How to Reduce LLM API Costs: Cost-First Routing, Context Compression and Caching

Most LLM spend comes from two habits: sending every request to a frontier model even when a smaller model would do, and resending long system prompts or repeated questions verbatim. This guide covers four practical levers to reduce your API bill—model routing, context compression, caching and key-level spend caps—and where each one has trade-offs. We build SentioGate, a hosted BYOK router that implements these controls, and we note its limits below.

As of 2026-10-26. Provider pricing and features change; verify current token rates on each provider's pricing page.

1. Stop hardcoding a single expensive model

Input and output token prices across current models vary by more than an order of magnitude. Classification, extraction, summarization and boilerplate code edits rarely need your most expensive model, while hard reasoning tasks do.

Instead of scattering model names across your codebase:

• Curate an allowlist of models you have tested for your workload across two or three providers (for example OpenAI, Gemini and Groq).
• Route by cost and health. In SentioGate, sending model: "auto" ranks your enabled models per request by cost, latency and current health, and fails over if the cheapest option returns HTTP 429 or 5xx.
• Pin models only when required. When a specific step truly needs a frontier model, pass that explicit provider/model ID.

2. Avoid paying a gateway markup on top of token costs

How you route affects your bill too:

• Direct BYOK keys: With a BYOK router like SentioGate, providers bill your accounts directly at 0% token markup, and the router charges a flat monthly plan (free during the 90-day beta for up to 25,000 requests/month, then from $9/month; see pricing).
• Credit aggregators: OpenRouter charges list price for inference plus a 5.5% credit purchase fee on Standard (or 5% on BYOK above $25,000/month of list-price inference). See our OpenRouter alternative guide for when each model comes out ahead.

3. Trim input tokens and cache repeated prompts

• Set realistic max_tokens. High max_tokens ceilings eat into your tokens-per-minute quota and invite verbose replies.
• Use provider prompt caching on long, static prefixes (OpenAI and Anthropic both discount cached prefix tokens when prompts share an identical prefix).
• Use semantic caching where appropriate. For workloads with repeated user questions (such as documentation bots or FAQ assistants), semantic caching returns a cached completion on near-duplicate prompts. Always turn caching off for non-deterministic or user-private queries; see our LLM semantic caching guide.
• Optional context compression. SentioGate includes optional prompt context compression for long inputs; test it on your prompts first to confirm it preserves every detail your task relies on.

4. Put spend thresholds on your keys

A runaway loop or background job can burn through a prepaid balance in minutes. In SentioGate's Vault you can configure per-key spend and balance pause thresholds so a key pauses automatically before exhausting your account budget.

import os
from openai import OpenAI

client = OpenAI(
    base_url="https://api.sentiogate.dev/v1",
    api_key=os.environ["SENTIOGATE_API_KEY"],
)

resp = client.chat.completions.create(
    model="auto",  # cost-first routing across your enabled Vault models
    messages=[{"role": "user", "content": "Tag this feedback as billing, bug, or feature."}],
)
print(resp.choices[0].message.content)

SentioGate's limits

• Cheaper models can produce lower-quality answers on complex tasks. Only enable models in your Vault that pass your own evaluation checks.
• Hosted only, with no Anthropic /v1/messages endpoint and no MCP gateway today. See our gateway comparison.
• Community pool is for evaluation only: without BYOK keys, requests use a shared community evaluation tier (150 requests per 5 hours per user during beta).

See also our BYOK LLM gateway guide and API docs.

Sources

All accessed 2026-10-26.

• OpenRouter: Pricing
• SentioGate: docs, pricing, live benchmarks