Cut your LLM spend.
Without touching
your prompts.
Driftlock is a drop-in middleware layer that sits between your code and the LLM API. Exact caching, prompt trimming, and budget guardrails — no rewrites, no vendor lock-in.
The Problem
LLM costs are a black box.
01
No cost attribution
You can't see which endpoints, users, or features are driving your bill. You're reading a single monthly total — not a root cause.
02
Silent margin erosion
Token costs compound quietly. A verbose system prompt repeated across millions of requests is hundreds of dollars you never see coming.
03
No spending guardrails
Feature teams ship prompts without cost budgets. One unbounded loop or an oversized context window can blow your daily cap before noon.
How It Works
Three steps to full cost control.
01
Wrap your LLM client
One line of code. Driftlock intercepts your API calls before they reach OpenAI, Anthropic, or any other provider.
02
Configure rules
Set caching TTLs, budget caps per endpoint or team, sampling rates, and shadow mode in a single config block.
03
See savings immediately
Cost attribution, cache hit rates, and savings data surface from the first request. No warmup, no setup lag.
Features
Built for teams that care about margin.
Exact Response Caching
Hash-based cache for identical prompts. Repeated calls are served from memory in under 1ms — no API round-trip, no token spend.
Cost Attribution
Per-endpoint, per-user, per-model cost breakdown. Know exactly which feature is expensive before the invoice lands.
Budget Caps
Set hard or soft spending limits by team, endpoint, or day. Requests over budget are blocked or queued automatically.
Shadow Mode Rollouts
Run new prompts alongside production traffic without affecting users. Compare cost and quality before flipping the switch.
Sampling Controls
Route a configurable percentage of traffic to a cheaper model. Test cost-quality tradeoffs in production, safely.
ROI Analytics
Track cache hit rates, cost savings, and latency changes across every deployment. Export to your existing observability stack.
ROI
The math is straightforward.
“We cut our LLM bill by 22% in the first week. Not by rewriting prompts — by finally understanding what was driving the cost.”
- Exact cache hits on repeated or near-identical prompts
- Prompt compression removing redundant tokens
- Traffic routed to cost-efficient models via sampling
Integration
Five minutes to integrate.
Driftlock wraps your existing LLM client. Your codebase doesn't change. Your prompts don't change. Your API keys stay yours.
import driftlock
import openai
# Wrap your existing client — one line
client = driftlock.wrap(openai.OpenAI(), config={
"cache": {"ttl": 3600},
"budget": {"daily_cap_usd": 500},
"sampling": {"cheap_model_pct": 0.1},
"log": {"dsn": "https://ingest.driftlock.dev"},
})
# Zero changes to your existing API calls
response = client.chat.completions.create(
model="gpt-4o",
messages=[{"role": "user", "content": prompt}]
)
# First call: → OpenAI API (~800ms, $0.0030)
# Repeated call: → Cache hit (<1ms, $0.0000)No vendor lock-in
Works with OpenAI, Anthropic, Mistral, and any OpenAI-compatible endpoint. Swap providers without touching Driftlock.
Transparent logging
Every request, cache hit, and cost event logged as structured JSON. Pipe to Datadog, Grafana, or your own store.
Self-hostable
Run Driftlock entirely within your own infrastructure. Prompts never leave your network unless you route them to the LLM.
Early Access
Apply for the pilot.
We're onboarding a small cohort of teams spending $2k–$100k/month on LLM APIs. Early access includes white-glove integration support and locked-in pricing.
Prefer a call? Book 15 minutes →