LangSmith Alternatives: 5 LLM Observability Platforms Compared (2026)

Key Takeaways

- LangSmith is tightly coupled to LangChain; alternatives offer framework-agnostic monitoring

- Best alternatives: Helicone (simplicity), Langfuse (self-hosting), Portkey (multi-provider), Braintrust (evaluation)

- Most migrations take 1-2 weeks with parallel running; final cutover in 1-2 days

- Stay with LangSmith if heavily invested in LangChain/LangGraph or using Hub/Datasets features

- All alternatives offer better cost tracking than LangSmith

LangSmith is LangChain's official observability platform, and if you're already deep in the LangChain ecosystem, it's a natural choice. But an increasing number of developers are searching for alternatives—and for good reasons that have nothing to do with LangSmith's quality.

This guide walks through why teams look beyond LangSmith, what makes a good alternative, and detailed comparisons of the top options. Whether you're evaluating LangSmith for the first time or considering a migration, this will help you make an informed choice.

Table of Contents:

• Why Developers Look for LangSmith Alternatives
• What Makes a Good LangSmith Alternative
• Top 5 LangSmith Alternatives Compared
• Feature Comparison Table
• Migration Guide

Why Developers Look for LangSmith Alternatives

LangSmith is a solid product, but it's not the right fit for every team. Here are the most common reasons developers explore alternatives:

1. Tight Coupling to LangChain

LangSmith is built for LangChain. While it technically supports other frameworks, the experience is clearly optimized for LangChain/LangGraph applications.

If your team uses:

• Direct LLM API calls (OpenAI SDK, Anthropic SDK)
• Other frameworks (Vercel AI SDK, Haystack, Semantic Kernel)
• Custom agent architectures

You'll find LangSmith's integration more awkward. You can make it work with custom instrumentation, but it feels like fighting the tool rather than working with it.

2. Cost Tracking Gaps

LangSmith focuses heavily on tracing and evaluation but has historically lacked robust cost tracking features. For teams where LLM spend is a major concern (and whose isn't?), this is a significant gap.

You can calculate costs by multiplying tokens by pricing, but:

• Pricing changes frequently
• Different models have different input/output costs
• Cached tokens, batch API, and other optimizations complicate the math
• Most teams want cost broken down by user, feature, or session

Alternative platforms often build cost tracking as a first-class feature, saving you from building spreadsheets or custom analytics.

3. Pricing Concerns at Scale

LangSmith's pricing is based on traces, which can get expensive as you scale. Teams running millions of LLM calls per month report surprise bills or having to selectively sample requests to stay within budget.

Some alternatives offer:

• More generous free tiers for startups
• Pricing based on API requests (not traces)
• Better predictability for budgeting
• Academic or open-source programs

4. Avoiding Framework Lock-In

Committing to LangChain AND LangSmith creates a double dependency. If you later want to:

• Move to a different framework
• Use direct LLM APIs for performance
• Build custom agent logic

You're locked into both your framework and your observability platform. Some teams prefer to decouple these decisions.

5. Data Sovereignty Requirements

LangSmith is a cloud-hosted service. If your compliance needs require:

• Data staying in your VPC
• Self-hosted deployment
• Air-gapped environments
• Specific geographic data residency

You'll need an alternative with self-hosting options.

What Makes a Good LangSmith Alternative

If you're evaluating alternatives, here's what to look for:

Framework Agnostic

The platform should work equally well with:

• Direct LLM SDK calls (no framework)
• Any LLM framework (LangChain, Haystack, etc.)
• Custom agent implementations

Avoid platforms that require you to restructure your code to fit their paradigm.

Comparable or Better Tracing

LangSmith's tracing is excellent. A good alternative should match or exceed:

• Multi-level trace visualization (parent/child relationships)
• Streaming support
• Function call tracking
• Metadata and tagging
• Search and filtering

First-Class Cost Tracking

Cost visibility should be built-in, not an afterthought:

• Automatic cost calculation for all major providers
• Breakdown by user, session, feature, or custom dimensions
• Cost trends and anomaly detection
• Budget alerts

Transparent Pricing

You shouldn't need to "contact sales" to understand what you'll pay. Look for:

• Published pricing tiers
• Clear usage limits
• Predictable overage handling
• Startup-friendly free tiers

Data Portability

Avoid vendor lock-in:

• Full data export via API
• Webhook integration for real-time events
• Open-source client libraries
• Migration documentation

Top LangSmith Alternatives Compared

Let's dive into the leading alternatives, examining each through the lens of a potential LangSmith user.

1. Helicone

What it is: A lightweight observability platform focused on simplicity and cost tracking.

Integration approach: Proxy-based or SDK

# Proxy approach - change base URL
import openai

client = openai.OpenAI(
    base_url="https://oai.helicone.ai/v1",
    default_headers={
        "Helicone-Auth": "Bearer YOUR_KEY"
    }
)

# SDK approach - decorator
from helicone import trace_llm

@trace_llm
def chat(messages):
    return client.chat.completions.create(
        model="gpt-4-turbo",
        messages=messages
    )

Strengths:

• Extremely simple integration (5-minute setup)
• Excellent cost tracking and analytics
• Generous free tier (100K requests/month)
• Great for teams that want observability without complexity
• Supports caching to reduce costs

Limitations:

• Less sophisticated evaluation framework than LangSmith
• Fewer integrations with external tools
• UI is simpler (pro or con depending on preference)

Best for: Teams that prioritize cost visibility and simple integration over advanced evaluation workflows.

Migration difficulty: Easy. Run both in parallel, cutover when ready.

Pricing: Free up to 100K requests, then $20/month for 1M requests.

2. Langfuse

What it is: Open-source observability platform with a LangSmith-like experience.

Integration approach: SDK with framework integrations

from langfuse.decorators import observe
from langfuse.openai import openai

# Automatic tracing
client = openai.OpenAI()

@observe()
def chat_completion(prompt):
    return client.chat.completions.create(
        model="gpt-4-turbo",
        messages=[{"role": "user", "content": prompt}]
    )

Strengths:

• Open-source (can self-host for free)
• Very similar UX to LangSmith (easier migration)
• Active development community
• Built-in prompt management
• Good LangChain integration (ironic, but useful if you're migrating gradually)

Limitations:

• Self-hosting requires infrastructure management
• Smaller community than LangSmith
• Cloud offering is newer (less mature than competitors)
• Fewer integrations out of the box

Best for: Teams that need data sovereignty or want to avoid vendor lock-in entirely.

Migration difficulty: Medium. Similar mental model to LangSmith, but requires code changes.

Pricing: Free (self-hosted), cloud pricing starts at $59/month for 500K traces.

3. Portkey

What it is: AI gateway with built-in observability, focusing on multi-provider workflows.

Integration approach: Gateway + SDK

from portkey_ai import Portkey

# Unified interface for multiple providers
portkey = Portkey(
    api_key="YOUR_PORTKEY_KEY",
    virtual_key="YOUR_PROVIDER_KEY"
)

# Automatic tracing, fallbacks, load balancing
response = portkey.chat.completions.create(
    messages=[{"role": "user", "content": "Hello"}],
    model="gpt-4-turbo"
)

Strengths:

• Best-in-class multi-provider support (OpenAI, Anthropic, Cohere, etc.)
• Automatic fallbacks and load balancing
• Excellent for teams using multiple LLM providers
• Strong cost optimization features
• Good prompt versioning and testing

Limitations:

• More complex setup than simpler alternatives
• Gateway architecture means another service in your stack
• Higher learning curve

Best for: Teams using multiple LLM providers or needing advanced routing logic.

Migration difficulty: Medium. Requires adopting the gateway pattern.

Pricing: Free up to 10K requests, then starts at $99/month.

4. Braintrust

What it is: Observability platform with a focus on evaluation and experimentation.

Integration approach: SDK with eval framework

from braintrust import init_logger, traced

logger = init_logger(project="my-app")

@traced
def generate_response(question):
    response = client.chat.completions.create(
        model="gpt-4-turbo",
        messages=[{"role": "user", "content": question}]
    )
    logger.log(
        input=question,
        output=response.choices[0].message.content,
        expected="ideal response",  # For eval
        scores={"quality": 0.9}
    )
    return response

Strengths:

• Superior evaluation framework (best-in-class)
• Excellent experiment tracking and comparison
• Strong prompt versioning
• Good for teams doing rigorous prompt engineering
• CI/CD integration for testing

Limitations:

• More expensive than alternatives
• Steeper learning curve
• Evaluation-first design may be overkill for simple use cases

Best for: Teams that prioritize systematic evaluation and experimentation.

Migration difficulty: Medium. Different paradigm from LangSmith.

Pricing: Free tier available, professional plans start at $200/month.

5. Arize Phoenix

What it is: Open-source observability focused on ML and LLM applications.

Integration approach: OpenTelemetry-based tracing

from openinference.instrumentation.openai import OpenAIInstrumentor
from opentelemetry.exporter.otlp.proto.http.trace_exporter import OTLPSpanExporter
from opentelemetry.sdk.trace import TracerProvider
from opentelemetry.sdk.trace.export import SimpleSpanProcessor

# Standard OpenTelemetry setup
tracer_provider = TracerProvider()
tracer_provider.add_span_processor(
    SimpleSpanProcessor(OTLPSpanExporter("http://localhost:6006/v1/traces"))
)

OpenAIInstrumentor().instrument()

Strengths:

• Fully open-source
• Strong focus on ML/LLM observability
• Built on OpenTelemetry standard (future-proof)
• Excellent embedding visualization
• Good drift detection

Limitations:

• Less polished UI than commercial options
• Self-hosting required (no cloud offering currently)
• Smaller community
• More infrastructure to manage

Best for: ML teams that want standardized observability across LLMs and traditional ML.

Migration difficulty: Medium to hard. Different paradigm (OpenTelemetry).

Pricing: Free (open-source).

Feature Comparison Table

Legend: ✗ = No, ✓ = Yes, ✓✓ = Excellent

Migration Guide: LangSmith to Alternatives

If you're currently using LangSmith and want to migrate, here's what the process looks like:

Step 1: Export Your Data

LangSmith provides data export via API:

from langsmith import Client

client = Client()

# Export runs from a project
runs = client.list_runs(project_name="my-project")

for run in runs:
    print(run.id, run.name, run.inputs, run.outputs)

Save this to JSON or CSV for import into your new platform.

Step 2: Parallel Integration

Run both LangSmith and your new platform simultaneously for 1-2 weeks:

# Example: Running LangSmith + Helicone in parallel
from langchain.callbacks import LangChainTracer
from helicone import trace_llm

langsmith_tracer = LangChainTracer(project_name="my-project")

@trace_llm  # Helicone tracing
def chat(messages):
    # LangChain still reports to LangSmith
    chain = get_chain()
    return chain.invoke(
        {"messages": messages},
        config={"callbacks": [langsmith_tracer]}
    )

This lets you validate that the new platform captures everything you need.

Step 3: Update Your Code

Most migrations involve changing decorators or wrappers:

# Before (LangSmith via LangChain)
from langchain.chat_models import ChatOpenAI
from langchain.callbacks import LangChainTracer

llm = ChatOpenAI(callbacks=[LangChainTracer()])

# After (Helicone)
from helicone import trace_llm
from openai import OpenAI

client = OpenAI(base_url="https://oai.helicone.ai/v1")

@trace_llm
def chat(messages):
    return client.chat.completions.create(
        model="gpt-4-turbo",
        messages=messages
    )

Step 4: Migrate Dashboards and Alerts

Recreate your key dashboards in the new platform:

• Cost trends
• Latency percentiles
• Error rates
• Custom metrics

Set up equivalent alerts for:

• Cost spikes
• Quality regressions
• Error rate increases

Step 5: Cut Over

Once you've validated the new platform for 1-2 weeks:

1. Remove LangSmith instrumentation from your code
2. Update documentation and runbooks
3. Train your team on the new platform
4. Cancel your LangSmith subscription (or downgrade to free tier as backup)

Expected timeline: 1-2 weeks for parallel running, 1-2 days for final cutover.

Common gotchas:

• Trace structure may differ (LangSmith's chain-based model vs. span-based)
• Custom metadata might need mapping
• Evaluation datasets need manual export/import
• Alert thresholds may need recalibration based on new platform's metrics

When to Stay with LangSmith

Despite the alternatives, LangSmith is the right choice for some teams:

You're Heavily Invested in LangChain

If your codebase is built on LangChain and LangGraph, and you're not planning to change that, LangSmith's tight integration is a feature, not a bug. You'll get:

• Automatic tracing of complex chains
• Deep visibility into LangGraph state machines
• Seamless prompt hub integration
• Native support for all LangChain components

You Use LangSmith-Specific Features

Some LangSmith features don't have direct equivalents:

• Hub: Centralized prompt management and versioning
• Datasets: Curated test sets with versioning
• Annotation queues: Built-in human evaluation workflows

If these are core to your workflow, alternatives will require cobbling together multiple tools.

Your Team Is Already Trained

If your team knows LangSmith well and it meets your needs, switching costs (learning curve, workflow disruption, potential bugs during migration) may outweigh the benefits of an alternative.

Budget Isn't a Constraint

If LangSmith's pricing works for your scale and budget, and you're satisfied with the features, there's no urgent reason to switch. "If it ain't broke, don't fix it."

Decision Matrix: Which Alternative Is Right for You?

Use this flowchart to narrow your options:

                    Start Here
                        |
                        v
        Need self-hosting? ──YES──> Langfuse or Arize Phoenix
                |
               NO
                |
                v
        Cost tracking priority? ──YES──> Helicone or Portkey
                |
               NO
                |
                v
        Multiple LLM providers? ──YES──> Portkey
                |
               NO
                |
                v
        Evaluation focus? ──YES──> Braintrust
                |
               NO
                |
                v
        Simplest integration? ──YES──> Helicone
                |
               NO
                |
                v
        ML team standardization? ──YES──> Arize Phoenix
                |
               NO
                |
                v
            Helicone (best general-purpose)

Quick Reference:

Conclusion

LangSmith is a strong platform, especially for LangChain-centric teams. But the LLM observability space has matured rapidly, and alternatives now offer compelling benefits:

• Framework independence (use any LLM library)
• Better cost tracking (first-class feature, not an afterthought)
• More flexible pricing (better free tiers, clearer paid plans)
• Self-hosting options (for data sovereignty)
• Specialized strengths (multi-provider, evaluation, simplicity)

The best alternative depends on your specific needs:

• Helicone for simplicity and cost focus
• Langfuse for self-hosting and open-source
• Portkey for multi-provider complexity
• Braintrust for evaluation rigor
• Arize for ML/LLM unification

Most platforms offer free tiers or trials. The best way to decide is to:

1. Integrate 2-3 alternatives in parallel (takes a few hours each)
2. Run them with production traffic for a week
3. Evaluate based on your actual usage patterns

You'll quickly discover which platform fits your team's workflow, technical requirements, and budget.

Ready to try an alternative? Most platforms integrate in under an hour. Start with a free tier, compare the experience to LangSmith, and make your decision based on real data rather than marketing claims.

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