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Why Choose Traceport

Building AI applications today means juggling multiple provider SDKs, managing API keys across services, and stitching together observability from scratch. Traceport solves this by providing a single, unified layer for your entire AI stack.

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The Problem

Switching between OpenAI, Anthropic, and Google requires rewriting integration code, managing separate API keys, and adapting to different response formats.
Most teams have little insight into per-request costs, token usage, or latency distribution across models and providers.
When a provider goes down, your application goes down. Building failover logic from scratch is complex and error‑prone.
System prompts hardcoded in application code become unmanageable — no version control, no rollbacks, no ability to update without redeploying.

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The Traceport Solution

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Unified API

Access all AI models through a single, consistent endpoint. No more provider‑specific SDKs. 
from openai import OpenAI

# Just change the base URL — everything else stays the same
client = OpenAI(
    api_key="<TRACEPORT_API_KEY>",
    base_url="https://api.traceport.ai/v1"
)

response = client.chat.completions.create(
    model="gpt-4o",
    messages=[{"role": "user", "content": "Hello!"}]
)

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Cost Optimization

Traceport tracks every token and every dollar. Use the dashboard to identify expensive models and optimize routing to reduce costs without sacrificing quality.

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Reliability

Built‑in failover and retry mechanisms ensure high availability. Config Workflows let you define automatic fallbacks — if OpenAI is down, route to Anthropic seamlessly.

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Security

Enterprise‑grade key management with encryption at rest. Your provider keys are stored securely and never exposed to your application code.

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Observability

Deep insights into every request and response with real‑time analytics. Track latency, token usage, costs, and error rates across all providers from a single dashboard.

Get Started →

Integrate Traceport in under 5 minutes.