Lower OpenAI bills for content

It’s never been easier to generate content, but harder to keep it cheap and consistent.

If you’re a Seed to Series A founder running content generation through the OpenAI API, you’ve probably had the same week I’ve had: you look at OpenAI billing, you swear your usage didn’t change, and somehow the number still climbed.

Here’s the part most pricing posts miss: OpenAI API pricing is only half the story. The other half is whether you keep paying for the same prompt tokens over and over.

As content generation becomes a real production system (not a weekend script), cost control stops being “nice to have” and turns into “this better not break our runway.”

OpenAI has been steadily pushing on this with Prompt Caching, which automatically discounts input tokens the model has recently seen. In their own write-up, OpenAI describes Prompt Caching as reusing recently seen input tokens to reduce costs and latency, with a 50% discount on cached input tokens in supported models. (Prompt Caching announcement)

This post is a practical workflow to lower token spend without breaking your brand voice. It’s not theory. It’s the set of guardrails we wish we had when we were duct-taping “content pipelines” together.

OpenAI API pricing is predictable, our prompts aren’t

When founders search OpenAI API pricing or OpenAI pricing, they’re usually looking for a per-token table.

That table matters, but the bigger lever is what causes you to pay for tokens repeatedly.

OpenAI billing is driven by:

• Input tokens (everything you send: system prompt, instructions, examples, retrieved context)
• Output tokens (the model’s response)

You can’t “coupon” your way out of output tokens. You lower them by tightening formats and asking for less.

Input tokens are where teams accidentally light money on fire, because content generation prompts tend to be huge and repetitive.

And that’s where API caching is real.

OpenAI’s Prompt Caching automatically applies to prompts longer than 1,024 tokens, caching the longest prefix the system has previously computed, starting at 1,024 tokens and increasing in 128-token increments. (OpenAI Prompt Caching announcement)

The docs go deeper:

• Caching works on exact prefix matches
• You should put static content first, variable content last
• You can influence routing (and cache hit rates) with prompt_cache_key
• Cache retention can be in_memory or extended (24h) on supported models

(Prompt caching docs)

So yes, token pricing matters. But your prompt structure decides how often you pay full price.

The content-pipeline problem: you pay for the same “static” words all day

Here’s the pattern I see in early teams:

• A long system prompt that encodes brand voice
• A pile of SEO instructions and formatting rules
• A “content brief” template
• A retrieval chunk (product pages, docs, past posts)
• Then the actual topic

Even if your topics change, 60–90% of that payload is the same every run.

If that static chunk is not identical, caching won’t help. If it’s identical but you run jobs sporadically, the in-memory cache window may not catch many hits.

OpenAI calls this out directly: caching is only possible for exact prefix matches, so put static content at the beginning and variable content at the end. (Prompt caching docs)

That sentence is the whole game for lowering OpenAI billing in content systems.

A founder-friendly workflow to cut token costs without silent prompt drift

When teams try to cut costs, they usually do one of two things:

• Chop the prompt and hope quality stays
• Switch to a cheaper model and accept more cleanup work

Both can work. Both can also quietly destroy your voice.

What you want instead is a workflow that treats prompts like code: versioned, reviewed, and tested.

Step 1: Split your prompt into three layers

If you want caching to actually land, you need stable prefixes.

I recommend splitting your prompt into:

1) The brand layer (cache this)

This is the “always true” stuff:

• Brand voice rules (tone, taboo phrases, point of view)
• Style guide snippets
• Output structure examples
• House rules like “don’t make claims without sources”

It should be long, detailed, and boring.

Most importantly: it should not change every request.

2) The policy layer (usually cache this)

This is where you put:

• SEO constraints (keyword placement, meta description format)
• Compliance notes (regulated claims, disclaimer language)
• Editorial QA checklist

If you update this layer, update it intentionally, and bump a version.

3) The job layer (do not cache)

This is the per-article payload:

• Topic
• ICP assumptions
• Angle
• Sources retrieved for this topic
• Anything dynamic like “today’s pricing table” pulled from a URL

This layer changes constantly, so it belongs at the end.

This layering does two things:

• Improves cache hit rates because your prefix stays stable
• Makes prompt changes observable, which prevents silent drift

Step 2: Make cache hits measurable, not vibes-based

If you’re serious about OpenAI billing, log usage on every call.

OpenAI includes cached_tokens in usage.prompt_tokens_details, so you can track what percentage of your input tokens were a cache hit. (Prompt Caching announcement, docs)

Two practical metrics I like:

• Cache hit ratio = cached_input_tokens / total_input_tokens
• Effective input cost = (uncached_input_tokens * uncached_rate) + (cached_input_tokens * cached_rate)

If cache hit ratio drops after a deploy, something changed in your prefix. That’s your bug report.

Step 3: Use prompt_cache_key like a routing hint

OpenAI’s docs explain that requests are routed based on a hash of the prompt prefix, and prompt_cache_key is combined with that hash, allowing you to influence routing and improve cache hit rates. (Prompt caching docs)

In content production, this is useful when you run multiple “families” of prompts:

• Blog post generation
• LinkedIn repurposing
• Landing page rewrite

Give each family a stable cache key.

Example:

• prompt_cache_key: "blog_v7"
• prompt_cache_key: "linkedin_v3"

When you bump a version, bump the key. It makes it obvious what changed, and you avoid mixing caches across incompatible prompts.

Step 4: Use extended cache retention for content bursts

If your pipeline runs in bursts (for example, you generate drafts on Monday, edits on Tuesday, refreshes on Friday), the default in-memory cache window can be too short.

OpenAI’s docs describe two retention policies:

• in_memory (cached prefixes generally remain active for 5–10 minutes of inactivity, up to one hour)
• 24h (extended retention, up to 24 hours) on supported models

(Prompt caching docs)

That 24-hour window is how caching goes from “nice for live chat” to “actually useful for batchy content work.”

One real engineering note from the docs: if you specify extended caching, that request is not considered Zero Data Retention eligible because key/value tensors may be held in GPU-local storage. Treat retention policy as a choice, not a default. (Prompt caching FAQ)

If you’re a founder, here’s the translation: extended caching is a cost win, but you should align it with your data/privacy posture.

Step 5: Cache the right things, not the tempting things

Here’s what I’ve learned to cache for content production.

Cache this

• System prompt (brand voice, tone, banned phrases)
• Examples of good outputs in your voice
• Structured output schema (if you’re using structured outputs)
• Tool definitions (if your pipeline uses tools consistently)

OpenAI explicitly notes that messages, images, tool definitions, and structured output schemas can be cached, as long as they’re part of the identical prefix. (Prompt caching docs)

Don’t cache this

• Retrieved context that changes per topic (docs chunks, SERP notes)
• Fresh pricing pulled from a page
• Anything user-specific

You can technically include these in the prefix, but then you’ll blow your cache key space and your hit rate will collapse.

Step 6: Stop silent prompt drift with prompt diffs and a QA gate

The hidden cost in AI content isn’t just token pricing. It’s rework.

Silent prompt drift looks like:

• Someone tweaks the system prompt “just a little”
• Your output subtly changes
• Two weeks later your blog reads like it’s written by three different companies

The fix is boring:

• Store prompts in git (or anything with history)
• Require a review on prompt changes
• Attach a prompt version to every generated artifact

Treat it like code because it behaves like code.

At Elevor, this is the mental model we build around: a publishing workflow with version-like control, so your “content teammate” doesn’t randomly change personality between releases.

If you’re not using Elevor, you can still steal the workflow:

• brand_prompt_v12.md
• policy_prompt_v4.md
• generator_prompt_v9.md

And in your database, store:

• model (because OpenAI API pricing varies by model)
• prompt_versions (brand, policy, generator)
• prompt_cache_key
• cached_tokens
• total_tokens

That dataset becomes your cost control system.

Quick math: why caching changes OpenAI billing fast

Caching is not a rounding error.

OpenAI’s own Prompt Caching post describes a 50% discount on cached input tokens, and their API pricing page lists “cached input” as a separate, cheaper rate for several models. (Prompt Caching announcement, API Pricing)

If your content run sends a 4,000 token prompt and 3,000 of those are stable prefix tokens:

• Without caching, you pay full input rates for all 4,000
• With caching, you may pay discounted rates for most of that 3,000

Multiply that across 30–200 pieces of content per month and it becomes a real line item.

The catch is you only get the discount when your prefix is identical.

That’s why this is not a “toggle caching” post. It’s a “design your prompts so caching can work” post.

The model choice that keeps quality while cutting cost

Founders often ask: “Should I just switch to the cheapest model?”

Sometimes, yes.

But in content, quality isn’t just “is the writing good.” It’s “does it sound like us, every time.”

Two practical approaches:

• Use a cheaper model for outlines and brief expansion, then a smarter model for the final draft
• Use the smarter model with caching-friendly prompts so your effective input cost drops

If you’re evaluating models, start with the official OpenAI pricing page and calculate real costs using your token mix (input vs output, cached vs uncached). The list price is not your realized price once you factor in caching.

A simple cost-control checklist you can hand to your engineer

If you only do a few things after reading this, do these:

• Put static prompt content first, dynamic content last (exact prefix match matters)
• Log usage including cached_tokens for every call
• Use prompt_cache_key consistently per prompt family
• Use extended retention (prompt_cache_key: "24h") when it matches your team’s data/privacy posture
• Version prompts and store versions with every generated artifact

This is the boring foundation for lower OpenAI bills.

It also happens to be the foundation for consistent brand voice.

Where Elevor fits, if you want a teammate not a pile of scripts

I built Elevor because early teams keep getting stuck in the same loop: you can either publish consistently, or you can keep quality consistent, but doing both without hiring a content team gets painful fast.

Elevor’s job is to make content production feel like shipping software:

• Brand voice locked in
• Editorial checks automated
• Publishing workflow with version control

If you’re already generating content through the OpenAI API, this workflow is the “start here” path.

If you want the teammate version of it, Elevor is what we’re building. (Why we built Elevor)