Langtrace Pricing & Plans 2026

Name: Langtrace
Brand: Langtrace
Availability: InStock

Complete pricing guide for Langtrace. Compare all plans, analyze costs, and find the perfect tier for your needs.

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Still deciding? Read our full verdict on whether Langtrace is worth it →

🆓Free Tier Available

💎4 Paid Plans

⚡No Setup Fees

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Free / Open Source

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Cloud Free

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Cloud Pro

Starting at $59/month

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Enterprise

Custom

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Pricing sourced from Langtrace · Last verified March 2026

Feature Comparison

Detailed feature comparison coming soon. Visit Langtrace's website for complete plan details.

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Is Langtrace Worth It?

✅ Why Choose Langtrace

• True OpenTelemetry-native instrumentation: Emits standard OTLP traces and spans, so data can be routed to Grafana, Datadog, Signoz, or any OTel backend without rewriting collectors or losing data fidelity. Teams already invested in OpenTelemetry infrastructure can unify GenAI telemetry with existing microservice observability rather than maintaining a separate system.
• Broad framework and model coverage: Auto-instruments 8 LLM providers (OpenAI, Anthropic, Gemini, Cohere, Groq, Mistral, Perplexity, Ollama) and over 10 frameworks and vector databases including LangChain, LlamaIndex, LangGraph, CrewAI, DSPy, AutoGen, Pinecone, Chroma, Weaviate, and Qdrant. This breadth covers most production GenAI stacks without requiring custom instrumentation.
• Self-hostable open-source core: AGPL-licensed server with Docker Compose deploy means regulated teams can run Langtrace inside their own VPC. The SDK itself is Apache-2.0 to ease commercial integration concerns. This dual-license model gives enterprises the flexibility to instrument applications freely while maintaining data sovereignty over the observability backend.
• Cost and token analytics per model and session: Built-in dashboards break down spend and token usage by model, user, project, and time window, which is concrete enough to drive budget alerts and provide finance teams with attribution data for AI infrastructure costs. Per-request cost is calculated automatically using each provider's pricing, removing the need for manual tracking spreadsheets.
• Integrated evaluation and dataset workflows: Production traces can be promoted into evaluation datasets, annotated with human feedback, and scored using built-in or custom evaluators, closing the loop between monitoring and prompt or model iteration. This eliminates the friction of exporting data to a separate evaluation tool and keeps the quality feedback cycle within the same platform.
• Lightweight setup with minimal code changes: Two-line SDK initialization captures full prompt, completion, tool call, and vector DB telemetry without requiring developers to wrap each LLM call manually. This low-friction onboarding means teams can start collecting observability data in minutes rather than spending days instrumenting their codebase.

⚠️ Consider This

• Younger ecosystem than incumbents: Community size, plugin marketplace, and third-party tutorials are smaller than Langfuse or Datadog, so edge-case issues can require digging into source code or waiting for maintainer responses. The ecosystem is growing but teams accustomed to extensive community resources may find fewer readily available guides and integrations.
• AGPL license on the server: Self-hosting the full Langtrace server under AGPL can raise legal review concerns at enterprises that prohibit copyleft for modified internal forks. Organizations that need to customize the server code should consult legal counsel about AGPL obligations, or use the managed Cloud offering to avoid license concerns entirely.
• Evaluation tooling is less mature than specialists: Built-in evals cover common cases but lack the depth of dedicated platforms like Braintrust or Arize, particularly for complex agent trajectory scoring, custom rubric pipelines, or large-scale human annotation workflows. Teams with advanced evaluation requirements may still need a complementary specialized tool.
• UI can lag on very high-volume workloads: Teams instrumenting millions of spans per day report that querying long time ranges in the hosted UI can be slow without tuning retention and sampling strategies. Self-hosted deployments can mitigate this by scaling ClickHouse resources, but the default configuration is optimized for moderate volumes.
• Limited no-code/business-user surface: Langtrace is engineer-oriented; product managers or non-technical stakeholders will find fewer pre-built reports and visualization options compared with marketing-focused analytics tools. Sharing insights with business teams typically requires exporting data or building custom dashboards outside the platform.

What Users Say About Langtrace

👍 What Users Love

✓True OpenTelemetry-native instrumentation: Emits standard OTLP traces and spans, so data can be routed to Grafana, Datadog, Signoz, or any OTel backend without rewriting collectors or losing data fidelity. Teams already invested in OpenTelemetry infrastructure can unify GenAI telemetry with existing microservice observability rather than maintaining a separate system.
✓Broad framework and model coverage: Auto-instruments 8 LLM providers (OpenAI, Anthropic, Gemini, Cohere, Groq, Mistral, Perplexity, Ollama) and over 10 frameworks and vector databases including LangChain, LlamaIndex, LangGraph, CrewAI, DSPy, AutoGen, Pinecone, Chroma, Weaviate, and Qdrant. This breadth covers most production GenAI stacks without requiring custom instrumentation.
✓Self-hostable open-source core: AGPL-licensed server with Docker Compose deploy means regulated teams can run Langtrace inside their own VPC. The SDK itself is Apache-2.0 to ease commercial integration concerns. This dual-license model gives enterprises the flexibility to instrument applications freely while maintaining data sovereignty over the observability backend.
✓Cost and token analytics per model and session: Built-in dashboards break down spend and token usage by model, user, project, and time window, which is concrete enough to drive budget alerts and provide finance teams with attribution data for AI infrastructure costs. Per-request cost is calculated automatically using each provider's pricing, removing the need for manual tracking spreadsheets.
✓Integrated evaluation and dataset workflows: Production traces can be promoted into evaluation datasets, annotated with human feedback, and scored using built-in or custom evaluators, closing the loop between monitoring and prompt or model iteration. This eliminates the friction of exporting data to a separate evaluation tool and keeps the quality feedback cycle within the same platform.
✓Lightweight setup with minimal code changes: Two-line SDK initialization captures full prompt, completion, tool call, and vector DB telemetry without requiring developers to wrap each LLM call manually. This low-friction onboarding means teams can start collecting observability data in minutes rather than spending days instrumenting their codebase.

👎 Common Concerns

⚠Younger ecosystem than incumbents: Community size, plugin marketplace, and third-party tutorials are smaller than Langfuse or Datadog, so edge-case issues can require digging into source code or waiting for maintainer responses. The ecosystem is growing but teams accustomed to extensive community resources may find fewer readily available guides and integrations.
⚠AGPL license on the server: Self-hosting the full Langtrace server under AGPL can raise legal review concerns at enterprises that prohibit copyleft for modified internal forks. Organizations that need to customize the server code should consult legal counsel about AGPL obligations, or use the managed Cloud offering to avoid license concerns entirely.
⚠Evaluation tooling is less mature than specialists: Built-in evals cover common cases but lack the depth of dedicated platforms like Braintrust or Arize, particularly for complex agent trajectory scoring, custom rubric pipelines, or large-scale human annotation workflows. Teams with advanced evaluation requirements may still need a complementary specialized tool.
⚠UI can lag on very high-volume workloads: Teams instrumenting millions of spans per day report that querying long time ranges in the hosted UI can be slow without tuning retention and sampling strategies. Self-hosted deployments can mitigate this by scaling ClickHouse resources, but the default configuration is optimized for moderate volumes.
⚠Limited no-code/business-user surface: Langtrace is engineer-oriented; product managers or non-technical stakeholders will find fewer pre-built reports and visualization options compared with marketing-focused analytics tools. Sharing insights with business teams typically requires exporting data or building custom dashboards outside the platform.

Pricing FAQ

Is Langtrace really open source, and what license does it use?

Yes. The Langtrace server is released under the AGPL-3.0 license, while the client SDKs are licensed under Apache-2.0. This means you can freely self-host the server and use the SDKs in commercial applications. The AGPL license requires that modifications to the server be shared if you distribute the modified version, but using the hosted Cloud offering avoids any license considerations entirely. The Apache-2.0 SDK license places no copyleft obligations on your application code.

How does Langtrace differ from Langfuse or Helicone?

Langtrace is built natively on the OpenTelemetry standard, so traces are portable to any OTel backend such as Grafana, Datadog, or Signoz. Langfuse uses a custom schema with its own ingestion format, which provides a polished experience within its ecosystem but creates more vendor lock-in for telemetry data. Helicone operates primarily as an API proxy logger that is extremely easy to set up but has less visibility into multi-step agent workflows and framework internals. Langtrace's OTel-native approach is best suited for teams that already have observability infrastructure and want GenAI tracing to integrate with it seamlessly.

Which models, frameworks, and vector databases does Langtrace support?

It auto-instruments 8 LLM providers: OpenAI, Anthropic, Google Gemini, Cohere, Groq, Mistral, Perplexity, and Ollama. Orchestration frameworks include LangChain, LlamaIndex, LangGraph, CrewAI, DSPy, and AutoGen. Supported vector databases include Pinecone, Chroma, Weaviate, and Qdrant. The SDK architecture is extensible, so additional providers and frameworks are added regularly as the ecosystem grows. Custom instrumentation is also supported through manual span creation for unsupported libraries.

Can I deploy Langtrace inside my own infrastructure?

Yes. Langtrace ships a Docker Compose setup and Kubernetes Helm charts so the server, Postgres database, ClickHouse analytics store, and UI can run in your own VPC or on-premises environment. This is particularly valuable for healthcare, finance, and government teams that cannot send raw prompts and completions to third-party SaaS providers. Self-hosted deployments receive all core features including tracing, evaluations, cost tracking, and dataset management at no licensing cost.

Does Langtrace support evaluations and dataset management?

Yes. You can curate datasets from real production traces, annotate them with human feedback, run prompt experiments across model versions, and score outputs using built-in evaluators for accuracy, faithfulness, toxicity, and JSON schema compliance. Custom evaluator functions are also supported. This workflow enables teams to go from observing a production issue to running a scored experiment that validates a fix, all within the same platform without exporting data to external tools.

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Langtrace Pricing & Plans 2026

Complete pricing guide for Langtrace. Compare all plans, analyze costs, and find the perfect tier for your needs.

🆓Free Tier Available

💎4 Paid Plans

⚡No Setup Fees

Is Langtrace Worth It?

✅ Why Choose Langtrace

• True OpenTelemetry-native instrumentation: Emits standard OTLP traces and spans, so data can be routed to Grafana, Datadog, Signoz, or any OTel backend without rewriting collectors or losing data fidelity. Teams already invested in OpenTelemetry infrastructure can unify GenAI telemetry with existing microservice observability rather than maintaining a separate system.
• Broad framework and model coverage: Auto-instruments 8 LLM providers (OpenAI, Anthropic, Gemini, Cohere, Groq, Mistral, Perplexity, Ollama) and over 10 frameworks and vector databases including LangChain, LlamaIndex, LangGraph, CrewAI, DSPy, AutoGen, Pinecone, Chroma, Weaviate, and Qdrant. This breadth covers most production GenAI stacks without requiring custom instrumentation.
• Self-hostable open-source core: AGPL-licensed server with Docker Compose deploy means regulated teams can run Langtrace inside their own VPC. The SDK itself is Apache-2.0 to ease commercial integration concerns. This dual-license model gives enterprises the flexibility to instrument applications freely while maintaining data sovereignty over the observability backend.
• Cost and token analytics per model and session: Built-in dashboards break down spend and token usage by model, user, project, and time window, which is concrete enough to drive budget alerts and provide finance teams with attribution data for AI infrastructure costs. Per-request cost is calculated automatically using each provider's pricing, removing the need for manual tracking spreadsheets.
• Integrated evaluation and dataset workflows: Production traces can be promoted into evaluation datasets, annotated with human feedback, and scored using built-in or custom evaluators, closing the loop between monitoring and prompt or model iteration. This eliminates the friction of exporting data to a separate evaluation tool and keeps the quality feedback cycle within the same platform.
• Lightweight setup with minimal code changes: Two-line SDK initialization captures full prompt, completion, tool call, and vector DB telemetry without requiring developers to wrap each LLM call manually. This low-friction onboarding means teams can start collecting observability data in minutes rather than spending days instrumenting their codebase.

⚠️ Consider This

• Younger ecosystem than incumbents: Community size, plugin marketplace, and third-party tutorials are smaller than Langfuse or Datadog, so edge-case issues can require digging into source code or waiting for maintainer responses. The ecosystem is growing but teams accustomed to extensive community resources may find fewer readily available guides and integrations.
• AGPL license on the server: Self-hosting the full Langtrace server under AGPL can raise legal review concerns at enterprises that prohibit copyleft for modified internal forks. Organizations that need to customize the server code should consult legal counsel about AGPL obligations, or use the managed Cloud offering to avoid license concerns entirely.
• Evaluation tooling is less mature than specialists: Built-in evals cover common cases but lack the depth of dedicated platforms like Braintrust or Arize, particularly for complex agent trajectory scoring, custom rubric pipelines, or large-scale human annotation workflows. Teams with advanced evaluation requirements may still need a complementary specialized tool.
• UI can lag on very high-volume workloads: Teams instrumenting millions of spans per day report that querying long time ranges in the hosted UI can be slow without tuning retention and sampling strategies. Self-hosted deployments can mitigate this by scaling ClickHouse resources, but the default configuration is optimized for moderate volumes.
• Limited no-code/business-user surface: Langtrace is engineer-oriented; product managers or non-technical stakeholders will find fewer pre-built reports and visualization options compared with marketing-focused analytics tools. Sharing insights with business teams typically requires exporting data or building custom dashboards outside the platform.

What Users Say About Langtrace

👍 What Users Love

✓True OpenTelemetry-native instrumentation: Emits standard OTLP traces and spans, so data can be routed to Grafana, Datadog, Signoz, or any OTel backend without rewriting collectors or losing data fidelity. Teams already invested in OpenTelemetry infrastructure can unify GenAI telemetry with existing microservice observability rather than maintaining a separate system.
✓Broad framework and model coverage: Auto-instruments 8 LLM providers (OpenAI, Anthropic, Gemini, Cohere, Groq, Mistral, Perplexity, Ollama) and over 10 frameworks and vector databases including LangChain, LlamaIndex, LangGraph, CrewAI, DSPy, AutoGen, Pinecone, Chroma, Weaviate, and Qdrant. This breadth covers most production GenAI stacks without requiring custom instrumentation.
✓Self-hostable open-source core: AGPL-licensed server with Docker Compose deploy means regulated teams can run Langtrace inside their own VPC. The SDK itself is Apache-2.0 to ease commercial integration concerns. This dual-license model gives enterprises the flexibility to instrument applications freely while maintaining data sovereignty over the observability backend.
✓Cost and token analytics per model and session: Built-in dashboards break down spend and token usage by model, user, project, and time window, which is concrete enough to drive budget alerts and provide finance teams with attribution data for AI infrastructure costs. Per-request cost is calculated automatically using each provider's pricing, removing the need for manual tracking spreadsheets.
✓Integrated evaluation and dataset workflows: Production traces can be promoted into evaluation datasets, annotated with human feedback, and scored using built-in or custom evaluators, closing the loop between monitoring and prompt or model iteration. This eliminates the friction of exporting data to a separate evaluation tool and keeps the quality feedback cycle within the same platform.
✓Lightweight setup with minimal code changes: Two-line SDK initialization captures full prompt, completion, tool call, and vector DB telemetry without requiring developers to wrap each LLM call manually. This low-friction onboarding means teams can start collecting observability data in minutes rather than spending days instrumenting their codebase.

👎 Common Concerns

⚠Younger ecosystem than incumbents: Community size, plugin marketplace, and third-party tutorials are smaller than Langfuse or Datadog, so edge-case issues can require digging into source code or waiting for maintainer responses. The ecosystem is growing but teams accustomed to extensive community resources may find fewer readily available guides and integrations.
⚠AGPL license on the server: Self-hosting the full Langtrace server under AGPL can raise legal review concerns at enterprises that prohibit copyleft for modified internal forks. Organizations that need to customize the server code should consult legal counsel about AGPL obligations, or use the managed Cloud offering to avoid license concerns entirely.
⚠Evaluation tooling is less mature than specialists: Built-in evals cover common cases but lack the depth of dedicated platforms like Braintrust or Arize, particularly for complex agent trajectory scoring, custom rubric pipelines, or large-scale human annotation workflows. Teams with advanced evaluation requirements may still need a complementary specialized tool.
⚠UI can lag on very high-volume workloads: Teams instrumenting millions of spans per day report that querying long time ranges in the hosted UI can be slow without tuning retention and sampling strategies. Self-hosted deployments can mitigate this by scaling ClickHouse resources, but the default configuration is optimized for moderate volumes.
⚠Limited no-code/business-user surface: Langtrace is engineer-oriented; product managers or non-technical stakeholders will find fewer pre-built reports and visualization options compared with marketing-focused analytics tools. Sharing insights with business teams typically requires exporting data or building custom dashboards outside the platform.