HiWay2LLM vs LangSmith

People search "LangSmith alternative" and land on HiWay2LLM. Then they read our homepage and realize we do something different. This page clears it up.

LangSmith and HiWay solve problems at two different layers of an LLM stack. LangSmith is observability — it watches what your LangChain app does, records the traces, runs evals on datasets, and helps you debug why an agent took the branch it took. HiWay is a router — it sits in front of the model providers, receives your API calls, and picks the cheapest model that can handle each one.

You can use both at once. Most serious LangChain teams do, and the configuration is three lines.

Quick decision

• Debugging a LangChain agent or chain? LangSmith is the right tool. Nothing else traces LangChain internals with the same fidelity.
• Watching your inference bill grow every month? HiWay is the right tool. Smart routing and BYOK cut the bill directly.
• Running LangChain in production at scale? Use both. HiWay as the provider behind ChatOpenAI or ChatAnthropic, LangSmith for tracing the chains around it.
• Not on LangChain? LangSmith's value drops sharply. It was built for the LangChain abstraction. HiWay works with any OpenAI-compatible client.

Pricing

LangSmith charges per trace and per eval run. The Developer tier is free up to 5K traces per month; Plus starts at $39 per seat per month with higher trace volumes; Enterprise is sales-led. If you run a production LangChain app, you'll land in Plus or Enterprise quickly — a single real-world agent conversation can easily hit 20–50 traces.

HiWay charges a flat fee for the routing layer. Inference is paid directly to the provider you bring keys for, at published wholesale rates — 0% markup from us:

Smart routing also auto-downgrades simple requests to cheaper models — typically 40-85% savings on a normal usage mix — which overtakes the $15/mo Build subscription within hours of real use, at any scale.

These are not competing line items. If you use both, you pay LangSmith for visibility into your chains and HiWay for the inference itself. If you drop LangSmith because you're no longer on LangChain, you keep HiWay. If you drop HiWay because you only use one model from one provider, you keep LangSmith.

Feature-by-feature

When to pick which

Migration — how to run HiWay behind LangChain while keeping LangSmith for tracing

This is the configuration most teams actually want: keep LangSmith tracing intact, swap the underlying LLM client to point at HiWay. Three lines change.

import os
from langchain_openai import ChatOpenAI

# LangSmith tracing stays as-is
os.environ["LANGSMITH_TRACING"] = "true"
os.environ["LANGSMITH_API_KEY"] = "lsv2_..."

# Calls go direct to OpenAI
llm = ChatOpenAI(
  model="gpt-4o",
  api_key=os.environ["OPENAI_API_KEY"],
)

response = llm.invoke("Hello")

import os
from langchain_openai import ChatOpenAI

# LangSmith tracing stays as-is
os.environ["LANGSMITH_TRACING"] = "true"
os.environ["LANGSMITH_API_KEY"] = "lsv2_..."

# Calls go through HiWay — smart routing + multi-provider fallback
llm = ChatOpenAI(
  model="auto",
  base_url="https://app.hiway2llm.com/v1",
  api_key=os.environ["HIWAY_API_KEY"],
)

response = llm.invoke("Hello")

That's it. Your LangChain code doesn't know HiWay is there. LangSmith still sees every trace. The difference is that the request now hits our router first, which picks Haiku / Sonnet / Opus / GPT-5-mini / Gemini Flash based on complexity — and falls back to a second provider if the first one craters.

LangSmith's observability stack — and what HiWay covers instead

LangSmith's core loop is: trace every run of your chain, record inputs/outputs/latency/tokens, build datasets from real runs, run evals against those datasets, compare prompt versions. It's a full observability + experimentation surface tailored to LangChain's abstractions. If you're debugging why an agent took the wrong tool call five hops deep, LangSmith is where you'll find the answer.

HiWay sits below that. It doesn't trace chains or agents. It traces requests. Per-request you get: which model was picked, why (routing decision), token counts, provider latency, cache hit, cost in dollars. You also get per-workspace analytics, burn-rate alerts, and an audit log. This is enough to answer "where is the money going" — not enough to answer "why did my agent loop."

The honest way to read this: if your pain is agent logic correctness, LangSmith. If your pain is inference cost and reliability, HiWay. If it's both, run both.

Data & compliance

LangSmith is operated by LangChain, Inc., a US company. Hosted LangSmith is US-hosted with GDPR terms on the Enterprise tier; a self-hosted LangSmith is available for regulated industries that need to keep traces inside their own infrastructure. Prompts and completions are stored as part of traces by default — that's the product.

HiWay is operated from France by Mytm-Group, hosted on OVH servers in the EU. Zero prompt logging is the default — prompts transit in memory and are never persisted. We sign a DPA on request (even on the free plan). If you want request-level traces with prompts, you opt in; by default you get metadata only (tokens, latency, model, cost).

If you use both, think about where your prompts live. LangSmith stores traces (by design). HiWay doesn't. For regulated workloads, the common pattern is: self-host LangSmith inside your VPC, route LLM calls through HiWay.

FAQ

Bottom line

LangSmith and HiWay aren't competitors. They're complementary layers of an LLM stack — one for observing what your application does, one for routing what hits the model providers. If you've ever felt like LangSmith doesn't help with your inference bill, or that you need a proper gateway in front of your chains, this is why.

Run both: LangSmith for chain tracing and evals, HiWay as the provider behind ChatOpenAI / ChatAnthropic. You keep everything you already love about LangSmith, you add smart routing, multi-provider fallback, and an EU-hosted gateway — without rewriting anything above the model client.