If your startup is building with LLMs, you’ve probably felt this pain: usage grows, the product gets better, and the bill starts to look like a second payroll.
“LLM cost” isn’t just a per-token model price. It’s tokens in and out, tool calls, retries, fine-tuning jobs, embeddings, vector database reads and writes, evaluation runs, plus the cloud compute that keeps it all moving. The goal isn’t to make AI “cheap” at any cost, it’s to cut spend without wrecking answer quality or uptime.
One practical way to stay flexible is using an OpenAI-compatible gateway like LLMAPI, so switching models, tracking spend, and staying online during provider issues is simpler. And if you run LLM workloads on Azure, free startup credits through Spendbase can be a quick win while you tighten the rest.
Find the real cost drivers before you try to optimize
Most teams optimize the wrong thing first. They stare at a model’s price per million tokens, then miss the bigger leak: how their app uses tokens.
A $2 model can still burn money if it’s fed a 10,000-token prompt every time. A pricey model can be fine if it’s used rarely and only where it adds real value. The key is to treat LLM spend like any other product cost, measure it per workflow, then focus on the few calls that dominate your bill.
Here’s what usually hides in plain sight:
- Long system prompts repeated on every call, even when most rules don’t apply.
- Verbose outputs (and verbose “thinking out loud” formatting) that users don’t need.
- Re-sending the same context on every turn instead of summarizing it.
- Multi-step agents that call tools in parallel, sometimes redundantly.
- Retry storms when a provider gets flaky, doubling or tripling token spend.
A simple “measure first” mindset keeps you honest. Track practical metrics that connect directly to product and reliability:
tokens per request, cost per user, cost per feature, cache hit rate, retry rate, latency, and error rate. If you can’t tie spend to a feature, you can’t decide what’s worth it.
Do a one-week cost audit that maps tokens to product features
A one-week audit is enough to surface the big offenders, without turning cost work into a month-long project.
Start by tagging each LLM request with metadata you already have: endpoint, feature name, environment, and customer (or tenant). If you can, also tag “mode” (chat, extraction, summarization, agent-run) since they behave very differently.
At the end of the week, rank flows by total tokens and total cost. You’ll usually see a steep curve where a small number of endpoints drive most spend. A good rule of thumb is to optimize the top 10 percent of flows first, because that’s where you’ll actually feel the savings.
Also set budgets that match how startups work: per environment (dev, staging, prod) and per team. Dev spend can explode quietly when prompts change a lot and nobody watches it.
Use LLMAPI analytics to see cost, speed, and limits across models in one place
LLMAPI works like a universal adapter: one API key, one bill, and an OpenAI-style interface that makes model switching a one-line change for many apps. It also centralizes visibility, which matters more than it sounds.
When teams lack side-by-side data, they default to a “safe” expensive model for every call. With LLMAPI’s live model comparison (cost, speed, and context limits) and per model or provider reporting, you can see where you’re overspending and where a cheaper option will do the job.
That visibility has a simple effect: it reduces waste. People stop guessing, stop “just using the best model,” and start matching the model to the task.
Lower cost per request with smarter prompting, routing, and caching
Cutting LLM spend is mostly about three things: fewer tokens, fewer calls, and fewer “oops” retries. For startups, this is runway math. Every wasted request is a tiny tax on growth.
A quick mental model helps: treat each request like shipping a package. The heavier it is (tokens), the more it costs. The more times you ship it (retries, agent loops), the more it costs. If you keep shipping the same package to lots of people (repeated prompts), caching saves you.
A short before-and-after example makes it obvious. Before: you paste a full ticket thread, raw logs, and a long policy doc, then ask, “What should we do?” The model responds with a 600-word essay. After: you send a 6-bullet summary of the thread, only the log lines around the error, and ask for a 5-step action plan with strict length limits. Same outcome, far fewer tokens.
Shrink tokens without hurting answers (shorter context, tighter outputs, fewer retries)
Start with context discipline. Summarize chat history and carry forward only what matters. Drop irrelevant logs, remove duplicated instructions, and avoid sending large blobs “just in case.” If your app needs long policies, keep them stable in a system prompt, and don’t restate them in every user message.
Then control output. Set max output tokens, ask for short structured responses, and define a stop condition when you can. Many product features don’t need paragraphs. They need a label, a few fields, or a concise plan.
Retries deserve special attention because they can multiply costs fast. Use exponential backoff, sensible timeouts, and clearer error handling so one spike doesn’t become a wave of repeat calls. Also decide upfront when to fall back to a simpler response rather than retrying.
Route tasks to the cheapest model that still meets your quality bar with LLMAPI
Most startups don’t need one model. They need a small set with clear roles.
A practical tiering strategy looks like this: use a small, cheap model for classification, extraction, and simple formatting; a mid-tier model for drafting and summarizing; and reserve top-tier models for hard reasoning, tricky code, or high-stakes user flows.
LLMAPI supports this “best model per task” approach without forcing you to juggle many accounts and keys. It can also automatically choose the cheapest or fastest provider for a target model, which matters when the same model is hosted in multiple places at different prices and speeds.
Reliability also affects cost. If a provider goes down, LLMAPI can automatically fail over to another provider so your app stays online. That can prevent expensive incident behavior, like retry loops, emergency model swaps, or rushed rollbacks.
Stop paying twice with semantic caching and reusable tool results
If your product answers the same question many times, or near-duplicates of it, semantic caching is one of the easiest savings wins. Think FAQ answers, standard formatting tasks, “classify this support ticket,” or common onboarding questions. Instead of paying for a new generation each time, you reuse prior results when the new prompt is close enough.
Reusable tool results help too. If an agent calls the same internal search or database lookup repeatedly within a session, cache those tool outputs with a short TTL and stable keys.
Be careful about what you cache. Avoid storing personal data in caches, and don’t cache highly dynamic queries (like “today’s usage” or “current inventory”). LLMAPI semantic caching is designed to reduce duplicate spend across users and sessions while keeping performance tight.
Cut your infrastructure bill too, free Azure credits and a simple cost plan
Per-call savings are only half the story. Startups also pay for the stuff around the model: containers, GPUs, batch jobs, vector databases, and background workers that embed documents or run evals.
If you’re on Azure, Spendbase offers a practical shortcut: free Azure credits for startups. The figures depend on eligibility, but the program can include up to $5,000 for pre-seed to Series A startups, and up to $150,000 through the Microsoft for Startups network. They aim to get back to you within one business day, which matters when cash is tight.
Spendbase’s model is success-based for Azure savings: they take 25 percent of what they save you on Azure. There are no minimum commitments, and they ask for a 30-day termination notice before offboarding. The optimizations are applied on top of your existing setup, without major architecture changes or downtime, which is exactly what most small teams need.
How Spendbase helps you claim Azure credits and negotiate savings without refactoring
The process is straightforward: they check eligibility, help you choose the best application path, prepare the documentation package, and communicate directly with Microsoft so credits can be applied faster.
Founders can speed this up by gathering a few basics:
- Company info (legal name, funding stage, domain, key contacts)
- Recent cloud invoices and a rough monthly spend trend
- Architecture summary (what runs where, major services, any GPUs or batch pipelines)
If you’re multi-cloud, they can also help optimize AWS and Google Cloud, and they may help secure AWS promotional credits up to $100K depending on eligibility.
Conclusion
Cutting LLM costs comes down to three levers: measure what’s actually expensive, reduce tokens and calls (prompting, routing, caching), and lower cloud spend with credits and negotiated savings.
Set a monthly LLM budget, pick a default cheap model for routine work, and reserve top-tier models for the few places where they change outcomes. Using LLMAPI helps keep your app model-agnostic while improving cost tracking and reliability, including failover when a provider has issues.
Next steps: run the audit this week, add routing tiers, enable semantic caching, and apply for Azure credits if you qualify. Your runway will notice.
