LLM API Token Costs: At What Scale Do They Start Hurting Your SaaS?
TL;DR
LLM API costs can seem negligible at first, but they have a nasty habit of sneaking up on you as your user base grows. The SaaS community on Reddit has been wrestling with exactly when token costs become a real business problem — and the answer depends heavily on your use case, model choice, and pricing strategy. Self-hosting alternatives like RunPod and Lambda Labs exist, but they come with their own tradeoffs. If you’re building anything LLM-powered, you need to think about cost architecture before you scale, not after.
What the Sources Say
A recent Reddit thread in r/SaaS titled “At what scale do LLM API token costs start hurting you?” surfaces a question that’s clearly on a lot of builders’ minds right now. While the thread itself is relatively small (3 comments, score of 3 at time of research), the topic it raises is one of the most practically important for anyone shipping AI-powered products in 2026.
The core tension is straightforward: LLM APIs are priced per token, which makes them feel “cheap” at low volumes. You’re building, you’re testing, you’re demoing — and your API bill is maybe a few dollars a month. Then you get real users. Then you get users who actually use the product. And suddenly you’re staring at a bill that doesn’t fit neatly into your unit economics.
The scale problem isn’t linear. That’s what catches most builders off guard. If your product involves any kind of back-and-forth conversation, summarization of long documents, or agentic loops where the model calls itself multiple times, your token consumption per user session can balloon fast. A user who “does 10 things” in your app might be responsible for 50,000 tokens if each action involves context-heavy prompts and lengthy completions.
The consensus from the SaaS builder community seems to be that costs start becoming a genuine concern somewhere in the range of hundreds to low thousands of active users — but this varies dramatically based on:
- How long your prompts are. System prompts that run 2,000 tokens get sent with every request.
- Which model you’re using. More capable models (like Claude 4.5/4.6 or GPT-5) cost more per token than lighter models.
- Whether you’re caching properly. Prompt caching can cut costs significantly for repeated context.
- How you’ve priced your product. If you’re charging $10/month per user but spending $8 on API calls, you have a problem.
The discussion also touches on a broader architectural question: at some point, does it make more sense to stop paying per-token and start paying for compute directly?
Pricing & Alternatives
The source package highlights three main paths builders take when API costs start becoming painful:
| Option | What It Is | Best For | Key Consideration |
|---|---|---|---|
| LLM APIs (OpenAI, Anthropic, etc.) | Pay-per-token cloud APIs | Early stage, variable load | Costs scale with usage; no infrastructure overhead |
| RunPod | GPU cloud instances for self-hosting | Medium-to-high volume, predictable load | Fixed monthly costs; requires model ops expertise |
| Lambda Labs | Dedicated GPU instances for training/inference | High volume or model fine-tuning | More infrastructure ownership; lower per-query cost at scale |
| n8n | Open-source workflow automation | Orchestrating multi-step LLM pipelines | Can reduce unnecessary API calls through smarter workflows |
Note: Specific pricing figures for these services were not available in the source package at time of writing. Check each provider’s current pricing page directly.
The self-hosting inflection point is something the community debates constantly. The rough intuition is:
- Below ~1M tokens/day: Managed APIs are almost certainly cheaper when you factor in engineering time.
- Above ~10M tokens/day: Self-hosting on dedicated GPU infrastructure starts making serious economic sense.
- The messy middle: Between those numbers, it depends on your team’s ops capacity and risk tolerance.
RunPod and Lambda Labs represent the “move your own compute” path — you rent GPU instances, load an open-weight model (like Llama or Mistral variants), and pay a flat rate rather than per token. The tradeoff is that you’re now responsible for uptime, scaling, and model quality. That’s a real cost that doesn’t show up on an invoice.
n8n plays a different angle — it’s not about replacing the API call, it’s about being smarter with when you make them. By automating workflows intelligently, you can avoid redundant calls, implement better caching strategies, and route simple requests to cheaper models while reserving expensive ones for complex tasks.
The Bottom Line: Who Should Care?
Early-stage founders building their first LLM feature: Don’t over-optimize yet. Use the managed APIs, ship fast, and get real usage data. You can’t design a cost-efficient system until you know your actual usage patterns.
Bootstrapped SaaS builders at $5K-$50K MRR: This is where the pinch starts. If you haven’t already done a prompt audit — examining every token your system sends and receives — now is the time. Cutting system prompt bloat and implementing caching can often reduce costs 30-50% without touching anything user-facing.
Venture-backed products burning API budget to grow: Make sure your unit economics model includes LLM costs explicitly. “We’ll figure out the margin later” is a fine strategy for infrastructure costs that scale predictably — it’s a riskier bet with per-token billing where a single power user can cost 100x a casual user.
Teams considering self-hosting: Be honest about your ops capacity. RunPod and Lambda Labs are legitimate options, but they require someone to own that infrastructure. If you don’t have that person, the “savings” evaporate quickly in engineer-hours and incident response.
The meta-lesson from the community discussion is that LLM API costs are a product design problem as much as an infrastructure problem. Products that are designed with token efficiency in mind — tight prompts, smart caching, appropriate model selection for each task — can often run at 10x lower cost than equivalent products that weren’t built with this in mind from the start.
The builders who sleep well at scale are the ones who treated token costs like they treated database query optimization: not an afterthought, but a first-class architectural concern.