✦ WORKS WITH YOUR EXISTING CODE // 1-LINE SETUP

EASY 1-LINE CODE INTEGRATIONS

Zero code rewrites. Wrap your client in 1 line of code or set an automatic spending limit. Works smoothly with OpenAI, Anthropic Claude, LangChain, LiteLLM, FastAPI, and AI agent loops.

6 READY-MADE CONNECTORSADDS ZERO NOTICEABLE DELAY100% PRIVATE ON YOUR DEVICEPREVENTS RUNAWAY BILLS
[OFFICIAL INTEGRATION SPEC]·>= 1.30.0

LiteLLM Proxy & Gateway

Connect Talanton to LiteLLM with a single line of code. Every AI call routed through LiteLLM across 100+ different models is automatically measured without changing any of your existing code.

The Scale of Honest Measure: Weighing Prompts Against Real Dollars
✦ PLATE V // THE GATEWAY BALANCE

The Scale of Honest Measure: Weighing Prompts Against Real Dollars

IDEAS MEET VALUE — In LiteLLM, every inbound prompt is measured on the scale before routing across 100+ AI providers.

Execution LatencyAdds 0.00014s delay
Audit EnginePrivate Local Database
Network FootprintZero external network calls
Integration Effort1 Line of Code

Side-by-Side Implementation Comparison

Standard ImplementationBlind Spend (No Observability)
import litellm

# Standard LiteLLM call
response = litellm.completion(
    model="together_ai/deepseek-ai/DeepSeek-V3",
    messages=[{"role": "user", "content": "Optimize SQL query"}]
)
With Talanton Drop-InObserved + Guardrailed
import litellm
from talanton.integrations.litellm import TalantonLiteLLMLogger

# Register Talanton in 1 line: handles all providers
litellm.callbacks = [TalantonLiteLLMLogger(db_path="~/.talanton/gateway.db")]

# Inbound application calls remain completely untouched
response = litellm.completion(
    model="together_ai/deepseek-ai/DeepSeek-V3",
    messages=[{"role": "user", "content": "Optimize SQL query"}]
)
Structured WAL Event Written to ~/.talanton/talanton.dbATOMIC COMMIT · WAL ENABLED
{
  "event_id": "ev_01jk9x6k90d98f",
  "gateway": "litellm_proxy",
  "virtual_model": "together_ai/deepseek-ai/DeepSeek-V3",
  "input_tokens": 620,
  "output_tokens": 890,
  "total_cost_usd": 0.000679,
  "routing_strategy": "cost_optimized_lowest_latency",
  "status": "success",
  "wal_commit": true
}
✦ METROLOGY TOPOLOGY COMPARISON

SaaS Telemetry Proxies vs Sovereign Talanton

Most AI cost tracking vendors require you to proxy your prompts through their cloud servers, adding network hops and transmitting sensitive user prompts outside your VPC boundary. Talanton runs completely in-process.

Architecture DimensionCloud SaaS Gateways (Helicone, Portkey)Talanton Sovereign Metrology
Telemetry Latency+40ms to +120ms (Cloud proxy hop)< 0.10ms (In-process Rust / BPE)
Prompt Privacy & Data ExfiltrationPrompts transmitted to 3rd-party servers0 bytes leave machine · 100% Local
Offline & Air-Gapped SupportImpossible (Requires internet connectivity)Full offline support · Zero network calls
Storage ArchitectureProprietary hosted cloud databaseOpen SQLite WAL schema (~/.talanton)
Cost Per Million Traces$50 to $250 / month billed SaaS tier$0.00 forever (Open-Source MIT)
QUICK CLI AUDIT

Test Any Integration in 3 Seconds

Verify your local SQLite WAL database and live inference costs immediately from the terminal using the Talanton CLI:

talanton logs --tail 10