In this blog post 3 Mistakes That Quietly Inflate Your AI Budget and How to Fix Them we will look at the most common (and fixable) reasons AI costs climb faster than expected. If you’re deploying LLM features in products, internal tools, or customer support, these mistakes can turn “promising pilot” into “why is the bill so high?”
High-level: most AI costs are driven by how many tokens you send and receive, how often you make requests, and which model you choose. When systems lack caching, allow context to grow without limits, or default to an overly capable model, usage scales linearly—or worse—while business value doesn’t. The good news is these are architecture and engineering decisions you can correct without sacrificing user experience.
The core technology behind AI budget blowouts
Modern AI apps typically use a large language model (LLM) behind an API. Every request is measured in tokens, roughly pieces of words. You pay for tokens in:
- Input tokens: the prompt, system instructions, retrieved documents, conversation history, tool results.
- Output tokens: the model’s response.
Three technical patterns determine spend:
- Request frequency (how many calls you make)
- Prompt size (how many tokens per call)
- Model selection (cost per token and latency)
The mistakes below each amplify one of these levers.
Mistake 1: No caching for repeat questions and repeat work
Many teams treat every LLM call as unique. In reality, AI apps often see repeats: common support questions, standard policy explanations, repeated document summarisation, and “regenerate” clicks. Without caching, you pay again for the same tokens—plus you add latency.
What caching looks like in AI systems
- Response caching: cache the final answer for identical (or near-identical) inputs.
- Embedding and retrieval caching: cache expensive upstream steps (document embeddings, search results, retrieved chunks).
- Tool-call caching: if the model calls tools (DB queries, APIs), cache those results too.
Practical steps to implement caching
- Start with deterministic inputs: set model temperature low for cached routes (e.g., 0–0.2) to reduce variation.
- Hash a canonical prompt: normalise whitespace, remove volatile fields, then hash to form a cache key.
- Use TTLs and versioning: include “prompt version” and “knowledge version” in the key so changes invalidate safely.
- Cache at multiple layers: retrieval results (seconds/minutes) and final answers (minutes/hours/days) depending on risk.
When not to cache
- Highly personalised outputs (unless you cache per user/segment and scrub sensitive data)
- Rapidly changing data (unless TTL is short and tool outputs are versioned)
- Compliance-sensitive prompts (ensure logs/caches follow your data policies)
Budget impact: caching reduces request frequency and repeat tokens. It’s often the fastest cost win because you’re cutting waste, not quality.
Mistake 2: Unbound context that grows forever
“Just send the whole conversation” feels safe—until you realise your input tokens are increasing every turn. A chat that starts cheap can become very expensive by message 20, especially when you include tool traces, full documents, or verbose system instructions each time.
Why unbound context gets expensive
- You pay for repeated tokens every call (system prompt + history + retrieved docs).
- Long prompts can slow responses, increasing user retries and compounding spend.
- Extra context can reduce quality by burying the key instructions in noise.
Practical ways to bound context without breaking UX
- Use a token budget per request
- Example policy: max 6,000 input tokens; reserve 1,000 for the answer.
- When you hit the limit, shrink history and retrieval, not your core instructions.
- Summarise and roll up conversation state
- Keep a short “memory” summary (facts, decisions, constraints).
- Keep only the last N turns verbatim (e.g., last 4–8 messages).
- Retrieve the right context instead of sending all context
- In RAG (retrieval augmented generation), fetch only the most relevant chunks.
- Limit chunks (e.g., top 3–6), cap chunk size, and deduplicate overlaps.
- Strip what users don’t need
- Remove tool logs, JSON blobs, stack traces, and raw HTML unless required.
- Store them server-side and provide IDs if the model must reference them.
Mistake 3: Using the wrong AI model for the job
It’s tempting to standardise on your most capable model “so it always works.” But model choice should be a product decision: different tasks have different accuracy needs, latency targets, and cost constraints. Overusing a premium model for routine tasks is like running every workload on the biggest cloud instance.
Common model selection mismatches
- Simple classification (routing, tagging, sentiment) done with a large generative model
- Extraction (fields from emails/invoices) done with a model tuned for creative writing
- High-volume internal assistants using top-tier models even for “draft a short reply”
A practical approach: model routing
Use a small/medium model by default and escalate only when needed.
- Define task tiers
- Tier 1: fast + cheap (summaries, rewriting, basic Q&A)
- Tier 2: balanced (most business reasoning, standard support responses)
- Tier 3: premium (complex analysis, multi-step planning, high-risk outputs)
- Add a simple “complexity check”
- Heuristic: message length, number of requirements, presence of code, ambiguity.
- Or a lightweight classifier model that decides which tier to use.
- Escalate on low confidence
- If the model returns uncertainty, missing citations, or fails validation, retry on a stronger model.
Budget impact: right-sizing models reduces cost per token and often improves latency. The key is to reserve premium models for genuinely premium needs.
A simple cost-control checklist you can apply this week
- Measure: log tokens in/out, request count, and average prompt size per endpoint.
- Cache: start with top 20 repeated questions or repeated document operations.
- Bound context: set token budgets, summarise memory, limit retrieval chunks.
- Route models: default to smaller models; escalate only on complexity or risk.
- Validate outputs: schemas/JSON validation reduces retries and expensive “fix the answer” loops.
Final thoughts
AI budgets rarely blow out because the technology is inherently uncontrollable. They blow out when systems are built without the same disciplines we apply to cloud cost management: caching, right-sizing, and limits. Fix those three mistakes—no caching, unbound context, and the wrong model—and you’ll usually see an immediate reduction in spend, along with faster responses and a more reliable user experience.
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