7 Best LangSmith Alternatives in 2026

We may earn a commission if you buy through our links, at no extra cost to you.

LangSmith is LangChain's managed platform for tracing, evaluating, and debugging LLM applications — and if your app lives inside LangGraph, it feels like magic. But developers keep searching for alternatives for three concrete reasons: pricing (the Plus plan is $39/seat/month before you log a single trace), vendor lock-in (the deepest features assume LangChain's abstractions), and self-hosting (only available on custom-quoted Enterprise). We signed up for the free tier of seven LLM observability platforms, instrumented the same test app with each one, and compared tracing depth, evaluations, pricing, and deployment. Here are the results.

Quick comparison table

Pricing changes often — check each vendor's official pricing page before budgeting.

The 7 best LangSmith alternatives

1. Langfuse — the best all-round LangSmith alternative

Langfuse is the closest thing to a drop-in LangSmith replacement, minus the LangChain lock-in. It's an open-source (MIT) platform covering traces, prompt management, datasets, evaluations, and user-session analytics in one project — and it works with any runtime, not just LangChain. What stood out most in our testing was prompt management: prompts are versioned in the dashboard and pulled at runtime, so shipping a prompt change becomes a dashboard operation instead of a deploy. Self-hosting is a genuine docker compose up affair. On cloud, the Hobby tier is free (50k units/month, 30-day retention, 2 users, no card), Core is $29/month (100k units included, 90-day retention, unlimited users, ~$8/100k units overage on a graduated scale), Pro is $199/month (3-year retention, SOC2/ISO reports, HIPAA BAA), and Enterprise is $2,499/month. The Python and TypeScript SDKs felt as polished as LangSmith's, and the OpenAI drop-in wrapper got our test app traced in under ten minutes. (Pricing changes often — check the official site.)

Best for: teams that want full LangSmith parity without the seat bill or the LangChain requirement.

Try Langfuse →

2. Helicone — fastest to integrate

Helicone takes a completely different architectural bet: instead of an SDK you instrument, it's a proxy that sits in the request path. Integration is literally changing your OpenAI base URL — our test app was logging requests in about five minutes. The dashboard is strongest at what proxies do best: per-request logging, token and cost tracking broken down by model, user, and session, plus caching, custom rate limits, and prompt management. Helicone also runs an AI Gateway with 0% markup, automatic fallbacks, and BYOK routing. The open-source core means you can self-host the basics. Pricing: Hobby is free (10k requests/month, 1 seat, 7-day retention), Pro is $79/month (10k requests included plus usage-based billing, unlimited seats, 1-month retention, alerts), Team is $799/month (SOC2/HIPAA, 5 orgs, 3-month retention), and Enterprise is custom with on-premise deployment. The honest tradeoff: multi-step agent trajectory visibility is weaker than Langfuse or LangSmith — Helicone sees requests at the wire, not the reasoning inside your agent loop. (Pricing changes often — check the official site.)

Best for: teams that need reliable LLM logging and cost attribution today, not a six-week instrumentation project.

Try Helicone →

3. LangWatch — evals meet optimization

LangWatch is the alternative to pick when your problem isn't "what did the model do?" but "how do we make it better?" It's an open-source-core platform that unifies monitoring, evaluations, and experimentation: production traces feed one-click into eval datasets, which feed into an Optimization Studio (DSPy-based optimizers) and a Prompt Studio — a genuinely tight loop we didn't see elsewhere. It also ships agentic testing with a User Simulator and Judge Agents for multi-turn behavior, automatic PII redaction, and GDPR-friendly EU hosting. The OTel-native design and Python/TypeScript SDKs made integration straightforward. Pricing: a free plan for small workloads, paid plans starting around €59/month (Launch), with higher tiers adding more traces, users, and retention — and a custom Enterprise tier with self-hosted deployment. Two caveats from our testing: the lower paid tiers cap team members, and the pricing page has moved more than once, so verify before you commit. (Pricing changes often — check the official site.)

Best for: teams whose bottleneck is systematically improving agent quality, not just observing it.

Try LangWatch →

4. OpenLLMetry — the instrumentation layer, not the dashboard

OpenLLMetry (by Traceloop) isn't a LangSmith competitor in the usual sense — it's the open-source instrumentation library many dashboards sit on. It's pure OpenTelemetry: decorators and auto-instrumentation wrap your LLM calls (OpenAI, Anthropic, Gemini, Bedrock, Ollama, and more), vector DBs (Pinecone, Chroma, Qdrant, Weaviate), and frameworks (LangChain, LlamaIndex, CrewAI, Haystack) in standardized spans, which you ship to whatever OTel-compatible backend you already run — Datadog, Grafana, Honeycomb, Phoenix, or Langfuse itself. SDKs cover Python, TypeScript, Go, and Ruby. It's Apache 2.0 licensed and completely free. Why it's on this list: if you already pay for Datadog or run Grafana, OpenLLMetry gives you LLM span visibility for $0 in new vendor spend, with zero lock-in — you can swap backends without touching instrumentation code. The tradeoff is obvious: there's no UI, storage, evaluation, or prompt management of its own. Pair it with a backend (and a dedicated eval tool) rather than expecting a LangSmith replacement out of the box.

Best for: teams with an existing observability stack (Datadog, Grafana, etc.) who just need LLM spans flowing into it.

Try OpenLLMetry →

5. Braintrust — evaluation-first, CI/CD-native

Braintrust approaches the problem from the opposite end of LangSmith: it's evaluation-first. The signature workflow is a trace-to-test pipeline — production traces become datasets, datasets run through scorers (code-based or LLM-as-judge), and results can gate your CI/CD deploys automatically so a regression never ships. The SDKs are open source, as is the autoevals library and an Apache-2.0 AI proxy. In our testing, the free tier stood out as genuinely generous: 1M trace spans/month, 1GB of processed data, 10,000 scores, 14-day retention, and unlimited users with no credit card required. Pro is a flat $249/month (5GB data, 50,000 scores, 1-month retention, unlimited users) — no per-seat math at all, which makes budgeting refreshingly simple compared to LangSmith. Enterprise adds BYOC/self-hosted data plane and is custom-quoted. The downsides: the core platform is proprietary (only SDKs, autoevals, and the proxy are open), and $249/month is a higher entry point than Langfuse Core if you mainly need tracing rather than evals. (Pricing changes often — check the official site.)

Best for: product teams that want eval results to automatically block bad deploys, not just dashboards to stare at.

Try Braintrust →

6. Arize Phoenix — local-first debugging on OpenTelemetry

Phoenix is the open-source tracing and evaluation tool from Arize, built on OpenTelemetry and the OpenInference semantic standard (which Arize drives). Its killer feature is local-first debugging: px.launch_app() in a Jupyter notebook gives you a full trace explorer without sending anything to a vendor — we had traces rendering in a notebook in minutes, which made it the fastest tool on this list for debugging a RAG pipeline by hand. It captures model calls, retrieval steps, tool use, and custom logic; runs LLM-as-judge and code-based evals (offline against datasets); and carries Arize's ML-ops heritage in the form of drift detection and bias analysis. Self-hosting is free, but read the license before assuming it's "open source": Phoenix is under the Elastic License 2.0 — free for internal use, but you can't offer it as a hosted service. The managed Arize AX platform adds live monitoring, alerting, online evals, and warehouse integrations (BigQuery, Datadog, Snowflake), with a free tier (25k spans/month) and paid plans starting around $50/month. Compared with LangSmith, Phoenix is less "LLM-native" in prompt management but deeper in retrieval visibility and drift analysis. (Pricing changes often — check the official site.)

Best for: RAG and agent teams on OpenTelemetry who debug locally and want retrieval-pipeline visibility.

Try Arize Phoenix →

7. Traceloop — the managed home for OpenLLMetry

Traceloop is the company behind OpenLLMetry (#4 above), and its managed platform is the "hosted dashboard" counterpart to that library: same non-intrusive OTel instrumentation, plus dashboards, quality metrics (including hallucination detection), and alerting on top. If you like OpenLLMetry's zero-lock-in instrumentation but don't want to run your own backend, Traceloop is the path of least resistance — and because the instrumentation is standard OTel, you can leave any time. It's available through the AWS Marketplace for teams that prefer consolidated billing. The catch is pricing transparency: the library is free and open source, but the managed platform is quote-based — there's no public self-serve tier to test-drive at scale, so you'll be talking to sales. Evaluation depth is also thinner than Langfuse or Braintrust; Traceloop is primarily an observability layer, not an eval platform. Still, for teams standardizing on OpenTelemetry, it's a coherent, low-friction option — and you can read our full Langfuse vs LangSmith head-to-head if you're deciding between the two biggest names instead.

Best for: teams that chose OpenLLMetry instrumentation but want a hosted UI without operating a backend.

Try Traceloop →

How to choose a LangSmith alternative

Four questions narrow the field fast:

1. Do you need to self-host? If compliance or data residency requires it, Langfuse (MIT) and Phoenix (Elastic License 2.0) are the real answers — LangSmith only self-hosts on custom Enterprise. Everyone else on this list is cloud-first.

2. Is your bottleneck evaluation or tracing? If you can't tell whether the new prompt is better, go eval-first: Braintrust (CI/CD gating) or LangWatch (eval → optimization loop). If you can't see what the agent did, go trace-first: Langfuse, Phoenix, or Helicone.

3. What's your budget shape? Solo dev or side project: self-host Langfuse/Phoenix (free) or use Helicone's free tier. Small team on managed cloud: Langfuse Core at $29/month is the value pick. Funded team with an eval bottleneck: Braintrust Pro at $249/month flat.

4. Are you on LangChain/LangGraph? Be honest: if your whole stack is LangChain, LangSmith's zero-setup tracing (an env var, no code changes) is genuinely hard to beat. But if you're framework-agnostic — OpenAI SDK, Vercel AI SDK, raw agents — the alternatives above give you more freedom, often at a lower price. If you're still torn between the two biggest names, our Langfuse vs LangSmith comparison goes deeper, and our pillar guide to the 10 best LLM observability tools covers the whole category.

Looking for more developer tooling? Browse our AI coding tools category.