Introducing Hanzo Console: Unified LLM Observability
Hanzo Console launches — a unified development environment for observing, debugging, and optimizing LLM applications across all providers.
Building LLM applications at scale means flying blind. You're sending requests to models, getting responses, and hoping things work. When they don't — when outputs are wrong, latency spikes, costs explode — you have no visibility into why.
Today we're launching Hanzo Console: a unified observability platform for LLM applications.
What Console Does
Console gives you a complete picture of your LLM usage across all providers:
Tracing: Every LLM call logged with full request/response, latency, token counts, and cost. View exact prompts, exact completions, token-level timing.
Cost tracking: Real-time cost by model, project, user, and API key. See where your budget goes and optimize accordingly.
Quality monitoring: Track output quality over time. Catch regressions before they reach users. A/B test prompts with statistical significance.
Multi-provider: One dashboard for OpenAI, Anthropic, Together AI, and every other provider you use. Stop piecing together reports from five different billing portals.
The Integration Tax
Every LLM provider offers some form of usage dashboard. But if you're using multiple providers — which most production applications do — you're reconciling reports from five different systems with five different schemas.
Console is provider-agnostic. Instrument once, see everything.
from hanzo import Console
console = Console(api_key="your-key")
# Automatic tracing with context manager
with console.trace("user_query"):
response = openai.chat.completions.create(
model="gpt-4o",
messages=[{"role": "user", "content": user_input}]
)Prompt Management
Console includes a prompt registry. Version your prompts, track which versions are deployed, compare performance across versions.
The problem with prompts in code: they get stale, they get forked, nobody knows which version is running in production. Console makes prompts first-class managed artifacts.
Dataset Curation
Turn production traces into evaluation datasets. Mark good outputs, flag bad ones, build a ground truth dataset directly from real usage.
When you're ready to fine-tune or change providers, you have a dataset that reflects actual production traffic — not synthetic examples.
Available Now
Console is available at console.hanzo.ai. Free tier includes 50K traces/month. Self-hosted deployment supported for enterprise.
Zach Kelling is the founder of Hanzo AI (Techstars '17).
Read more
Introducing Enso — one API, routed to the best frontier intelligence
Enso is Hanzo's learned router: no single frontier model leads every benchmark, so Enso routes each request to the model most likely to win it — and learns from your feedback as it goes. Microsecond routing on a CPU, a transparent per-request meter, and a 1% fee that pays for itself. Here is how it works, the measured results, and the cost-transparency angle a monolith cannot offer.
Hanzo Observability: Metrics, Logs, and Traces in One Platform
Launching the Hanzo Observability stack — unified metrics, logs, and distributed tracing with OpenTelemetry-native ingestion.
Route every prompt to the right model — and cut AI cloud costs by up to 90%
Hanzo Router places each request on the cheapest model that can still serve it — reuse a local model for free, load one that fits, or fall through to cloud. Here's the idea, what it feels like in production (qualitative), and the honest cost arithmetic (quantitative) — no fabricated benchmark.