LLM gateway intelligence meets spatial observability.
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Helicone provides simple, elegant LLM proxy integration. Route your API calls through their gateway and you get cost tracking, rate limiting, caching, and request logging with minimal setup. We respect that simplicity. This page is an honest look at where DeepCube takes a different approach, where Helicone excels, and how you can evaluate both.
One thing to weigh first if you are evaluating Helicone in 2026: Helicone was acquired by Mintlify (announced 2026-03-03), and the standalone product is now in maintenance mode, meaning security patches, bug fixes and new model support continue but active feature development has stopped. Mintlify's post says it will "work closely with every customer to support a smooth migration to another platform" (source: mintlify.com/blog/mintlify-acquires-helicone, verified 2026-08-16). To be clear about what has not been announced: there is no sunset date, no end-of-life date and no migration deadline, the pricing page and product remain live, and the status page shows all services operational (verified 2026-08-16). Maintenance mode is a roadmap signal, not a shutdown notice, and it is worth weighing on that basis rather than a stronger one.
DeepCube is the product; spatial observability is the category. We are not an APM company, we are a spatial observability company.
Beyond the gateway. Full request lifecycle visibility.
Gateway Metrics Without Application Context
You see the LLM call. You do not see what triggered it.
- Gateway-level visibility is surface-level visibility. You see the LLM request and response. You do not see the application code that triggered the call, or the service chain that led to it.
- When latency spikes, Helicone tells you the model was slow. It cannot tell you whether the slowdown started upstream in your application stack.
- No cross-service distributed tracing: Helicone captures LLM request/response pairs and builds session trees across LLM calls, vector database queries and tool calls. It accepts OTLP-shaped payloads at
POST /v1/trace/log, but the published span schema carries noparentSpanId, so spans cannot be assembled into a parent-child trace across your microservices. The AI workflow is traced. The system underneath it is not. (source, verified 2026-08-18)
Application-level depth. Full distributed traces. One view.
Go Deeper Than Gateway Metrics
See LLM calls in the context of the full request lifecycle.
- Full distributed traces across all services: from the user request, through your API gateway, into your application logic, through the retrieval pipeline, to the LLM call, and back.
- 3D spatial topology: See the entire journey rendered spatially. When your LLM feature degrades, understand whether the issue is the model, the gateway, the retrieval layer, or the application underneath.
- Application-level correlation: Helicone tells you the model call took 2.3 seconds. DeepCube tells you that 1.8 seconds was the vector database query, and Tessa has a fix ready.
Application-level tracing. Dependency correlation. Full context.
Architecture: How We Differ
Application-level instrumentation vs gateway proxy.
Helicone has the simplest integration story in LLM observability: change your base URL and you are instrumented. The built-in rate limiting, response caching, and cost tracking at the gateway level are practical features that work immediately with no SDK changes.
DeepCube goes deeper than the API boundary. Application-level OpenTelemetry instrumentation captures the full distributed trace: every service, every dependency, every application metric. Your LLM calls appear in the context of the complete request lifecycle.
| Aspect | DeepCube | Helicone |
|---|---|---|
| Scope | LLM observability + distributed tracing (spatial observability) | LLM gateway proxy or async SDK logging (LLM-layer monitoring) |
| Integration Model | OpenTelemetry SDK instrumentation (application-level) | Simple proxy URL swap (gateway-level) |
| Trace Depth | Full distributed traces across all services | LLM request/response pairs and session trees across LLM calls, vector DB queries and tool calls. The published span schema carries no parentSpanId, so spans cannot be assembled into a parent-child trace across services (source, verified 2026-08-18) |
| Visualization | 3D spatial topology + web dashboards | Request logs, usage dashboards, session trees |
| Service Topology | Auto-discovered 3D service map | No service topology or dependency map. Not present in the docs feature index or the plan comparison matrix (docs index, pricing, verified 2026-08-18) |
| Application Metrics | Application metrics via OTel correlated with traces | No application or infrastructure metrics. "User Metrics" is end-user LLM usage analytics, not host, process or runtime telemetry (source, docs index, verified 2026-08-18) |
| Cost Tracking | Token and cost telemetry via OTel | Built-in cost tracking and budget alerts (source: docs.helicone.ai/guides/cookbooks/cost-tracking, verified 2026-08-19) |
| Rate Limiting / Caching | Application-level (your infrastructure) | Built-in gateway rate limiting and response caching (source: docs.helicone.ai/features/advanced-usage/custom-rate-limits and /caching, verified 2026-08-19) |
| Cross-Signal Correlation | Traces + metrics + logs unified in spatial view | LLM-layer only: LLM calls, vector DB queries and tool calls correlated within a session. No correlation with service or infrastructure signals |
Tessa fixes code. You review it. You own it.
| Capability | Tessa (DeepCube) | Helicone |
|---|---|---|
| AI Diagnosis | Cross-signal anomaly detection with spatial context | Read-only MCP Server exposes observability data to "Claude Desktop, Cursor, and other MCP-compatible tools"; no remediation agent (source, verified 2026-08-19) |
| Codebase Access | Full workspace: read, search, rename, modify | No first-party workspace access. The Helicone MCP server exposes exactly two tools, query_requests and query_sessions, both read-only (source, verified 2026-08-19) |
| Code Fixes | Available on every plan. Tessa traces the issue to the source file in your connected workspace and proposes the change. You review, you approve, you commit. | No remediation or action agent. Both MCP tools are queries (source, verified 2026-08-19) |
| Root Cause Analysis | Application-level: LLM + app + dependencies | LLM-workflow level: session trees show where an agent sequence broke down. No visibility below the LLM layer |
| Accountability Model | Human on the loop | Manual debugging by engineer. The MCP server is read-only, so nothing on the Helicone side proposes or applies a change (source, verified 2026-08-18) |
AI: Tessa vs Manual Investigation
Human on the loop.
Tessa accesses your connected codebase workspace, diagnoses from 3D topology, and makes the fix. You review, you own it. When your LLM feature experiences increased error rates, Tessa does not just show you the failed API calls. It traces the issue through the full request lifecycle, correlates it with application health, identifies the root cause, and proposes a code fix.
Helicone shows you that your LLM API calls are failing. Tessa tells you why, traces the cause to your code, and writes the fix.
One platform for LLM + application monitoring. One price.
Pricing: Application Observability Value
One platform vs gateway tool + APM tool.
- One platform, not three: DeepCube includes LLM observability, APM, and AI diagnosis. Helicone covers the LLM gateway layer only.
- Application-level depth: Gateway metrics tell you what happened at the API boundary. DeepCube tells you what happened across your entire stack.
- Predictable pricing: Nodes x tier price = monthly cost. No per-request or per-log charges.
- AI included: Tessa is part of every DeepCube plan at no extra charge. There is no AI seat, no AI add-on, and no per-query AI meter to budget for. Every plan can run every Tessa skill, including Code Fix and root-cause analysis; higher plans put a more capable model behind them.
| Capability | DeepCube | Helicone |
|---|---|---|
| LLM Observability | Included | Hobby (free, 10K req/mo) / Pro $79/mo / Team $799/mo / Enterprise custom (source: helicone.ai/pricing, tier names verified 2026-08-18, prices verified 2026-08-16; product in maintenance mode post-Mintlify acquisition) |
| Application Monitoring (APM) | Included | Not available. Helicone scopes itself to "production LLM applications" (source, verified 2026-08-18) |
| APM / Distributed Tracing | Included | Not available (requires separate tool) |
| AI Assistant | Included (Tessa) | No AI assistant. Read-only MCP server only, no remediation (source, verified 2026-08-18) |
| 3D Spatial Topology | Included | Not available |
| Application Observability Total | $45/node/month (Analyze) | Helicone + APM tool = multiple bills |
DeepCube pricing from immersivefusion.com/pricing. Helicone pricing from helicone.ai/pricing. Verify current pricing before purchase. All prices USD.
Keep gateway features. Add application-level depth.
Already Using Helicone? Add Application-Level Depth.
Complement gateway metrics with application-level tracing.
- Keep gateway features: If you rely on Helicone's rate limiting and caching, keep the proxy. DeepCube adds the application observability layer.
- Add OTel instrumentation: Add OpenTelemetry to your services and point the Collector at DeepCube. Your LLM calls appear in the context of the full request lifecycle.
- Zero re-instrumentation: Your OpenTelemetry SDKs work with DeepCube out of the box.
- Exit guarantee: If DeepCube is not right for you, change one endpoint URL. Your instrumentation stays exactly the same.
OTel Collector Config
exporters:
otlp/deepcube:
endpoint: "https://otlp.deepcube.ai"
headers:
API-Key: "YOUR-API-KEY"
service:
pipelines:
traces:
exporters: [otlp/deepcube]
metrics:
exporters: [otlp/deepcube]Standard OTel Collector config. Application telemetry flows to DeepCube. Keep Helicone as your LLM proxy alongside if needed.
Ready to Go Deeper Than Gateway Metrics?
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