Looking for a LangSmith alternative in 2026? LangSmith is excellent for LangChain-native applications, but the LLM observability market has expanded dramatically. Self-hosted open-source platforms, eval-first systems, OpenTelemetry-native frameworks, and full-stack APM vendors now offer compelling alternatives — many with better pricing, broader framework support, or stronger governance controls. This guide compares the top LangSmith competitors for 2026.
Updated – May 2026
Full refresh for 2026: title updated from 2024, new entrants added (Braintrust, SigNoz, Confident AI, Laminar, Datadog ADK integration, Weights & Biases Weave, TruLens, WhyLabs), current pricing, and a quick-reference comparison table near the top.
- Braintrust raised the bar on production evals with CI/CD quality gates and 1M free trace spans/month.
- Helicone tightened its free tier to 10K requests/month with 7-day retention; Pro is $79/mo.
- Langfuse Cloud Pro is now $199/mo; self-host remains free under MIT.
- LangSmith Plus is $39/seat/mo; self-hosting remains Enterprise-only with $100K+/yr commitments.
- Datadog LLM Observability added native Google Agent Development Kit (ADK) auto-instrumentation in February 2026.
TL;DR: LangSmith Alternatives at a Glance
The market in 2026 splits roughly into five camps: LLM-native SaaS platforms (Braintrust, HoneyHive, Orq.ai), open-source self-hostable platforms (Langfuse, Helicone, Phoenix, Lunary), OpenTelemetry-first frameworks (OpenLLMetry, SigNoz, Laminar), eval-first platforms (Confident AI, TruLens), and enterprise APM extensions (Datadog, New Relic, WhyLabs, Weights & Biases Weave). Pick based on framework lock-in tolerance, data residency, and whether evals or tracing is the bigger pain.
Quick Comparison Table updated May 2026
| Tool | Best For | License / Hosting | Starting Paid Price | Free Tier |
|---|---|---|---|---|
| LangSmith | LangChain-native apps | Commercial SaaS; self-host Enterprise-only | $39/seat/mo (Plus) | 5K traces/mo |
| Langfuse | Self-hosted, framework-agnostic | Open source (MIT); SaaS + self-host | $29/mo Core, $199/mo Pro | 50K observations/mo (30-day retention) |
| Braintrust | Production evals + CI/CD quality gates | Commercial SaaS | $249/mo Pro | 1M trace spans/mo, 10K eval runs |
| Helicone | Fast setup, proxy-based logging | Open source; SaaS + self-host | $79/mo Pro, $799/mo Team | 10K requests/mo (7-day retention) |
| Phoenix by Arize | Drift detection, model explainability | Open source (ELv2); SaaS + self-host | $50/mo AX Pro (up to $1,000/mo) | Phoenix OSS + AX Free |
| HoneyHive | User analytics, multi-agent session replays | Commercial; SOC 2/HIPAA/GDPR | ~$50K+/yr enterprise | Self-serve free tier |
| OpenLLMetry (Traceloop) | OTEL-native, vendor-neutral export | Open source (Apache 2.0) | Traceloop SaaS varies | Free OSS SDK |
| SigNoz | Unified LLM + full-stack observability | Open source; SaaS + self-host | Cheaper than LLM-only tools | Generous OSS tier |
| Confident AI | Eval-first, 50+ research-backed metrics | Commercial SaaS | Custom | Free dev tier |
| Orq.ai | All-in-one collab for technical + non-technical teams | Commercial SaaS | Custom | Trial |
| Datadog LLM Obs | Existing Datadog customers, full APM | Commercial SaaS | Bundled with Datadog | Datadog free tier |
| Lunary | Open-source tracing with prompt eval | Apache 2.0; SaaS + self-host | Paid tiers from ~$20/mo | 1K events/day |
| Portkey | LLM gateway + observability | Commercial; OSS gateway | Tiered | 10K requests/mo |
| Weights & Biases Weave | Teams already on W&B for ML lifecycle | Commercial; bundled with W&B | W&B pricing | W&B free tier |
| TruLens | Quality-metric evaluation framework | Open source | Free | Free OSS |
| WhyLabs | Regulated/on-prem governance, PII controls | Commercial | Custom enterprise | Limited free tier |
How to read this table
“Best For” reflects each vendor’s strongest 2026 positioning, not their only use case. Most platforms overlap on tracing and prompt management — the real differentiator is governance posture, eval depth, and whether you need OpenTelemetry compatibility for an existing observability stack.
An Introduction to LangSmith
LangSmith, released in July 2023, is the commercial observability offering from LangChain, the widely adopted framework for building LLM applications. Its core value proposition lies in tight integration with the LangChain ecosystem — for developers already using LangChain, adopting LangSmith is seamless, with no significant code adjustments required to begin uploading traces from LLM calls to its cloud platform.
The platform is designed around the concept of unified testing and observability. This paradigm allows development teams to capture real user interaction data and transform it into evaluation datasets, uncovering issues that traditional monitoring and testing tools might miss. LangSmith lets users rate LLM replies either manually or by using another LLM, providing a flexible feedback loop for continuous improvement.
From a technical standpoint, LangSmith is API-first and OpenTelemetry (OTEL) compliant, complementing existing DevOps investments rather than requiring an overhaul. It provides a cloud-based SaaS service with a free Developer tier that includes 5,000 traces per month updated May 2026 . The Plus plan runs $39 per seat per month for up to 10 seats. Self-hosting, however, is reserved as an add-on for Enterprise contracts — typically $100,000+ annually plus $950–$1,150/month of Kubernetes infrastructure (16+ vCPUs, 64+ GB RAM minimum) updated May 2026 — which can be a significant consideration for organizations with strict data residency or security requirements.
While LangSmith is an excellent choice for many, especially those embedded in the LangChain ecosystem, the 2026 LLM observability space offers a rich tapestry of alternatives — from comprehensive platforms to specialized open-source frameworks, each with unique strengths.
Top Alternatives to LangSmith in 2026
The LangSmith alternatives landscape is diverse, offering solutions that range from open-source frameworks providing maximum control to comprehensive observability platforms designed for enterprise scale. Each tool is engineered with a specific philosophy — model explainability, user engagement analytics, eval-first quality gates, or cost-effective scalability.
Braintrust: Production Evals and CI/CD Quality Gates
Braintrust has emerged as one of the strongest LangSmith alternatives in 2026, especially for production teams that need to gate releases on quality. The platform unifies production tracing, structured evaluations, and CI/CD integration so scorers run automatically on pull requests, analyze statistical significance, and can block merges when quality degrades updated May 2026 .
Braintrust vs. LangSmith
| Feature | Braintrust | LangSmith |
|---|---|---|
| Primary Focus | Production evals + CI/CD quality gates | Unified testing and observability for LangChain apps |
| Free Tier | 1M trace spans/mo, 10K eval runs, unlimited users updated May 2026 | 5K traces/mo, 1 seat (Developer) updated May 2026 |
| Paid Pricing | Pro $249/mo; Enterprise custom updated May 2026 | Plus $39/seat/mo (up to 10 seats); Enterprise $100K+/yr updated May 2026 |
| Framework Coupling | Framework-agnostic | Deep LangChain integration |
| CI/CD Integration | Native quality gates, scorers run on PRs | Manual configuration required |
| Eval Depth | Structured experiments, statistical comparisons | Dataset-based evals |
Pros: Generous free tier, framework-agnostic, strong CI/CD story, fast UX for engineers.
Cons: Less mature self-hosting story, paid tiers ramp quickly at enterprise scale.
Langfuse: The Open-Source Powerhouse
Langfuse remains the most-used open-source LLM observability tool in 2026, offering a powerful and transparent platform for teams seeking an alternative to commercial offerings. It provides comprehensive tracing, evaluations, prompt management, and metrics to help developers debug and improve their LLM applications.
Its core philosophy is built on being model and framework agnostic, combined with a commitment to open-source principles. This makes Langfuse a highly attractive option for organizations that prioritize customization, data security, and full control over their deployment environments.
Langfuse vs. LangSmith
| Feature | Langfuse | LangSmith |
|---|---|---|
| License | Open source (MIT) updated May 2026 | Commercial, product-driven |
| Deployment | Strong self-hosting + managed cloud | Primarily cloud SaaS; self-host Enterprise-only |
| SaaS Pricing | Free 50K observations/mo (30-day retention); Core $29/mo; Pro $199/mo updated May 2026 | Developer free (5K traces); Plus $39/seat |
| Agnosticism | Model and framework agnostic | Deeply integrated with LangChain |
| Features | Detailed tracing, prompt templating, human/AI evaluation, dataset management | Unified testing and observability, real-user-data evals |
| OTEL Support | First-class | Compliant |
| Self-Host Cost | Free (MIT); operate PostgreSQL + ClickHouse + Redis + S3 + K8s updated May 2026 | $100K+/yr enterprise license plus infrastructure |
Pros: Completely open-source and framework agnostic, excellent self-hosting capabilities, comprehensive feature set, vibrant community.
Cons: Self-hosting requires PostgreSQL, ClickHouse, Redis, S3, and Kubernetes for production scale; non-trivial to operate.
Helicone: The Proxy-Based Open-Source Framework
For teams that prioritize fast setup and proxy-based logging, Helicone presents a compelling alternative. An alumnus of the Y Combinator W23 batch, Helicone is an open-source framework designed for developers who need to efficiently track, debug, and optimize their LLMs. Its architecture is built for flexibility, offering both self-hosted and cloud gateway deployment options.
Helicone vs. LangSmith
| Feature | Helicone | LangSmith |
|---|---|---|
| Model | Open-source; gateway + self-host | Commercial SaaS |
| SaaS Pricing | Free 10K requests/mo (7-day retention); Pro $79/mo (30-day retention, unlimited seats); Team $799/mo (SOC 2 + HIPAA) updated May 2026 | Developer free; Plus $39/seat |
| Setup | Two-line proxy change | SDK + LangChain instrumentation |
| Core Function | Logs requests/responses; Sessions for multi-step workflows | Unified testing and observability |
| Features | Prompt versioning, user segmentation, text and image I/O | OTEL-compliant, native LangChain tracing |
| Target Audience | Teams that want a drop-in proxy with cost analytics | LangChain users wanting an integrated platform |
Setting up Helicone is straightforward, requiring only a couple of code changes to configure it as a proxy. It supports OpenAI, Anthropic, and other OpenAI-compatible endpoints updated May 2026 . With Sessions, developers can track and visualize multi-step workflows across different agents. It also supports prompt versioning and granular user segmentation.
Pros: Open-source MIT-licensed core, fastest setup of any platform, flexible deployment, automatic cost tracking.
Cons: 7-day retention on free tier is shorter than competitors, proxy model adds latency, fewer enterprise features than dedicated platforms.
Phoenix by Arize AI: Deep Insights and Model Explainability
Phoenix, a product of the established ML observability platform Arize AI, focuses on the deep, granular details of model performance. It is a specialized, open-source platform designed to help teams monitor, evaluate, and optimize their AI models at scale, with strong emphasis on model explainability, drift detection, and RAG evaluation.
Phoenix by Arize AI vs. LangSmith
| Feature | Phoenix by Arize AI | LangSmith |
|---|---|---|
| Primary Focus | Model explainability, drift detection, RAG evaluation | Unified testing and observability |
| Pricing | Phoenix OSS free; AX Free hosted; AX Pro $50/mo; up to $1,000/mo enterprise tier updated May 2026 | Plus $39/seat/mo; Enterprise $100K+/yr |
| OTEL Support | Genuinely OTel-native | OTEL-compliant via SDK |
| Key Features | Drift detection, built-in hallucination detection, embedding analysis, LLM-as-judge evals updated May 2026 | Real-user-data evals, LangChain tracing |
| Deployment | Open source (ELv2) self-host or hosted AX | Cloud SaaS primary |
| Compatibility | LangChain, LlamaIndex, CrewAI, OpenAI agents | Native LangChain |
Phoenix excels at identifying when a model deviates from expected behavior due to data pattern changes — model drift — and pairs this with explainability features that maintain trust and transparency. The platform includes a built-in hallucination detector and an OpenTelemetry-compatible tracing agent, making it a robust tracking tool.
Pros: Industry-leading drift detection and explainability, genuinely OTel-native, open source, low-cost SaaS tier.
Cons: Narrower focus; lacks prompt templating and end-to-end deployment features found in Orq.ai or LangSmith.
SigNoz: Unified LLM + Full-Stack Observability
SigNoz has emerged in 2026 as a strong choice for teams that want LLM tracing alongside their existing application observability stack rather than as a separate silo. Built on OpenTelemetry, SigNoz provides traces, metrics, and logs in a single platform — and notably offers significantly cheaper tracing costs than LLM-only specialists updated May 2026 .
Pros: Open source, OTEL-native, unified APM + LLM observability, cheaper tracing at scale.
Cons: Less LLM-specific eval depth than Braintrust or Confident AI; you assemble more of the eval workflow yourself.
Confident AI: Eval-First Observability
Confident AI takes a contrarian position in 2026: evals first, tracing second. The platform ships with 50+ research-backed metrics, regression testing, red teaming, multi-turn agent simulations, and cross-functional workflows that include product managers in eval review updated May 2026 .
Pros: Deepest eval coverage of any vendor, red teaming built in, strong story for regulated industries.
Cons: Tracing UX is less mature than tracing-first vendors, commercial-only.
Orq.ai: The All-in-One Collaboration Platform
Launched in February 2024, Orq.ai has positioned itself as a comprehensive end-to-end solution for managing the entire AI application lifecycle. It’s not just an observability tool but a Generative AI Collaboration Platform designed to help teams develop, deploy, and optimize LLM applications at scale.
One of Orq.ai’s most significant differentiators is its focus on collaboration, bridging the gap between technical and non-technical team members. Its Playgrounds & Experiments feature allows teams to run controlled sessions testing different AI models, prompt configurations, and RAG pipelines. Seamless integration with 130+ AI models updated May 2026 empowers teams to experiment and select the best-fit model for any use case.
For deployment, Orq.ai ensures dependability with built-in guardrails, fallback models, and regression testing. On the security front, Orq.ai meets stringent requirements with SOC 2 certification and compliance with both GDPR and the EU AI Act.
Pros: All-in-one LLMOps platform, 130+ model integrations, strong security and compliance posture (SOC 2, GDPR, EU AI Act), user-friendly for non-technical roles.
Cons: Newer platform with fewer community resources, fewer third-party integrations than established tools.
Other Notable Alternatives updated May 2026
The 2026 LLM observability market is crowded. Several other tools earn consideration for specific use cases:
- HoneyHive: Emphasizes user tracking and engagement analytics, with multi-agent session replays and CI/CD flows. SOC 2, HIPAA, and GDPR compliance out of the box. Enterprise pricing ~$50K+/year updated May 2026 . MIT-licensed self-hosting available.
- OpenLLMetry by Traceloop: Open-source SDK built on the OpenTelemetry standard. Auto-instruments LLM frameworks and transmits observability data to 10+ backend tools. Vendor-neutral telemetry export is the killer feature for teams that already standardize on OTEL.
- Lunary: Model-independent, open-source tracing tool (Apache 2.0) compatible with LangChain and OpenAI agents. Its Radar tool categorizes LLM answers based on predefined criteria. Free tier is limited to 1,000 daily events updated May 2026 .
- Portkey: Started as an open-source LLM Gateway, expanded into observability. Acts as a proxy that maintains a prompt library, caches responses, balances load across models, and configures fallbacks. Free tier of 10,000 monthly requests updated May 2026 .
- Datadog LLM Observability: For organizations already invested in Datadog, extending its use to LLMs is a natural choice. Datadog provides out-of-the-box dashboards for LLM observability and, as of February 2026, automatic instrumentation for applications built with Google’s Agent Development Kit (ADK) updated May 2026 . AI Agent Monitoring is generally available; LLM Experiments and the AI Agents Console are in preview.
- New Relic AI Monitoring: Full-stack observability and APM with strong distributed tracing. Less mature LLM-specific tooling than Datadog but solid end-to-end visibility for teams already on the platform.
- Weights & Biases (Weave): Part of the W&B MLOps suite. Weave tracks LLM calls, prompts, artifacts, and evaluations alongside experiments — ideal if your team already standardizes on W&B for ML lifecycle management. updated May 2026
- TruLens: Open-source evaluation framework focused on LLM quality metrics (feedback functions) and guardrail checks. Integrates into your tracing stack to quantify grounding and hallucinations. updated May 2026
- WhyLabs AI Observatory: Enterprise-grade monitoring with strong data governance, PII controls, and compliance reporting. Well-suited for regulated, on-prem, or VPC deployments. updated May 2026
- Laminar: A newer entrant focused on agent observability with strong session-tracing UX, frequently cited in 2026 alternative roundups.
Choosing Between LangSmith Competitors: A 2026 Decision Framework
The honest answer
There is no single best LangSmith alternative. The right tool depends on your framework stack, governance requirements, and whether your bottleneck is tracing, evals, or quality gates. Most teams end up with two tools — one for tracing and one for evals — not one platform for both.
In 2026, buyers increasingly prioritize production-first evaluations, robust governance, and clear paths to on-prem or VPC deployment. Use this checklist when evaluating LangSmith alternatives:
- Evaluation depth and cost: Built-in evals (human/AI), coverage for production data, transparent pricing for eval runs.
- Tracing granularity: Multi-step/multi-agent traces, token-level metrics, latency breakdowns, span-level context.
- Datasets and versioning: First-class dataset management, prompt/version history, rollbacks across environments.
- Safety and guardrails: Native toxicity/hallucination checks, policy enforcement, deny/allow lists.
- PII/PHI handling: Redaction/anonymization, retention controls, export policies.
- Governance/RBAC: Fine-grained roles, audit logs, SSO/SCIM, approvals, project isolation.
- Deployment model: SaaS vs. self-hosted/VPC, SOC 2/ISO 27001, data residency, air-gapped feasibility.
- Integration coverage: SDKs for OpenAI, Anthropic, Azure, Bedrock; OTEL compatibility.
- CI/CD for prompts and evals: Test gates in CI, regression testing, canarying prompts/models.
- TCO and pricing transparency: Predictable tiers, volumetric costs, free-tier limits that match your scale.
Related deep-dives: our guides to LLM tracing in production, AI agent observability, and comparing prompt management tools unpack the tracing, agent-monitoring, and prompt-versioning criteria in detail.
Quick-Pick Recommendations updated May 2026
- Production team that needs to block bad deployments? Braintrust.
- Startup that needs to move fast and prioritize user feedback? HoneyHive or Helicone.
- Full data control with a self-hosted solution? Langfuse or Helicone (with engineering capacity for ClickHouse + K8s).
- Deep model explainability for high-stakes apps? Phoenix by Arize AI.
- All-in-one collaboration between technical and non-technical stakeholders? Orq.ai.
- Multi-agent or RAG debugging with span-level visibility? Langfuse, Phoenix, or Laminar.
- Highly regulated environment with on-prem governance/compliance? WhyLabs or self-hosted Langfuse.
- Already standardizing on Weights & Biases for ML? W&B Weave.
- Already on Datadog for APM? Datadog LLM Observability — especially with Google ADK.
- Vendor-neutral OpenTelemetry stack? OpenLLMetry or SigNoz.
- Eval-first with regression testing and red teaming? Confident AI or TruLens.
How metacto Helps You Choose and Implement
Navigating the crowded landscape of LLM observability tools can be a daunting task. The decision between LangSmith and its many competitors depends on a complex interplay of factors: your team’s technical expertise, your application’s specific needs, your budget, your long-term scalability goals, and your data security requirements.
With over 20 years of experience in app development and more than 100 successful projects launched, metacto has the deep technical expertise required to guide you through these decisions. Our work in AI development and AI-enabled mobile app development has given us firsthand experience with the challenges of building, deploying, and maintaining robust LLM applications.
As fractional CTOs, we provide the strategic technical leadership to evaluate trade-offs across these platforms with confidence. Once a decision is made, our engineering team can integrate the chosen service — whether it’s LangSmith, Braintrust, Langfuse, or any other competitor — into your application, ensuring you have the visibility you need from day one. If you want a deeper look at LangSmith’s own pricing model before committing, see our LangSmith pricing guide.
Frequently Asked Questions
What is the best LangSmith alternative in 2026?
There is no universal best. For production teams needing CI/CD quality gates, Braintrust leads in 2026. For open-source self-hosting, Langfuse is the most popular choice. For drift detection and explainability, Phoenix by Arize AI. For eval-first workflows with regression testing, Confident AI. The right pick depends on your framework stack, governance posture, and whether tracing or evals is your bigger pain point.
Are there cheaper alternatives to LangSmith?
Yes. Langfuse self-hosted is free under MIT (you operate the infra). Helicone is free up to 10,000 requests/month, then $79/month for Pro. Braintrust has a generous free tier of 1M trace spans per month. Phoenix by Arize is free open-source or $50/month for the hosted AX Pro tier. For comparison, LangSmith Plus is $39 per seat per month and self-hosting requires a $100,000+ annual Enterprise contract.
What are the best open-source LangSmith alternatives?
Langfuse (MIT), Helicone (open-source core), Phoenix by Arize AI (ELv2), OpenLLMetry by Traceloop (Apache 2.0), Lunary (Apache 2.0), SigNoz (Apache 2.0), and TruLens (open source). Langfuse is the most-used open-source LLM observability platform in 2026 thanks to its framework-agnostic design and strong self-hosting story.
Is LangSmith better than Langfuse?
It depends on your stack. LangSmith is better if you are heavily invested in LangChain and want zero-config tracing. Langfuse is better if you need framework agnosticism, full data control via self-hosting, lower SaaS pricing, or you object to LangChain coupling. Langfuse's open-source license and self-hosting flexibility make it the preferred choice for regulated industries and teams that want to own their observability infrastructure.
What about Braintrust vs LangSmith?
Braintrust is purpose-built for production evals and CI/CD quality gates — its scorers run automatically on pull requests and can block merges when LLM quality regresses. LangSmith focuses on unified testing and observability for LangChain apps. Choose Braintrust if you want to enforce quality at the deployment boundary; choose LangSmith if you live inside LangChain and want native integration.
Can I use Datadog for LLM observability instead of LangSmith?
Yes — and in 2026 it is increasingly compelling. Datadog LLM Observability provides out-of-the-box dashboards, supports OpenAI, Anthropic, AWS Bedrock, and LangChain via the Python SDK, and as of February 2026 ships automatic instrumentation for applications built with Google's Agent Development Kit. The trade-off is fewer LLM-specific eval features compared to LangSmith or Braintrust. It is the right answer for teams already invested in the Datadog ecosystem.
How do I evaluate a LangSmith alternative for a regulated industry?
Prioritize four things: (1) deployment model — self-host or VPC support, not SaaS only; (2) compliance certifications — SOC 2 Type II, HIPAA, ISO 27001, GDPR; (3) PII/PHI handling — automatic redaction, granular retention controls, export controls; (4) governance — fine-grained RBAC, audit logs, SSO/SCIM. Top picks for regulated workloads in 2026: self-hosted Langfuse, WhyLabs AI Observatory, HoneyHive, or self-hosted Phoenix.
Conclusion
The journey to building a successful LLM application does not end at deployment. Continuous monitoring, evaluation, and optimization are critical for long-term success, and choosing the right observability platform is a cornerstone of this process. LangSmith offers a powerful, well-integrated solution — especially for teams already utilizing LangChain — but its framework coupling, enterprise-gated self-hosting, and aggressive paid pricing have driven significant migration to alternatives in 2026.
The right LangSmith competitor for you depends on three questions: Where does your framework loyalty land? How important is owning your data? Is your bottleneck tracing depth, evaluation rigor, or production quality gates? Braintrust wins on production evals and CI/CD. Langfuse wins on open-source self-hosting and framework agnosticism. Helicone wins on speed of setup. Phoenix wins on drift detection and OTEL-native architecture. Confident AI wins on eval depth. Datadog wins for existing APM customers. Orq.ai and HoneyHive win for collaboration with non-engineers.
This comparison reflects market and capability updates through May 2026 based on recent industry roundups, vendor pricing pages, and direct platform evaluations updated May 2026 . The best choice is not universal; it is deeply personal to your project’s goals, your team’s structure, and your operational constraints.
Ready to choose your LLM observability stack?
Talk to a metacto engineer about pairing the right observability platform with your AI application. We will assess your framework stack, compliance posture, and team workflow — then implement and integrate the chosen tool end-to-end.
References
- Braintrust. “LangSmith alternatives (2026): Best tools for LLM tracing, evals, and prompt iteration.” 2026. https://www.braintrust.dev/articles/langsmith-alternatives-2026 updated May 2026
- SigNoz. “Top 7 LangSmith Alternatives for LLM Observability in 2026.” 2026. https://signoz.io/comparisons/langsmith-alternatives/ updated May 2026
- Confident AI. “Top 5 LangSmith Alternatives and Competitors, Compared (2026).” 2026. https://www.confident-ai.com/knowledge-base/compare/top-langsmith-alternatives-and-competitors-compared updated May 2026
- Langfuse. “LangSmith Alternative? Langfuse vs. LangSmith.” 2026. https://langfuse.com/faq/all/langsmith-alternative updated May 2026
- Arize AI. “LangSmith alternatives for AI observability and LLM evaluation.” 2026. https://arize.com/resource-hub/langsmith-alternatives/ updated May 2026
- InfoQ. “Datadog Integrates Google Agent Development Kit into LLM Observability Tools.” February 2026. https://www.infoq.com/news/2026/02/datadog-google-llm-observability/ updated May 2026
- LangChain. “LangSmith Plans and Pricing.” 2026. https://www.langchain.com/pricing updated May 2026
- Phoenix by Arize. “Pricing.” 2026. https://phoenix.arize.com/pricing/ updated May 2026