Tracia vs Helicone: LLM Monitoring and Observability Compared

The biggest difference between Tracia and Helicone is architectural. Helicone's primary integration is a proxy: you change your API base URL and LLM traffic flows through Helicone's servers, where it's logged and analyzed. Helicone also offers async logging via OpenLLMetry that avoids putting the proxy in the critical path, but the proxy remains the most common setup. Tracia is an SDK: you call prompts.run() or runLocal() and traces are submitted separately from your LLM calls.

Tracing Your LLM Calls

The core use case: you have an LLM call and want to trace it. The architectural difference is clear here.

Helicone

import openai

client = openai.OpenAI(
    api_key="sk-...",
    base_url="https://oai.helicone.ai/v1",
    default_headers={
        "Helicone-Auth": "Bearer ...",
    },
)
response = client.chat.completions.create(
    model="gpt-4o",
    messages=[{"role": "user", "content": "Help me resolve this billing issue."}]
)

Change your base URL and add an auth header. Traffic flows through Helicone's proxy, where it's logged automatically. Helicone's newer Rust-based AI Gateway brings the proxy overhead down to ~1-5ms P95, a significant improvement over the original architecture.

Tracia

from tracia import Tracia

tracia = Tracia(api_key="tr_xxx")

response = await tracia.run_local(
    model="gpt-4o",
    messages=[{"role": "user", "content": "Help me resolve this billing issue."}]
)

One API key. Your call goes directly to the provider and the trace is submitted asynchronously in the background. No proxy hop.

Managed Prompt Execution

Both tools let you manage prompts in a dashboard and run them without hardcoding. The approaches reflect the architectural difference.

Helicone

With Helicone, you reference a prompt by ID in your AI Gateway call. The Gateway compiles the template and substitutes variables:

import openai

client = openai.OpenAI(
    api_key="sk-...",
    base_url="https://oai.helicone.ai/v1",
    default_headers={
        "Helicone-Auth": "Bearer ...",
        "Helicone-Prompt-Id": "customer-support",
    },
)
response = client.chat.completions.create(
    model="gpt-4o",
    messages=[{"role": "user", "content": "Help {{customer_name}} with: {{issue}}"}],
)

The prompt is versioned in the dashboard and deployed through the Gateway. You can manage production/staging environments and update prompts without code changes. Variables are substituted by the Gateway at request time.

Tracia

import { Tracia } from 'tracia';

const tracia = new Tracia({ apiKey: 'tr_xxx' });

const response = await tracia.prompts.run('customer-support', {
  customer_name: 'Sarah',
  issue: ticketDescription
});
// ✓ Prompt fetched, rendered, executed, and traced in one call

from tracia import Tracia

tracia = Tracia(api_key="tr_xxx")

response = await tracia.prompts.run("customer-support", {
  "customer_name": "Sarah",
  "issue": ticket_description
})
# ✓ Prompt fetched, rendered, executed, and traced in one call

Both tools let you update prompts without redeploying code. The key difference is architectural: Helicone routes through a proxy, Tracia handles it SDK-side.

Quick Overview

Latency Considerations

With Helicone's proxy:

Your App -> Helicone Gateway -> LLM Provider -> Helicone Gateway -> Your App

Helicone's Rust-based AI Gateway has brought this overhead down to ~1-5ms P95. You can also use their async logging integration to avoid the proxy entirely, though the proxy remains the primary integration path.

With Tracia's runLocal():

Your App -> LLM Provider (direct)
       |-> Tracia (async, non-blocking)

The LLM call path is unaffected. With prompts.run(), Tracia makes the provider call on your behalf, so latency depends on Tracia's infrastructure rather than a proxy hop.

Multi-Provider Support

Helicone supports 100+ providers through their unified AI Gateway, including OpenAI, Anthropic, Azure, Google, AWS Bedrock, Groq, and many more.

Tracia supports OpenAI, Anthropic, Google Gemini, and Amazon Bedrock through the same API. With runLocal(), it works across 100+ models.

Prompt Management

Both tools offer prompt management, though with different strengths.

Helicone provides prompt versioning with a playground, variable support, version history with rollback, and deployment via their AI Gateway. You can test prompt variations in the playground and deploy them as configuration changes without modifying application code.

Tracia provides:

• Full version history with diff viewing and rollback
• {{variable}} syntax for dynamic content
• Integrated playground for testing prompts with different models
• Test runs to batch-evaluate prompts against multiple scenarios
• A public prompt library with production-ready templates you can fork
• Evaluators for automated output assessment on prompt results

What Tracia adds beyond Helicone's prompt management is test runs for batch evaluation, a public template library, and automatic trace-to-prompt linking. When you call prompts.run(), every trace is automatically associated with the prompt version that produced it.

Evaluation

Helicone offers Evaluators and Scores, including LLM-as-judge scoring and custom evaluators for assessing output quality.

Tracia offers 11 built-in evaluator rules (contains, regex, JSON validation, length limits, etc.) plus LLM-as-judge evaluators and test runs for batch evaluation. Tracia's evaluators are rule-based and run on individual traces, with results appearing in the analytics dashboard alongside cost and latency data.

Helicone's evaluators focus on scoring production requests, while Tracia's evaluators and test runs focus on assessing output quality per trace and validating prompt changes before deployment.

Cost Tracking

Both tools are strong here. Cost tracking is core to both products.

Helicone provides detailed cost breakdowns per request, per model, and over time. Request caching helps reduce duplicate calls. Their cost analytics are well-implemented.

Tracia offers built-in pricing for 100+ models. Costs are calculated automatically and integrated into the analytics dashboard with breakdowns by prompt, model, and time period.

When to Choose Helicone

• You want the simplest possible setup (change one URL)
• Proxy-based monitoring is acceptable for your use case
• You want an open-source tool you can self-host
• Cost tracking and request caching are your main priorities
• You want features like rate limiting, key management, and request caching

When to Choose Tracia

• You don't want a proxy between you and your LLM provider
• You want prompt management with test runs and a template library
• You need both managed (prompts.run()) and local (runLocal()) execution
• You want rule-based evaluators that run on individual traces
• You want every trace automatically linked to its prompt version
• You want to update prompts without redeploying code

Bottom Line

Helicone and Tracia both provide solid LLM observability with strong cost tracking. The core difference is architectural: Helicone's gateway approach is simple to set up (change one URL) and their Rust-based AI Gateway has brought proxy overhead down to single-digit milliseconds. Tracia's SDK approach doesn't add a proxy hop at all and connects prompt management directly to tracing.

If you want quick, proxy-based monitoring with cost analytics, caching, and the option to self-host, Helicone is a strong choice. If you want prompt management and tracing unified without a proxy, with test runs and automatic trace-to-prompt linking, Tracia covers that ground.

Start free with 10,000 traces per month. No proxy in your call path, no added latency. Try Tracia free.