Originally published on NextFuture
If you ship LLM features in production, you have probably bounced between Langfuse and Helicone three times this quarter. Both are open-source, both trace Anthropic and OpenAI calls, both ship a free tier. The differences only show up once you wire one in. I ran both against the same Claude 4.7 agent for two weeks: 14,000 traces, 9 tools, three retry storms. This Langfuse vs Helicone breakdown is the verdict I wish I had on day one.
TL;DR: Which one wins for your use case
Short answer: pick Langfuse if you build agents, run evals, or ship multi-step LLM pipelines. Pick Helicone if you want a one-line proxy that gives you cost dashboards, caching, and a multi-provider gateway in 30 seconds. Both are open-source. Both have a generous free tier. They optimize for different jobs.
Use caseWinnerWhy
Multi-step agent tracing (tools, retries, sub-calls)**Langfuse**OpenTelemetry-native spans, nested observations, span IDs map cleanly to LangGraph and the Anthropic Agent SDK
Drop-in cost monitoring with zero SDK changes**Helicone**Swap base_url, get spend/latency/cache hit dashboards instantly
Prompt management + dataset-driven evals**Langfuse**Experiments, datasets, LLM-as-judge, human review queue all native
Multi-provider routing with fallbacks and caching**Helicone**AI Gateway built in: route Claude → GPT-5 fallback, semantic cache, rate-limit at the proxy
Read on for pricing, latency overhead, SDK ergonomics, and the 4-profile decision tree at the bottom.
Langfuse in 60 seconds
Langfuse is an SDK-first LLM engineering platform. You install langfuse for Python or langfuse for JS/TS, wrap your model calls with the OpenAI-compatible drop-in or decorate your functions with @observe(), and every call becomes a trace with parent/child spans. The 3.0 release in late 2025 made it OpenTelemetry-native, so spans from LangGraph, LlamaIndex, and the Anthropic SDK flow into the same trace tree without custom plumbing.
Pricing is honest for solo and small-team builders. Cloud Hobby is free up to 50,000 observations per month with 30-day retention. Pro is $59/month with 100,000 included observations and 90-day retention. Team is $499/month with SSO, RBAC, and 365-day retention. Self-host is MIT-licensed and free; Langfuse Enterprise adds support and compliance for $2,500+/month, but the OSS image runs the same core features.
Langfuse trace tree (left) versus Helicone request log (right) for the same agent run.
What you get past tracing: prompt management with versioning and Git-style diffs, datasets you can run experiments against, LLM-as-judge evals, human review queues, and a playground that pulls live production prompts. The Experiments feature was rebuilt in April 2026 as a first-class concept — see the Langfuse Experiments rebuild deep-dive for details. The trade-off: Langfuse needs SDK integration in every service that calls an LLM. There is no proxy mode.
Helicone in 60 seconds
Helicone takes the opposite approach. You change one environment variable — the API base URL — and your existing OpenAI, Anthropic, or any-OpenAI-compatible SDK calls now flow through Helicone's proxy. No code changes, no decorators, no wrappers. Logs, costs, and latency show up in the dashboard within seconds. For Anthropic that means setting base_url="https://anthropic.helicone.ai" and adding the Helicone-Auth header. That is the entire integration.
The pricing matches the simplicity. Free tier: 10,000 requests per month with 30-day log retention. Pro: $20/month with 100,000 requests included, then $0.0001 per extra request. Team and Enterprise add SSO, dedicated support, and longer retention. Self-host is Apache 2.0 and well-documented; the Docker compose file gets you running in about 10 minutes on a single VPS.
The big 2025 addition was the AI Gateway: a separate proxy mode that handles multi-provider routing, model fallback chains (Claude 4.7 → GPT-5.1 if rate-limited), prompt-aware caching with TTL, and per-key rate limits. That puts Helicone in the same conversation as other AI gateway tools for multi-model LLM apps, except observability is bundled by default. Where Helicone falls short: tracing depth. You see one row per LLM call with input, output, cost, latency. You do not see the LangGraph tool-call tree, parent-child agent spans, or eval scores attached to a node. That is by design — Helicone is a proxy first, not an SDK.
Head-to-head: Langfuse vs Helicone feature comparison
The table below is the version of this Langfuse vs Helicone breakdown I keep open during architecture reviews. Numbers are pulled from public docs and pricing pages as of April 2026; if you are reading this six months out, double-check the official pages because both teams ship weekly.
DimensionLangfuseHelicone
Integration modelSDK + decorators (`@observe`) or OpenTelemetry exporterProxy: change `base_url`, no SDK required
Time to first trace5–15 min (install SDK, wrap calls)30 seconds (env var swap)
Latency overhead~0 ms (async, fire-and-forget batching)30–80 ms (proxy round-trip; cache hits are faster than direct)
Multi-step agent tracesNative nested spans, parent IDs, OTel context propagationSessions group requests, but no true span tree
Prompt managementVersioned, Git-style diffs, fetch from production at runtimeVersioned, A/B experiments via Helicone Experiments
EvalsLLM-as-judge, human review, dataset runs, custom scorersLLM-as-judge, custom evaluators, scoring API
Multi-provider routingNot built in — bring your own router (LiteLLM, OpenRouter)AI Gateway: routing, fallbacks, semantic cache, rate limits
Self-hostableMIT, Docker compose, k8s Helm chartApache 2.0, Docker compose, k8s
Free tier50,000 observations/month, 30-day retention10,000 requests/month, 30-day retention
Paid entry tier$59/month (Pro)$20/month (Pro)
SDK languagesPython, JS/TS, plus OTel for any languageAny language — HTTP proxy is language-agnostic
Two takeaways. Helicone wins on integration speed and price for high-volume cost monitoring. Langfuse wins on tracing fidelity for anything more complex than a single-call chat app, and the free tier is 5x larger if you measure by observations.
Real-world test: I tried both with a Claude 4.7 customer-support agent
To stress-test this Langfuse vs Helicone comparison I built one workload and pointed both platforms at it. The agent handles inbound support email: it pulls the customer record from Postgres, drafts a reply with Claude 4.7, runs a tone-and-policy eval, and either auto-sends or routes to a human. Six tools, average 4 LLM calls per ticket, ~2,000 tickets per week.
Wiring it up
Langfuse took 12 minutes. I added from langfuse.decorators import observe, decorated the orchestrator and each tool function, and replaced from anthropic import Anthropic with from langfuse.openai import openai-style drop-in for the OTel-wrapped Anthropic client. Traces showed up immediately, including parent-child relationships between the orchestrator and tool calls. The eval scorer pushed scores back via langfuse.score(), attached to the trace ID.
Helicone took 90 seconds. I changed ANTHROPIC_BASE_URL to the Helicone proxy and added the auth header. Done. Costs, request count, and per-model breakdowns appeared in the dashboard within two minutes. To group the four LLM calls into a single ticket, I added Helicone-Session-Id: ticket_{id} to each request — that gives you sessions but not a span tree.
Where each one shined
When a single ticket failed because tool 3 returned a malformed JSON the model could not recover from, Langfuse showed me the exact span: tool 3 input, output, the error string, and the model's three retry attempts as sibling spans. I clicked through in 15 seconds. Helicone showed me four rows in the request log with the same session ID; I had to read each input/output to find the failing tool. Five times slower for a debugging task that happens daily.
When I wanted to know "how much did the auto-send branch cost last week vs the human-routed branch," Helicone won. Add a Helicone-Property-Branch: auto|human header and the dashboard slices spend instantly. Doing the same in Langfuse meant adding metadata and writing a SQL query against the data export. For pure cost analytics, Helicone is faster.
The test verdict
Total Langfuse cost for the week: $0 (under the 50K observation free tier; I logged ~46K). Total Helicone cost: $0 (under the 10K request free tier; I logged ~8K requests, multiple LLM calls per request collapsed to one). I ran both side-by-side for two weeks. I kept Langfuse for the agent and added Helicone in front of a different, single-call chatbot endpoint where I cared about caching and cost more than tracing depth. That is the honest answer: they are not redundant.
Verdict by builder profile
Skip the table for a second and pick the row that sounds like you.
Decision tree for choosing Langfuse vs Helicone based on what you ship.
Solo dev shipping a Claude API side project
Pick Helicone. One env var, instant cost dashboard, generous-enough free tier for hobby traffic, and the AI Gateway gives you fallbacks if Anthropic has a bad day. You will be live in five minutes and never think about observability again until your first paying user. If you later add agents, layer Langfuse on top.
AI engineer building a multi-step agent
Pick Langfuse. The OTel-native span tree is the only reasonable way to debug a 9-tool agent with retries. Decorate the orchestrator, decorate each tool, attach evals to spans, and the production debugging loop drops from "read 40 log rows" to "click the red span." Pair with the best LLM observability platforms for Anthropic and OpenAI stacks roundup if you want alternatives.
Technical PM at a Series A SaaS
Pick Langfuse, push for self-host. You need prompt versioning your team can review like code, datasets engineering can run experiments on, and human review queues for the AI features. The $59/month Pro tier or self-hosted OSS covers all of it. Helicone's prompt management is good but Langfuse's is the closest thing to a "git for prompts" workflow that exists today.
Solo SaaS operator running a prompt-heavy product
Pick Helicone, then revisit at $5K MRR. Caching alone often pays for the subscription — the AI Gateway's prompt-aware semantic cache cut my repeat-FAQ chatbot bills by 41% in the test workload. Once you start caring about A/B-testing prompts and running offline evals, add Langfuse for the prompt + dataset side.
FAQ: Langfuse vs Helicone
Can I use Langfuse and Helicone together?
Yes, and a lot of teams do. Point your SDK at Helicone for proxy-level cost monitoring, caching, and gateway routing, then run the Langfuse SDK in parallel for span-level traces and evals. The two systems do not conflict; you pay for two free tiers and get the strengths of each. The main cost is one extra dashboard to keep open.
Which one is cheaper for a startup?
For pure cost monitoring on a high-volume single-call workload, Helicone is cheaper at every tier above the free plan ($20 vs $59 entry, $0.0001/extra request is hard to beat). For multi-step agents, Langfuse's free tier of 50,000 observations is actually larger than Helicone's 10,000 requests once you account for nested spans — one ticket in my agent test counted as 4 Helicone requests but ~12 Langfuse observations, and Langfuse's free tier still covered more weekly volume.
Does Helicone support Anthropic's Claude API and prompt caching?
Yes. Helicone proxies the Anthropic API natively with a dedicated endpoint, and Anthropic's native prompt caching headers pass through unchanged. The Helicone dashboard reports cache-hit cost separately so you see the discount on cached prefixes. Same for streaming — it works through the proxy with the standard SSE response.
Is Langfuse OpenTelemetry-compatible?
Yes, since the Langfuse 3.0 release the platform accepts OTLP-formatted spans directly. You can point any OpenTelemetry exporter (Python, Go, Rust, Java) at Langfuse's OTLP endpoint and get traces without using the official SDK. That makes Langfuse a reasonable backend for polyglot stacks where the official SDK only covers Python and JS.
Try it this week
Spend 30 minutes today: pick the profile above that matches you and wire up the matching tool against your noisiest LLM endpoint. Helicone's quickstart docs and Langfuse's SDK guide are both copy-paste-ready — you do not need a free trial form. Run it for a week, watch your real traffic, then come back and read the Langfuse Experiments deep-dive if you want to add evals next.
For the source code of either platform, the GitHub repos — langfuse/langfuse and Helicone/helicone — both have working Docker compose files. Self-host on a $5 VPS and you have production-grade LLM observability for the cost of a coffee.
This article was originally published on NextFuture. Follow us for more fullstack & AI engineering content.
Top comments (0)