, and asynchronous batch in that order. 5. Benchmark quality and latency before shifting models or endpoint type. 6. Compare steady utilization with dedicated per-minute capacity, then publish forecast, risk, rollback, and owner. ## Approval Boundaries Do not change a production model, reduce quality/safety controls, submit batch jobs, or provision dedicated replicas solely from a spreadsheet estimate. ## Output Return source-stamped prices, usage baseline, reconciled cost, option-by-option savings, quality/latency evidence, break-even assumptions, recommendation, and rollback. ## Error Handling | Condition | Response | |---|---| | Usage lacks token fields | Add measurement before estimating savings. | | Catalog and invoice diverge | Use billed data for history and current catalog for forward scenarios. | | Batch model ineligible | Price synchronous or another explicitly tested model. | | Dedicated utilization uncertain | Run a bounded capacity test; do not provision from peak guesses. | ## Examples The example below shows the minimum redacted evidence expected from a successful invocation of this operator workflow. ```text baseline=reconciled; prices=live-snapshot; option=batch; savings=modeled; quality=gate-required ``` ## Resources - [Skill-specific official documentation](references/official-docs.md) - [Serverless models and pricing](https://docs.together.ai/docs/serverless/models) - [Usage and cost analytics](https://docs.together.ai/docs/billing-usage-limits) - [Batch overview](https://docs.together.ai/docs/inference/batch/overview) --- Source attribution: jeremylongshore Original project: https://github.com/jeremylongshore/claude-code-plugins-plus-skills/blob/ab85a061f7bcc039dac13ee193de7a4cd4388571/plugins/saas-packs/together-pack/skills/together-cost-tuning/SKILL.md License: MIT License information: https://opensource.org/license/mit Original upstream instructions; downloaded from Mega AI Hub. ## Upstream license notice MIT License Copyright (c) 2025 Jeremy Longshore Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. # Mega AI Hub resource metadata Creator / publisher: jeremylongshore Original project: https://github.com/jeremylongshore/claude-code-plugins-plus-skills/blob/ab85a061f7bcc039dac13ee193de7a4cd4388571/plugins/saas-packs/together-pack/skills/together-cost-tuning/SKILL.md Attribution basis: Source repository record Fields of work: Data & Analytics, Development & Engineering, Product Management Roles: Data Scientist, ML / AI Engineer, Product Manager Specialties: API Tools: Together Ai Country applicability: Country not specified Classification basis: Source catalog labels Instruction SHA-256: 631dd0ed2b4a2c2c2b978d67fb823644f35ba769c5194c08b4b4af5be0dd5969