Top LangSmith Alternatives and Competitors, Compared

1. Confident AI

What is Confident AI?

Confident AI is a platform that combines LLM evals, A|B testing, metrics, tracing, dataset management, and prompt versioning to test AI apps in one collaborative platform.

It is built for engineering, product, and QA teams, and is native to DeepEval, a popular open-source LLM evaluation framework.

Key features

• 🧪 LLM evals, including sharable testing reports, A|B regression testing, prompts and model performance insights, and custom dashboards.

• 🧮 LLM metrics, with support for 30+ single-turn evals, 10+ multi-turn evals, multi-modal, LLM-as-a-judge, and custom metrics such as G-Eval. Metrics are 100% open-source and by DeepEval.

• 🌐 LLM tracing, with integrations with OpenTelemetry, and 10+ integrations with OpenAI, LangChain, Pydantic AI, etc. Traces can be

  evaluated via online + offline evals in development and production.

• 🗂️ Dataset management, including support for multi-turn datasets, annotation assignment, versioning, and backups.

• 📌 Prompt versioning, which supports single-text and messages prompt types, variable interpolation, and automatic deployment.

• ✍️ Human annotation, where domain experts can annotate production traces, spans, threads, and incorporate back in datasets for testing.

Who uses Confident AI?

Typical Confident AI users are:

• Engineering teams that focus on code-driven AI testing in development

• Product teams that require annotations from domain expert

• Companies that have AI QA teams needing modern automation

• Teams that want to track performance over time in production

Typical customers include growth-stage startups to up-market enterprises, including Panasonic, Amazon, BCG, CircleCI, and Humach.

How does Confident AI compare to LangSmith?

Confident AI ensures you’re not vendor-locked into the “Lang” ecosystem:

Confident AI is the only choice if you want to support all forms of LLM evaluation around one centralized platform. These include single & multi-turn, for AI agents, chatbots, and RAG use cases alike. Evals are centered around “test cases”, with LLM traces to follow — making it approachable for even non-technical stakeholders.

LangSmith has support for evaluation scores but mainly for traces that are not applicable for all use cases (especially multi-turn), and creates a disconnect for less-technical team members.

Evals on Confident AI is also powered by DeepEval, one of the most popular LLM evaluation framework. This means you get access to the same evaluations as Google, Microsoft, and other Big Techs that have adopted DeepEval.

How popular is Confident AI?

Confident AI is DeepEval’s cloud platform, and as of September 2025, DeepEval has become the world’s most popular and fastest growing LLM evaluation framework in terms of downloads (700k+ monthly), and 2nd in terms of GitHub stars (runner-up to OpenAI’s open-source evals repo).

More than half of DeepEval users end up using Confident AI within 2 months of adoption.

Why do companies use Confident AI?

Companies use Confident AI because:

• It combines open-source metrics with an enterprise platform: Confident AI brings a full-fledged platform to those using DeepEval, and it just works without additional setup. This simplifies cross-team collaboration and centralizes AI testing.

• It is evals centric, not just an UI solution: Customers appreciate that it is not another observability platform with generic tracing. Confident AI offers evals that is deeply integrated with LLM traces, that operates on different components within your AI agents.

• It covers all use cases, for all team members: Since engineers are no longer the only ones involved inAI testing unlike traditional software development, Confident AI is built for multiple personas, even for those without coding experience.

• Customizations are off the charts: Confident AI are used for those needing full control over their LLMOps pipeline, and offers a low-level Evals API. This means users can manage data without clicking around in the UI, and even offer evals to their own clients and customers as a result.

Bottom line: Confident AI is the best LangSmith alternative for growth-stage startups to mid-sized enterprises. It takes an evaluation-first approach to observability, while not vendor-locked into the “Lang” ecosystem.

It’s broad eval capabilities mean you don’t have to adopt multiple solutions within your org, and the Evals API makes it flexible enough for customization.

2. Arize AI

What is Arize AI?

Arize AI is an AI observability and evaluation platform for AI agents, and is agnostic to tools other than LangChain/Graph. It was originally built for ML engineers, with it’s more recent releases on Phoenix, it’s open-source platform, tailored towards developers for LLM tracing instead.

Key Features

• 🕵️ AI agent, with support for graph visualizations, latency and error tracking, integrations with 20+ frameworks such as LangChain.

• 🔗 Tracing, including span logging, with custom metadata support, and the ability to run online evaluations on spans.

• 🧑‍✈️ Co-pilot, a “cursor-like” experience to chat with traces and spans, for users to debug and analyze observability data more easily.

• 🧫 Experiments, a UI driven evaluation workflow to evaluate datasets against LLM outputs without code.

Who uses Arize AI?

Typical Arize AI users are:

• Highly technical teams at large enterprises

• Engineering teams with few PMs

• Companies with large-scale observability needs

While it offers a free and $50/month tier, it’s limitations is a barrier for teams wishing to scale up. Only a maxium of 3 users are allowed, with a 14-day data retention, meaning you’ll have to engage in an annual contract for anything beyond this.

How does Arize AI compare to LangSmith?

While both look similar on paper, and targets the same technical teams, Arize AI is stricter on it’s lower tier plans, and pricing is not transparent for both beyond the middle-tier.

How popular is Arize AI?

Arize AI is slightly less popular than LangSmith, but mostly due to the LangChain brand. Stated on Arize’s website, around 50 million evaluations are ran per month, with over 1+ trillion spans logged.

Data on LangSmith is less readily available.

Why do companies use Arize AI?

1. Self-host OSS: Part of its platform, Phoenix, is self-hostable as it os open-source, making it suitable for teams that need something quick up and running.

2. Laser-focused on observability: Arize AI handles observability scale well, for teams looking for fault tolerant tracing, it is one of the best options.

3. Non-vendor-lockin: Unlike LangSmith, Arize AI is not tied into any ecosystem, and instead follows industry standards such as OpenTelemetry.

Bottom line: Arize AI is the best LangSmith alternative for large enterprises with highly technical teams looking for large-scale observability. Startups, mid-sized enterprises, and those needing comprehensive evaluations, pre-deployment testing, and non-technical collaborations might find better-valued alternatives.

3. Braintrust

What is Braintrust?

Briaintrust Data is a platform for collaborative evaluation of AI apps. It is more non-technical friendly than its peers, with testing more UI driven in a “playground” more than being code-first.

Key Features

• 📐 Evals playground is a key differentiator between LangSmith and Braintrust. The playground allows non-technical teams to test different variations of model and prompt combinations without touching code.

• ⏱️ Tracing with observability is available, with the ability to run evaluations on it, as well as custom metadata logging.

• 📂 Dataset editor for non-technical teams to contribute to playground testing, no code required.

Who uses Braintrust?

Typical Braintrust users are:

• Non-technical teams such as PMs or even external domain experts

• Engineering teams for initial setup

Braintrust puts a strong focus on support non-technical workflows and UI design that is not just tailored towards engineers. Customers include Coursera, Notion, and Zapier.

How does Braintrust compare to LangSmith?

Evaluation playground makes Braintrust a good alternative to LangSmith for users that needs more sophisticated non-technical workflows.

LLM tracing and observability is fairly similar, however teams might find Braintrust’s UI more intuitive than LangSmith’s for analysis.

BrainTrust is more generous seats cap offering unlimited users for $249/month, but has a higher base platform fee for the middle-tier than LangSmith ($39/month).

How popular is Braintrust?

Braintrust is far less popular than LangSmith, largely due to a lack of OSS component. With a lack of community, this also means not a lot of data is available on its adoption.

Why do companies use Braintrust?

1. Non-technical workflows: Even folks that are outside of your company that have never touched a line of code can collaborate on testing on the playground.

2. Intuitive UI: More understandable even for those without a technical background, making it more easy for non-technical folks to collaborate.

Bottom line: Braintrust is a great alternative for companies looking for a platform that makes it extremely easy for non-technical teams to test AI apps. However, for more low-level control over evaluations, teams might have better luck looking elsewhere.

4. Langfuse

What is Langfuse?

Langfuse is a 100% open-source platform for LLM engineering. To break it down, this means they offer LLM tracing, prompt management, evals, to “debug and improve your LLM application”.

Key Features

• ⚙️ LLM tracing, which is similar to what LangSmith has, the difference being Langfuse supports more integrations, with easy-to-setup features such as data masking, sampling, environments, and more.

• 📝 Prompt management allow users to version prompts and makes it easy to develop apps without storing prompts in code.

• 📈 Evaluation allow users to score traces and track performance over time, on top of cost and error tracking.

Who uses Langfuse?

Typical Langfuse users are:

• Engineering teams that need data on their own premises

• Teams that want to own their own prompts on their infrastructure

Langfuse puts a strong focus on open-source observability. Customers include Twilio, Samsara, and Khan Academy.

How does Langfuse compare to LangSmith?

It should not be confused that Langfuse is part of the “Lang”-Chain ecosystem. For LLM observability, evals, and prompt management, both platforms are extremely similar.

However Langfuse does have better developer experience, and its generous pricing of unlimited users for all tiers means there is less barrier to entry.

How popular is Langfuse?

Langfuse is one of the most popular LLMops platforms out there due to it being 100% open-source, with over 12M SDK downloads each month for its OSS platform, while there is little data available for LangSmith.

Why do companies use Langfuse?

• 100% open-source:

  Being open-source means anyone can setup Langfuse without worry about data privacy, making adoption fast and easy.

• Great developer experience:

  Langfuse has great documentation with clear guides, as well as a breadth of integrations supported by it’s OSS community.

Bottom line: Langfuse is basically LangSmith, but open-source with slightly better developer experience. For companies looking for a quick solution that can be hosted on-prem, Langfuse is a great alternative to avoid security and procurement.

For teams that does not have this requirement, needs to support more non-technical workflows, and more streamlined evals, there are other better-valued alternatives.

5. Helicone

What is Helicone?

Helicone is an open-source platform that offers an unified AI gateway as well as observability on model requests for teams to build reliable AI apps.

Key Features

• ⛩️ AI gateway where you could call 100+ LLM providers through the OpenAI SDK format

• 📷 Model observability to track and analyze requests by cost, error rate, as well as tag LLM requests with metadata, enabling advanced filtering

• ✍️ Prompt management to compose and iterate prompts, then easily deploy them in any LLM call with the AI Gateway

Who uses Helicone?

Typical Helicone users include:

• Engineering teams needing multiple LLM providers unified

• Startups that need fast setup and pinpoint cost tracking

Helicone puts a strong focus on its AI gateway, and its observability is not as focused on tracing apps than it is on model requests. Customers include QA wolf, Duolingo, and Singapore Airlines.

How does Helicone compare to LangSmith?

Helicone focuses on observability on the model layer instead of the framework layer, which is where LangSmith operates with LangChain and LangGraph.

Helicone also has an intuitive UI that is usable for non-technical teams, making it a great alternative for those needing cross-team collaboration, open-source hosting, and working with multiple LLMs.

How popular is Helicone?

Helicone is less popular than Langfuse sitting at 4.4k GitHub stars. However it is popular among startups especially among YC companies. Little data is available on LangSmith but it is likely there are more deployments of LangSmith than Helicone.

Why do companies use Helicone?

• Open-source: Being open-source means teams can try it out locally quickly before deciding if a cloud-hosted solution is right for them

• Works with multiple LLMs: Helicone is the only contender on this list that has a gateway, which is a big plus for teams valuing this capability

Bottom line: Helicone is the best alternative if you’re working with multiple LLMs and need observability on the model layer instead of the application layer. It is open-source, making it fast and easy to setup to get through data security requirements.

For teams that are operating at the application layer, and need full-fledged LLM-tracing, and evaluations, there are other alternatives more suited.

Honorable Mentions

• Galileo AI, Traceloop, and Gentrace: Which is similar to Arize AI, but with no community and 100% closed-source.

• Keywords AI: Which is similar to Helicone and adopted within the startup community.

Do you want to brainstorm how to evaluate your LLM (application)? Ask us anything in our discord. I might give you an “aha!” moment, who knows?

Confident AI: The DeepEval LLM Evaluation Platform

The leading platform to evaluate and test LLM applications on the cloud, native to DeepEval.

Regression test and evaluate LLM apps.

Easily A|B test prompts and models.

Edit and manage datasets on the cloud.

LLM observability with online evals.

Publicly sharable testing reports.

Automated human feedback collection.

Try Now for FreeCheckout DeepEval