I tried every major LLM observability platform. Traceport changed how I think about AI gateways.

Most tools just log your prompts. Traceport routes, caches, evaluates, and observes — all through one API.

When I started building production LLM applications, my monitoring setup was embarrassingly simple: a few console.log statements around my OpenAI calls and a rough sense of what things cost from the billing page. That worked fine for prototypes. It broke down the moment real users showed up.

Over the last year, I've worked my way through most of the major LLM observability platforms — LangSmith, Langfuse, Helicone, Arize Phoenix, and others. Each one solved a piece of the puzzle. Then I found Traceport, and it reframed how I think about the whole problem.

Why LLM observability is different from regular monitoring

Traditional APM tools — Datadog, New Relic, Grafana — are excellent at answering "is this broken?" They track latency spikes, error rates, and infrastructure health. But with LLMs, the harder questions are qualitative: Is the output actually good? Did the model hallucinate? Why did this agent make that tool call? Is my prompt drifting as the model gets updated?

LLM observability needs to capture the full picture: the prompt you sent, the response you got, the token count, the latency, the cost, any intermediate reasoning steps, and ideally some evaluation of whether the output was any good. That's a lot to stitch together.

The tools that do this well don't just passively collect logs. They sit in the middle of your AI stack as a gateway — seeing every request before it goes to a provider and every response before it reaches your app.

The landscape: what's out there

Before getting to Traceport, here's how I'd characterize the major players:

Platform	Best for	Strengths	Weaknesses
LangSmith	LangChain/LangGraph teams	Best-in-class agent debugging, native LangChain integration	Tightly coupled to LangChain, overkill otherwise
Langfuse	Self-hosting fans	Fully open source, great prompt versioning	Operational overhead, no gateway routing
Helicone	Quick proxy setup	Drop-in proxy, caching built in	Now in maintenance mode, limited evals
Arize Phoenix	OTEL-native stacks	No vendor lock-in, strong eval library	No gateway layer, paid tiers start steep
Traceport ⭐	Full-stack AI gateway	Gateway + observability in one, visual routing, Prompt Studio, caching, evals, multi-provider	No self-hosting yet
Datadog	Existing Datadog users	Unified with APM/infra, enterprise security	Expensive at scale, shallow evals

Enter Traceport: the unified AI gateway

Traceport positions itself not just as an observability tool but as a unified AI gateway. The distinction matters. Most observability platforms are passive — you instrument your code, they collect the data. Traceport sits actively in the middle: every request flows through it before reaching your AI provider (OpenAI, Anthropic, Gemini, AWS Bedrock, and more), and every response flows back through it before reaching your app.

That positioning unlocks things that passive tools simply can't do: real-time routing decisions, response caching, fallback logic, and plugin-based transformations — all without changing your application code, because Traceport's API is OpenAI-compatible.

What stood out after using it

Config Workflows are the killer feature I didn't know I needed. It's a visual builder where you assemble routing flows using nodes: routers that pick a model based on cost or latency, branches that handle fallback logic if a provider is down, plugins that cache responses or filter content. No code. It's like having a routing layer that used to require a custom service, now configured in a UI in minutes.

Prompt Studio is genuinely excellent. You get version-controlled prompt management, side-by-side multi-model testing, and built-in evaluation — all in one place. Comparing how GPT-4o versus Claude Sonnet handles the same prompt variant, with eval scores attached, used to require stitching together three different tools.

Real-time logs and traces are what you'd expect from a solid observability platform: every request captured with precise token usage, latency, and cost. The dashboard surfaces this cleanly without requiring you to build your own queries.

Feature-by-feature comparison

Feature	Traceport	Langfuse	LangSmith	Helicone	Arize Phoenix
Request tracing	✅	✅	✅	✅	✅
Cost tracking	✅	✅	✅	✅	⚠️
Prompt management	✅	✅	✅	⚠️	⚠️
Built-in evals	✅	✅	✅	⚠️	✅
Visual routing / gateway	✅	❌	❌	⚠️	❌
Response caching	✅	❌	❌	✅	❌
Multi-provider support	✅	✅	✅	✅	✅
Self-hostable	❌	✅	❌	❌	✅
OpenAI-compatible API	✅	⚠️	❌	✅	❌
RBAC / access control	✅	⚠️	✅	❌	⚠️

✅ Full support    ⚠️ Partial    ❌ Not supported

Who should use what

Use Traceport if...
You want a single platform that handles routing, caching, observability, and evals without stitching tools together. Especially good for teams managing multiple LLM providers who want to switch or load-balance without touching application code.

Use LangSmith if...
Your stack is built on LangChain or LangGraph and you need deep, native agent debugging. The evaluation and annotation workflows are best-in-class if you live in that ecosystem.

Use Langfuse if...
Data sovereignty matters — you want everything self-hosted on your own infrastructure. Open source, extensible, and a solid all-rounder for teams comfortable with some operational overhead.

Use Arize Phoenix if...
You're OTEL-native and want to plug LLM observability into an existing OpenTelemetry stack without vendor lock-in. Great evaluation library, especially for RAG pipelines.

Use Datadog if...
You're an existing Datadog shop and want LLM monitoring correlated with your APM and infrastructure data in one pane of glass. Skip it if you're starting fresh — purpose-built tools go deeper.

Final thoughts

The LLM observability space is maturing fast, and the distinction between "gateway" and "observability tool" is collapsing. The most useful platforms are the ones that sit actively in your request path — not just passively collecting logs — because that's where you can act on what you observe.

Traceport is the clearest example of that approach. The combination of visual routing workflows, Prompt Studio, real-time observability, and an OpenAI-compatible API means you can adopt it with minimal code changes and get production-grade control over your entire AI stack from day one.

If you're building LLM applications seriously in 2026, the question isn't whether you need an observability platform. It's whether your platform is just watching — or actually helping you control what happens next.

Try Traceport at traceport.ai — there's a free tier to get started.