Hard spending limits that actually enforce. Intelligence that tells you exactly where tokens are wasted. Deploys entirely on your infrastructure.
Your data never leaves your infrastructure
Hard stops keep you safe. Intelligence makes you efficient. Both run on your infrastructure.
Per-org, per-feature, per-user, per-team. When the limit hits, traffic stops. Not a soft alert — a guarantee. Sub-10ms overhead.
Decompose your LLM bill by product feature, end-user account, and internal team. See the true cost per user for unit economics.
Identifies when your pipeline retrieves more context than useful. Specific k and score threshold recommendations with dollar savings.
Detects expensive models handling simple tasks. Routes to cheaper models without quality loss — automatically identified.
Agents accumulate conversation history that grows unbounded. We detect when to summarise and estimate the monthly savings.
Deploys on Kubernetes, Docker Compose, or bare VM. Terraform modules for AWS, GCP, and Azure. Air-gapped for regulated industries. Zero customer data ever leaves your environment.
Most cost tools count tokens and stop there. CostLine maps the structural relationships in your codebase — which files call which functions, which retrieval paths are actually used, which dependencies cause token bloat.
That structural understanding turns a generic alert into a specific recommendation: "reduce k from 8 to 4 in your checkout retrieval pipeline, saving $180/month."
Point your OpenAI or Anthropic SDK at CostLine. One line change. Your existing code works unchanged — no SDK migration, no prompt changes.
base_url="https://proxy.costline.dev/v1"
Add lightweight headers to identify features, customers, and teams. Optional — but unlocks per-feature budgets and attribution.
X-TW-Feature: checkout · X-TW-Customer: {id}
Create spend limits by org, feature, or customer in the dashboard. When a limit fires, requests are blocked immediately — not after the fact.
After 50+ requests, the analyser detects patterns — over-retrieval, model mismatch, history bloat — and generates quantified recommendations.
Not "you're using too many tokens." Specific, quantified, actionable.
Avg 8 chunks retrieved per request. Bottom score 0.38 — 4 chunks are noise.
→ Reduce k to 4, add score threshold at 0.6
68% of requests use claude-opus. Median output: 180 tokens — low complexity signal.
→ Route to claude-haiku for short-output tasks
Avg 9.2 turns in context at request time. History is 48% of total input tokens.
→ Summarise conversation at turn 5
Every warning includes estimated monthly savings at your current request volume.
Swap your base URL. Optionally add intelligence headers. Your existing SDK calls work unchanged.
from openai import OpenAI client = OpenAI( api_key="sk-your-key", # default: api.openai.com ) response = client.chat.completions.create( model="gpt-4o", messages=messages, )
from openai import OpenAI client = OpenAI( api_key="tw_live_your_key", base_url="https://proxy.costline.dev/v1", default_headers={ "X-TW-Feature": "checkout", "X-TW-Customer": customer_id, } ) response = client.chat.completions.create( model="gpt-4o", messages=messages, )
CostLine deploys on-prem or in your cloud via Helm, Docker Compose, or Terraform. Zero customer data ever leaves.
Deploy on your infrastructure. Pay once. Scale without surprise bills.
Small engineering teams
Growth-stage AI companies
Custom requirements
Early adopter pricing — locked in for your first year.
15-minute conversation. We'll show you what CostLine would find in your current LLM spend.