LLM traces, correlated with your whole application.

Introduction. LangSmith is a strong LLM tracing and evaluation platform. It is framework agnostic and now ingests OpenTelemetry from any framework, with deep tooling for active LLM development: prompt playground, annotation queues, dataset management, and detailed trace visualization. We respect that. This page is an honest look at where DeepCube takes a different approach, where LangSmith excels, and how you can evaluate both. The difference is not which frameworks each tool sees; it is whether your LLM traces are correlated with the full application and service topology underneath them.

DeepCube is the product; spatial observability is the category. We are not an APM company, we are a spatial observability company.

Deep LLM Tracing, Narrow Application Visibility. What happens when the problem is below the LLM layer?

• LangSmith traces the LLM layer, but production AI applications rarely stay within one framework. You might use LangChain for orchestration, a custom retrieval pipeline, and a fine-tuned model served via vLLM. LangSmith now ingests OpenTelemetry from any framework via its OTLP endpoint, so the trace data travels; what it does not provide is the application topology underneath. (source: docs.langchain.com/langsmith/trace-with-opentelemetry, verified 2026-08-18)
• No visibility into your services: LangSmith does not see the API gateway routing requests, the vector database latency, or the service topology your LLM application depends on. Its data model is runs, traces, threads, and trajectories; there is no host or service entity in it. Infrastructure charts exist only for agents you host on LangSmith Deployment. (source, verified 2026-08-18)
• Open SDK, closed platform: LangSmith's client SDK is open source (MIT) and it accepts end-to-end OpenTelemetry ingestion from any OTel-instrumented app, so instrumentation is not the lock-in point. The platform that stores and renders the data is. The differentiator is not framework coverage; it is correlating LLM traces with full application and service topology. (source, verified 2026-08-18)

Framework-Agnostic, Application-Level Observability. Works with any LLM framework. Correlates with everything else.

• Built on OpenTelemetry, not on any single LLM framework. DeepCube works with LangChain, LlamaIndex, Semantic Kernel, custom pipelines, or any combination.
• Your LLM traces live alongside your application metrics and service topology in a single 3D spatial environment.
• When your LangChain agent slows down, DeepCube shows you whether the issue is in the chain logic, the model provider, the retrieval layer, or the application underneath.

Architecture: How We Differ. Open standard ingestion vs a closed platform.

LangSmith is framework agnostic and ingests OpenTelemetry from any framework. The prompt playground, annotation queues, and dataset management make it a strong choice for teams doing active LLM development. Where it stops is the layer below the LLM: it does not build a service topology or correlate traces with application-level metrics.

DeepCube works with any LLM framework via OpenTelemetry too. The difference is correlation: it provides deep tracing for LangChain, LlamaIndex, Semantic Kernel, or custom pipelines, and ties that LLM telemetry to your full application health and 3D service topology.

Architecture table, full cells with sources.

• Scope, LangSmith: Agent engineering platform: LLM tracing and evaluation, plus agent deployment, sandboxes, an LLM gateway, no-code agents, and an autonomous fix agent. No application or service layer. (source, verified 2026-10-10)
• Framework Support, LangSmith: Framework agnostic; ingests OpenTelemetry from any framework via its OTLP endpoint (source: docs.langchain.com/langsmith/trace-with-opentelemetry, verified 2026-08-18)
• Instrumentation, LangSmith: LangSmith SDK (open source, MIT); also accepts end-to-end OTel ingestion (source, verified 2026-08-18)
• Visualization, LangSmith: Trace trees, prebuilt and custom dashboards with configurable charts (source, verified 2026-08-18)
• Service Topology, LangSmith: No service map in the documented data model: runs, traces, threads, and trajectories, with no host or service entity (source, verified 2026-08-18)
• Application Metrics, LangSmith: No metrics from your services. Infrastructure charts exist only for agents hosted on LangSmith Deployment (CPU, memory, replicas, Postgres, queue depth). (source, verified 2026-08-18)
• Prompt Management, LangSmith: Prompt playground, versioning, hub (source, verified 2026-08-18)
• Annotation / Evaluation, LangSmith: Annotation queues, dataset management, online evals (source, verified 2026-08-18)
• Vendor Lock-in, LangSmith: Low and getting lower: LangSmith now does end-to-end OpenTelemetry, ingesting from any source and exporting OUT to Datadog / Grafana / Jaeger, so the trace data is portable (source, published 2025-03-26). The platform itself remains LangChain-aligned; self-hosted and hybrid deployment are Enterprise-only (source, verified 2026-10-10).

AI: What Each Assistant Investigates From. Code access is table stakes. Context is not. (The AI table's DeepCube column was headed "Tessa (DeepCube)".)

Both products now ship AI that works in your code. LangSmith Engine reads a connected repository and opens pull requests on Plus and Enterprise; its fix validation and Red Teaming features are still in private beta. Tessa works in your connected codebase workspace on every DeepCube plan. You review the change, and you own it.

The difference is what each one investigates from. Engine reasons over LLM traces and agent code. Tessa investigates from where you stand in the 3D model of your application: from the node or trace behind a degraded LangChain result, Tessa correlates the retrieval latency with the vector database performance, checks the embedding service health, and proposes the fix where the cause actually is.

AI table, full cells with sources.

• AI Diagnosis, LangSmith: Engine (autonomous issue detection, root-cause diagnosis, and proposed fixes; Plus and above; its fix validation and Red Teaming features are in private beta, LangSmith Cloud only) (source, beta status, verified 2026-10-10); Insights Agent (automated topic clustering with optional guided categories, scheduled reports) (source, verified 2026-08-18); MCP Server exposes prompts / traces / datasets / experiments to external AI agents (source, verified 2026-08-18)
• Codebase Access, LangSmith: LangSmith Engine connects a GitHub repository via a GitHub App and reads the connected repository's source to locate the code path behind a failing trace. Plus and Enterprise only. (source, verified 2026-10-10)
• What the AI can reach in your source, DeepCube: Tessa reads your code where it already lives, on your machine. No vendor GitHub App, no copy of your repo in our cloud, and only what she reads for your question goes to the model: a list of file names by default, and file contents only when she opens them. She works with your developer's own git access, so she can do only what that developer can already do, under their identity and your existing branch protections.
• What the AI can reach in your source, LangSmith: LangSmith Engine connects through a GitHub App and "reads your source code to locate the code path behind a failing trace", in the repositories the app can access (source, verified 2026-10-10)
• Code Fixes, DeepCube: Available on every plan. Tessa traces the issue to the source file in your connected workspace and proposes the change. You review the change and own the outcome.
• Code Fixes, LangSmith: LangSmith Engine opens pull requests with proposed code changes in the connected repository. Plus and Enterprise only. Neither the Engine docs nor the pricing page puts a release-stage label on this PR flow; Engine's fix validation and Red Teaming are in private beta on LangSmith Cloud, by waitlist. Engine is metered in LSUs (1 LSU = $1; an Engine run can consume about 7 to 45 LSUs), an additional charge on top of the Plus seat fee. (source, pricing, verified 2026-10-10) LangSmith Chat (formerly Polly) refines prompts and generates evaluator code. (source, verified 2026-08-18)
• Root Cause Analysis, LangSmith: Trace-level, plus agent source code when a repository is connected to Engine. Not the surrounding services or infrastructure. (source, verified 2026-10-10)
• Accountability Model, LangSmith: Autonomous loop on Plus and above: Engine detects, diagnoses, opens a PR, and auto-reopens recurring issues. A human approves the PR. (source, verified 2026-10-10)

Pricing: Application Observability Value. One platform vs LLM tool + APM tool.

• One platform, not seven: DeepCube includes LLM observability, APM, and AI diagnosis on one meter. LangSmith now spans Observability, Evaluation, Deployment, Fleet, Engine, Sandboxes, and LLM Gateway, billed in seats and LSUs (1 LSU = $1), and none of them covers the application layer. (source, verified 2026-10-10)
• No framework tax: DeepCube works with any LLM framework via OpenTelemetry. No SDK lock-in, no ecosystem dependency.
• Predictable pricing: Nodes x tier price = monthly cost. The platform is priced per node, not per seat, and there are no per-trace charges.
• AI included: Tessa is part of every DeepCube plan at no extra charge. AI is included in the per-node price: you need no AI seat or add-on, and there is no per-query AI charge. On paid plans the bundled Tessa allowance is a per-node budget; when it is spent, Tessa keeps working on a lighter model rather than stopping. Every plan can run every Tessa skill, including Code Fix and root-cause analysis; higher plans put a more capable model behind them. LangSmith meters Engine in LSUs on top of the Plus seat fee. (source, verified 2026-10-10)

Pricing table, full cells with sources.

• LLM Observability, LangSmith: Developer free / Plus $39/seat/mo / Enterprise custom (source, verified 2026-10-10)
• Infrastructure and Service APM, LangSmith: Not in the documented data model for your services: no host or service entity (source, verified 2026-08-18)
• Distributed Tracing (LLM spans), LangSmith: Available via context propagation headers, for LLM spans (source, verified 2026-08-18). No infrastructure or service APM for your own services.
• AI Assistant, LangSmith: LangSmith Chat (formerly Polly), Insights, and Engine. Insights and Engine require Plus or above; Chat requires you to supply your own model API key as a workspace secret (source, verified 2026-08-18)
• 3D Spatial Topology, LangSmith: Trace trees and dashboard charts (source, verified 2026-08-18)

DeepCube pricing from deepcube.ai/pricing. LangSmith pricing from langchain.com/pricing. Verify current pricing before purchase. All prices USD.

Already Using LangSmith? Add Application Context. No rip and replace required.

• Keep LangSmith for LLM development: Prompt playground, annotation queues, dataset management. Add DeepCube for application monitoring and cross-signal correlation.
• Complement or replace: Use DeepCube alongside LangSmith, or consolidate when ready. Your choice.
• Full-stack correlation: As your stack grows, DeepCube ties LLM telemetry to your service topology and application metrics, the layer LangSmith does not cover. (source, verified 2026-08-18)
• Exit guarantee: If DeepCube is not right for you, change one endpoint URL. Your instrumentation stays exactly the same.