Apify vs LangSmith: Which Is Better in 2026?

Apify is a comprehensive platform for web scraping, data extraction and browser automation, giving developers and businesses the tools to collect data from any website and automate web-based workflows at scale. It combines a marketplace of ready-made scrapers and automation tools ("Actors") with the infrastructure to build, run and scale your own — covering the full spectrum from non-technical users who just need data from a specific site to developers building sophisticated, custom scraping and automation solutions.

At the heart of Apify is its store of pre-built Actors that scrape popular websites and perform common automation tasks, so you can often get the data you need without writing any code — just configure an existing scraper for sites like search engines, social platforms, marketplaces and more, and run it. For custom needs, Apify provides a powerful platform and open-source libraries to build your own scrapers and automations, with managed cloud infrastructure that handles running them reliably at scale, dealing with the hard parts like proxies, browser automation, scheduling, storage and avoiding blocks.

This dual approach — ready-made tools plus a robust build-your-own platform — makes Apify versatile and widely used for market research, lead generation, price monitoring, content aggregation, SEO, training data collection for AI, and countless other use cases that depend on web data. It integrates with other tools and offers APIs so extracted data flows into your systems. As demand for web data continues to surge — driven heavily by AI's appetite for training and real-time information — capable, scalable scraping and automation infrastructure has become increasingly important. Apify is one of the most established and full-featured platforms in this space. For developers and businesses that need to extract data from the web or automate web tasks — whether through ready-made scrapers or custom-built solutions, at any scale — Apify offers a powerful, flexible and proven platform.

LangSmith is an observability, testing and evaluation platform for LLM applications and AI agents, built by the team behind LangChain. As AI apps move from demos to production, LangSmith gives developers the missing visibility layer — showing exactly what an agent did, where it went wrong, what it cost, and whether changes actually made it better.

What is LangSmith?

LangSmith gives you complete visibility into agent and LLM behavior through tracing, monitoring and evaluation. Every run is captured step by step, so you can see the prompts, tool calls, retrievals and model responses that produced an output — and pinpoint what is hurting latency, cost or quality. On top of that sit real-time dashboards, automatic insight clustering, and a rigorous evaluation framework for measuring quality over time.

Who it's for

LangSmith is built for development teams shipping AI agents and LLM applications — from solo builders and startups to large enterprises. Its customers include names like Expedia, Autodesk, Nvidia, Coinbase and ServiceNow, which speaks to how it holds up at serious scale and under real production demands.

Key features

• Tracing: step-by-step visibility into exactly what your agent is doing
• Monitoring: real-time dashboards for token usage, latency, error rates, cost and custom feedback scores
• Insights: automatic clustering to detect usage patterns, common behaviors and failure modes
• Evaluations and datasets for measuring and improving quality
• SmithDB, a purpose-built database for querying nested agent traces with sub-second performance
• SDKs for Python, TypeScript, Go and Java, plus OpenTelemetry support

Framework-agnostic by design

Although it comes from the LangChain team, LangSmith is deliberately framework-agnostic. It works with popular agent frameworks natively and supports OpenTelemetry, so you can instrument an app whether or not it is built on LangChain. That openness matters — it means teams are not locked into one stack to get production-grade observability.

Built specifically for agents

General application-monitoring tools were not designed for the messy, nested, non-deterministic nature of LLM agents. LangSmith was. Its tracing understands multi-step agent runs, SmithDB is optimized for querying those deeply nested traces quickly, and its insight clustering surfaces the failure modes that are unique to AI systems — hallucinations, tool misuse, prompt regressions — rather than just server errors.

Deployment and pricing

LangSmith offers flexible deployment to suit data-residency and compliance needs: fully managed cloud, bring-your-own-cloud (BYOC), and self-hosted. Pricing starts with a free tier for development, then scales with trace volume, with enterprise pricing available on request. That range lets a hobbyist start free and an enterprise run it inside their own infrastructure.

From prototype to production with confidence

The hardest part of building with LLMs is not the demo — it is trusting the system once real users hit it. LangSmith's evaluations and datasets let teams turn subjective "does this feel better?" judgments into measurable scores: you build test sets from real traces, run new prompts or models against them, and see quantitatively whether quality improved or regressed. Paired with live monitoring of cost, latency and error rates, that closes the loop between shipping a change and knowing its true impact, so teams can iterate quickly without breaking what already works.

Why choose LangSmith

For any team taking an LLM app or agent beyond a prototype, LangSmith is close to essential. It turns opaque, unpredictable AI behavior into something you can see, measure and improve — catching regressions before users do and giving you the evaluation data to ship changes with confidence. If you are building agents seriously, purpose-built observability like this is what keeps them reliable in production.