Your LLM calls don't live in isolation. Neither should your observability.
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LLM observability tools like Arize Phoenix, LangSmith, Langfuse, and Helicone are excellent at what they do: tracing LLM calls, tracking token usage, measuring costs, and evaluating output quality. But your LLM calls do not live in isolation. They depend on API gateways, databases feeding context, vector stores serving embeddings, and the services running all of it. DeepCube correlates LLM telemetry with application health in a single 3D spatial view. This page is an honest look at where DeepCube adds value and where these tools excel.
DeepCube is the product; spatial observability is the category. We are not an APM company, we are a spatial observability company. Where LLM-only tools flatten AI workloads into isolated traces, DeepCube un-compresses your LLM calls back into the full, high-dimensional reality of the system they run on: the gateways, vector stores, embedding services, and databases they depend on, correlated with application health in one spatial view.
Most teams run two observability stacks for AI apps. One for LLM, one for everything else.
DeepCube brings both into one spatial view.
The Blind Spot in LLM Observability
Your LLM calls depend on your application. Your LLM tools cannot see it.
- LLM-only tools see the LLM call, and increasingly the spans around it, but not the system underneath. Several now accept OpenTelemetry from any framework, and Helicone sits at the gateway itself. What none of them builds is the service topology below: which service called which, on what infrastructure, and how a change there moved the LLM behavior here.
- When latency spikes, is it the model, the retrieval pipeline, or the database behind it? LLM-only tools cannot answer that question.
- Two observability stacks: most teams run a traditional APM tool for application health and an LLM tool for AI workloads. Two dashboards. No correlation between them.
One spatial view. LLM calls in the context of your entire system.
Spatial Observability + LLM in One View
See your LLM calls in the context of your entire application stack.
- 3D spatial topology: Your LLM services, the APIs that call them, the vector databases they query, and the relationships between all of them.
- When a RAG pipeline slows down, you see the entire chain: the API request, the vector store query, the LLM call, and the response, all in spatial context.
- Tessa understands both your LLM workloads and your application stack, tracing a slow completion back to a database connection pool exhaustion three services upstream, then fixing it.
LLM observability that sees the whole system
DeepCube vs LLM Observability Tools
Application-level correlation is the gap.
Each LLM observability tool excels at the LLM layer. Arize Phoenix pairs a strong evaluation framework with 40+ auto-instrumentations via OpenInference (github.com/Arize-ai/openinference, verified 2026-10-10). LangSmith is framework-agnostic, ingesting traces over OpenTelemetry as well as from LangChain, with prompt management built in (docs.langchain.com). Langfuse offers open-source (MIT core) prompt management and OTel ingestion (langfuse.com, github.com/langfuse). Helicone provides a simple gateway/proxy with cost tracking, rate limiting, and caching (helicone.ai, docs.helicone.ai). None of them provide spatial observability across your full application stack: service topology, application monitoring, or cross-signal correlation. DeepCube fills that gap.
Note: the LLM-evaluation-quality ratings below (strong/good/basic) are DeepCube's qualitative assessment, not vendor-sourced facts. These evaluation-framework ratings reflect DeepCube's qualitative assessment based on each tool's published evaluation features (datasets, online evals, annotation, experiment tracking) as of 2026-06-01, not a quantitative benchmark. Capability cells reflect each vendor's first-party documentation, re-checked in the 2026-10-10 competitive refresh.
| Capability | DeepCube | Arize Phoenix | LangSmith | Langfuse | Helicone |
|---|---|---|---|---|---|
| LLM Tracing | Yes | Yes | Yes | Yes | Yes |
| Application Monitoring (APM) | Included (via OTel) | No (LLM observability only) | No | No | No |
| Service Topology / Maps | 3D Spatial | No | No | No | No |
| Cross-Signal Correlation | LLM + App + Traces | LLM only | LLM + OTel ingest | LLM traces + LLM-application metrics, OTel ingest | LLM only (gateway) |
| First-Party In-Product AI | Tessa: investigates from where you stand in the 3D model (a node, a trace, or the core for the whole system), across LLM and application signals, and works in your connected codebase workspace; included in every plan | Alyx in Arize AX and PXI in Phoenix (beta), working over LLM trace and eval data; on Enterprise, Signal opens fix PRs against an attached GitHub repo (source, Signal, PXI, verified 2026-10-10) | LangSmith Chat (formerly Polly), Insights, and Engine; Engine reads a connected repo and opens PRs on Plus and Enterprise, metered in LSUs; its fix validation and Red Teaming are in private beta on LangSmith Cloud (source, Engine, verified 2026-10-10) | Langfuse Assistant: available on Cloud, public beta on self-hosted; works over Langfuse data, no first-party codebase access (source, verified 2026-10-10) | None in-product; an MCP server with two query tools for external AI clients (source, verified 2026-10-10) |
| LLM Evaluation Framework (DeepCube's assessment) | Basic | Strong | Strong | Good | Basic |
| Prompt Management | Via codebase | Yes | Yes | Yes | Basic |
| OpenTelemetry Native | gen_ai.* (universal backend target: gen_ai.* + OpenInference + OpenLLMetry) | OTel-native (OpenInference, authored by Arize) | Framework-agnostic, OTel ingestion | Ingests gen_ai.* + OpenInference + OpenLLMetry | Gateway/proxy-based |
Note on the AI row: AI that works in your code is now common, so treat it as table stakes (LangSmith Engine opens pull requests; on Arize Enterprise, Signal opens fix PRs against an attached repo). The difference is what each agent investigates from. The LLM-layer tools reason over LLM traces, evals, and agent code. Tessa investigates from where you stand in the 3D model of the application: the LLM calls and the services underneath them.
AI that works in your code is table stakes. What it investigates from is not.
| Scenario | LLM-Only Tool | DeepCube |
|---|---|---|
| RAG latency spike | Shows LLM call took longer | Shows vector DB connection pool exhausted, causing queuing upstream of LLM call |
| LLM cost spike | Shows token count increased | Correlates cost with traffic patterns, identifies which service generates excessive calls |
| Degraded output quality | Evaluation scores flag the issue | Traces to retrieval pipeline returning stale context due to upstream dependency |
| Code fix | Varies: LangSmith Engine opens PRs from LLM traces and agent code (Plus and Enterprise; its fix validation is in private beta); on Arize Enterprise, Signal opens fix PRs against an attached GitHub repo; Langfuse and Helicone have no first-party code access | Available on every plan. Tessa traces the issue from the 3D topology to the source file in your connected workspace and proposes the change. You review the change and own the outcome. |
Why Application Context Matters for LLM Apps
The bottleneck is rarely the model.
A typical RAG application involves an API gateway, an embedding service, a vector database, a retrieval layer, an LLM API call, post-processing, and a response. When latency spikes, LLM-only tools show you the LLM call took longer. But was it actually the LLM?
In DeepCube's 3D topology, you see the entire pipeline spatially. Tessa can identify that the vector database connection pool is exhausted, causing queuing upstream of the LLM call. The LLM itself is fine. The bottleneck is three services away.
One platform for LLM and application observability
Choosing the Right Tool
Honest guidance on when to use what.
- If you only need LLM evaluation and prompt testing: Arize Phoenix or LangSmith may be the right fit. They have deeper LLM-specific evaluation capabilities than DeepCube today.
- If you need LLM observability AND application monitoring: DeepCube replaces two tools with one. Your LLM traces, application traces, and metrics in a single spatial view.
- If you need to correlate LLM performance with system health: DeepCube shows LLM call latency next to the upstream application services in one 3D topology. The LLM-layer tools on this page do not model your services.
- If you need AI that fixes code: several tools now ship it (LangSmith Engine opens pull requests on Plus and Enterprise; Arize Signal opens fix PRs on Enterprise). Tessa does it on every DeepCube plan, starting from where you stand in the 3D model, up to the whole system from the core, rather than the LLM layer alone, with AI included in the per-node price.
| Capability | DeepCube (Analyze) | LLM Tool + APM Tool |
|---|---|---|
| LLM Observability | Included | LLM tool (separate bill) |
| Application Monitoring (APM) | Included | APM tool (separate bill) |
| Service Topology | Included (3D spatial) | APM tool only (if available) |
| AI Assistant | Included (Tessa) on every plan, with codebase access and code fixes. AI is included in the per-node price: no AI seat, no per-query AI charge. Model quality scales with the plan. | Present, scoped to the LLM layer, and in some cases metered: Alyx in Arize AX and PXI in Phoenix (beta); LangSmith Chat, Insights, and Engine (Engine billed in LSUs, 1 LSU = $1); Langfuse Assistant; a query-only MCP server in Helicone. They reason over LLM-layer data, not the services your application runs on (source, verified 2026-10-10) |
| Cross-Signal Correlation | LLM + App unified | No correlation between tools |
| Estimated Monthly (40 nodes) | $1,575 with volume pricing | Multiple bills, no unified view |
DeepCube pricing from deepcube.ai/pricing. Analyze tier with volume pricing: 25 nodes at $45/node/month plus 15 nodes at $30/node/month = $1,575. All prices USD.
Your OTel instrumentation works with DeepCube out of the box
Already Using an LLM Tool? Add Application Context.
Keep your LLM tool for evaluation. Add DeepCube for everything else.
- Dual-destination: Your OTel Collector fans out telemetry to both DeepCube and your LLM tool simultaneously.
- No code changes: If you already use OTel SDKs, your instrumentation does not change.
- Complement or replace: Use DeepCube alongside your LLM tool, or consolidate when ready. Your choice.
- Exit guarantee: If DeepCube is not right for you, change one endpoint URL. Your instrumentation stays exactly the same.
Dual-Destination Collector Config
exporters:
otlp/deepcube:
endpoint: "https://otlp.deepcube.ai"
headers:
API-Key: "YOUR-API-KEY"
otlp/llm-tool:
endpoint: "your-llm-tool:4317"
service:
pipelines:
traces:
exporters: [otlp/deepcube, otlp/llm-tool]
metrics:
exporters: [otlp/deepcube]Standard OTel Collector config. LLM traces go to both. Application metrics go to DeepCube.
Detailed tool-by-tool comparisons
Arize Phoenix
Strong LLM eval framework, 40+ OpenInference auto-instrumentations, Phoenix MCP server, and in-product agents (Alyx in AX, PXI in Phoenix, beta). LLM observability only: no service topology or application metrics. Now part of Dynatrace (acquisition completed 2026-10-01, ir.dynatrace.com). AX Pro from $50/mo.
CompareLangSmith
Framework-agnostic with OpenTelemetry ingestion (not LangChain-only), prompt management. LangSmith Engine reads connected repos and opens PRs (Plus and Enterprise; fix validation and Red Teaming in private beta). No application-level correlation. Developer tier free, Plus $39/seat.
CompareLangfuse
Open-source (MIT core), OTel ingestion, LLM application-level metrics, Langfuse Assistant in-app agent. No service/infra topology. Acquired by ClickHouse (2026-01-16), stays MIT-licensed and self-hostable. Hobby free, Core $29, Pro $199.
CompareHelicone
LLM gateway/proxy with cost tracking, rate limiting, and caching, plus a query-only MCP server. No cross-service distributed tracing: its published span schema carries no parentSpanId (docs.helicone.ai, verified 2026-10-10). Acquired by Mintlify (2026-03-03); standalone product now in maintenance mode, so weigh continuity before adopting. Free 10K requests, Pro $79.
CompareReady to See LLM Observability in Context?
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