Reduce OpenAI / Anthropic API Costs — Without Re-launching Your Site
LLM apps that ground answers, control cost, and pass evals
What you're seeing
Your AI feature works but the monthly API bill is climbing and your CFO wants a plan to cut it.
Our approach
We route to cheaper models when quality permits, add prompt caching, batch where it fits, and rewrite the worst token hogs. Bills typically drop 40–70% with no UX regression.
Symptoms teams come to us with
- The model confidently invents facts and fake citations
- You ship LLM changes by vibes, with no eval to catch regressions
- API bills are climbing and finance wants a plan
- RAG retrieves the wrong chunks or stale documents
What we get right
- 01Retrieval grounding and citation-required prompting
- 02A scored eval harness wired into CI before changes ship
- 03Model routing, caching and batching to cut token spend
- 04Chunking, embeddings and re-ranking tuned for recall
What you get
Concrete deliverables, no fluff
Every engagement ends with measurable, documented outcomes — no black-box agency reports.
Evaluation harness with scored test cases
Implementation behind feature flags + rollback plan
Cost & latency dashboard wired to your observability
Hand-off doc covering prompts, models, and guardrails
From brief to shipped fix
A transparent, low-risk process — a senior engineer reads your brief personally, and nothing starts until you approve a written plan and price.
Diagnose
A senior engineer reviews your brief, reproduces the issue, and pinpoints the real root cause — not the symptom — before any code is touched.
Scoped plan & quote
You get a written plan to reduce OpenAI / Anthropic API Costs, a firm timeline, and a fixed quote. Nothing starts until you approve it — no surprise invoices.
Ship the fix
We implement on a branch and open a pull request you review, working to your code-review standards on your repo — never a black box.
Verify & hand off
We verify on staging and production, share before/after evidence where it applies, and leave you a short hand-off note so the fix sticks.
Why teams hand this task to Krapton
Senior engineers only
Your brief is read and handled by a senior engineer — no junior hand-off, no sales-rep filter in between.
Root cause, not a patch
We reproduce and fix the underlying cause, then add a guard so the same class of issue does not quietly return.
Your repo, your standards
Every change lands as a pull request you review, on your repository, following your existing review process.
NDA on day one
Confidentiality and IP are covered before we look at a single line of code. All work stays in your accounts.
Fixed quote up front
You approve a written plan and price before work starts. If scope changes, we re-quote in writing — no surprise invoices.
Proof, where it applies
Performance, SEO and reliability work ships with before/after evidence so the result is measurable, not anecdotal.
Three ways to engage
No retainer required. Pick the model that matches the work — pricing for this task starts from $3,500, with a fixed quote before anything starts.
Per task
Most popularOne clearly-scoped fix at a fixed price. Best when you know exactly what is broken and want it handled end to end.
- Fixed quote up front
- One PR, reviewed by you
- No retainer required
Hourly
Pay only for the hours worked. Best for diagnostics, audits, or exploratory work where the scope is still emerging.
- Weekly timesheets
- Pay for what you use
- No minimum commitment
Per sprint
A focused 1–2 week sprint when the work is bigger than one fix but smaller than a full project.
- 1–2 week blocks
- Clear sprint goal
- Scale up or stop anytime
Tooling we use
Industry-standard stack, no proprietary lock-in
Reduce OpenAI / Anthropic API Costs — your questions, answered
How much does it cost to reduce OpenAI / Anthropic API Costs?
Pricing starts from $3,500 and depends on the scope we find during the diagnostic. You get a fixed, written quote before any work begins — most engagements like this run 2–6 weeks.
How long does it take to reduce OpenAI / Anthropic API Costs?
Typically 2–6 weeks for a focused engagement. After a short diagnostic we commit to a firm timeline so you know exactly what to expect.
Will you work directly on our existing codebase?
Yes. We work on your GitHub, GitLab or Bitbucket, ship every change as a pull request you review, and follow your code-review standards — not ours.
What exactly will I have at the end?
Concrete, documented outcomes — Evaluation harness with scored test cases, Implementation behind feature flags + rollback plan, Cost & latency dashboard wired to your observability, and more. No black-box agency report.
How quickly can you start, and do you sign an NDA?
For a focused task like this we can usually start within 24–48 hours of the brief. We sign an NDA on day one, before we look at any code — yours or ours.
How do you make sure a model or prompt change is actually better?
We build a scored evaluation harness — golden set plus LLM-as-judge and human spot-checks — and gate changes against it, so you ship improvements you can prove instead of regressions you can't see.
More in AI Dev
Related tasks we handle
Keep exploring
Related tasks and resources to plan your next step with Krapton.
Let's get this off your plate
Send a 60-second brief on Reduce OpenAI / Anthropic API Costs and a senior engineer replies within 24 hours with a plan and a fixed quote. NDA on day one, no retainer required.