Same OTel philosophy. Different vision for what comes next.
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SigNoz and DeepCube both build on OpenTelemetry from day one. No proprietary agents. No vendor lock-in. Standard OTLP ingestion. If you are evaluating SigNoz, you already understand why OTel-native architecture matters, and we respect that. The question is not which platform collects telemetry better. Both do it the right way. The question is what happens after your data arrives: how you see it, how you investigate it, and whether AI can close the loop from diagnosis to fix.
Same OTel foundation. Different destination.
What If Your Observability Platform Could Fix the Problem?
Beyond dashboards. Beyond diagnosis. All the way to the fix.
- SigNoz gives you better dashboards. DeepCube gives you a spatial topology where your services, their relationships, and their telemetry live in a single 3D environment.
- AI in the price, starting from the space: Tessa diagnoses from the 3D topology and makes the fix in your connected codebase workspace. You review, you own it. Human on the loop.
- Phantoms: when a call goes out and nothing ever reports back from the other side, or a consumer goes quiet while messages keep arriving, DeepCube draws the missing side where it should be, so what is missing shows up in the space instead of as a quiet gap on a dashboard.
- Same OTel philosophy. Different vision for what the engineer experience should be.
OTel-native ingestion. Spatial-native experience.
Where SigNoz Excels
Strong OTel-native architecture with a thriving open-source community.
- ClickHouse single datastore for all signals: traces, metrics, and logs in one backend.
- 31K+ GitHub stars, 140+ contributors building in the open. (source: github.com/SigNoz/signoz via GitHub API, counted 2026-08-19 at 31,889)
- Self-hosted option gives teams full control over their data and infrastructure.
- Real, shipped AI. On 2026-05-04 SigNoz announced its hosted MCP server live, exposing SigNoz telemetry to external coding agents (Claude Code, Cursor, Codex, Gemini CLI); that same post described an in-product AI Assistant as "currently in beta" and did not yet name it. SigNoz announced Noz, its in-product AI teammate, in a separate post in June 2026. This is genuine, not vaporware, and by SigNoz's own account Noz is still pre-GA: "Noz is in beta and free to use while it is in beta", and "When it leaves beta, pricing will be transparent and easy to reason about". (MCP announcement, verified 2026-08-29; Noz announcement, verified 2026-10-10. SigNoz's Noz docs and pricing page do not restate the beta status)
- Now cheaper. SigNoz dropped its Cloud Teams base from $199 to $49/month in 2025. At low telemetry volume it is the cost-efficient choice, and we say so. (source: signoz.io/blog/cloud-teams-plan-now-at-49usd, verified 2026-10-10)
- Honest positioning as a cost-effective Datadog alternative resonates with teams tired of vendor lock-in.
Zero proprietary agents. Full OTel ecosystem.
Architecture: How We Differ
Both OTel-native. Different post-ingestion experience.
Both platforms share the same belief in open standards. DeepCube takes the OTel-native foundation and builds a fundamentally different post-ingestion experience: spatial visualization in 3D, an AI assistant that accesses your codebase and fixes code, and a native desktop client. SigNoz ships one surface and describes it themselves: SigNoz "serves both the API backend and the intuitive ReactJS frontend" (source, verified 2026-08-19).
The difference is in what happens after your telemetry arrives.
| Aspect | DeepCube | SigNoz |
|---|---|---|
| Instrumentation | Standard OpenTelemetry SDKs only | Standard OpenTelemetry SDKs |
| Data Ingestion | Direct OTLP (gRPC + HTTP) | Broader than ours. SigNoz's own architecture doc says its collector is configured to "Ingest telemetry data from diverse sources including Jaeger, Kafka, OpenCensus, OTLP, and Zipkin" and to provide "protocol translation for seamless integration with existing monitoring tools" (source, verified 2026-08-19) |
| Visualization | 3D spatial topology + web dashboards | Dashboards, charts and the Service Map, which SigNoz says "shows observed dependency endpoints, services, databases, or messaging systems, as nodes" and which we characterize as 2D (retrieved 2026-10-10) |
| AI Assistant | Tessa: diagnoses + fixes code in your workspace | Noz in-product AI assistant (listed on the Teams plan, in beta per its launch post, and Cloud-only: SigNoz's own repository describes it as "a native AI teammate (in SigNoz Cloud)" and says it "is available only on SigNoz Cloud") + MCP Server (43 tools, Cloud-hosted or self-hosted) so external agents can query SigNoz; Noz itself reads telemetry and writes SigNoz configuration (repository, source, MCP, verified 2026-10-10) |
| Deployment | Managed SaaS | Self-hosted (free) or managed cloud |
Tessa starts from the spatial view. You review the fix. You own it.
| Capability | Tessa (DeepCube) | SigNoz |
|---|---|---|
| AI Assistant | Tessa: full-featured AI assistant in-product | Noz in-product AI assistant (listed on the Teams plan, in beta) + MCP Server (43 tools, Cloud-hosted or self-hosted). SigNoz defines that server as "an open standard that lets AI agents interact with your SigNoz observability data" (source, verified 2026-10-10), and scopes Noz to "your traces, logs, metrics, dashboards, alerts, and infrastructure in plain English" (source, verified 2026-10-10) |
| Codebase Access | Full workspace: read, search, rename, modify | The codebase is yours, not SigNoz's, and their own sentence order says so: "Connect SigNoz to your coding agent and get the full picture in one query: traces, logs, metrics, service topology, and deployment history. Your codebase is already loaded, so you can correlate production telemetry with the code that caused it" (source, verified 2026-08-19). It is already loaded because it lives in the coding agent you supply. SigNoz names the boundary too: "The SigNoz MCP Server connects external coding agents (Claude Code, Cursor, and others) to SigNoz" (source, verified 2026-10-10) |
| Spatial Context | 3D topology + traces + metrics | 2D dashboards plus a real service dependency view, which SigNoz describes as an "Overview of your application graph with health indication", and the context it hands a coding agent includes "service topology" (pricing matrix, agent-native observability, verified 2026-08-19). A health-annotated graph, not a space you can move through |
| Code Fixes | 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. | No first-party code fixes, and SigNoz's own word for what Noz changes is configuration: "Noz can also take that next step for you, like creating a dashboard, setting up an alert, or saving a view", and "When a suggested fix would change your configuration, Noz asks for confirmation first" (source, verified 2026-10-10). The write surface matches: 14 of the 43 MCP tools mutate, and all 14 act on SigNoz objects (alert rules, dashboards, saved views, notification channels), none on a source file (tool list, counted 2026-10-10) |
| Root Cause Analysis | AI-driven with spatial context | AI-assisted via Noz |
| Pricing | Included in the price of every plan. No AI seat or add-on required, no per-query AI meter. Model quality scales with the plan. | Noz is listed on the SigNoz pricing page in the $49/month Teams plan ("Access to MCP Server and Noz: Our new AI teammate"), and its launch post says it is free while in beta, with post-beta pricing not yet set: "When it leaves beta, pricing will be transparent and easy to reason about" (pricing, launch post, verified 2026-10-10) |
AI: Tessa vs Noz
Human on the loop.
Tessa connects the telemetry of the environment you are viewing to your connected codebase. Add your repositories as workspaces, and Tessa can trace a production issue from the 3D topology straight to the source file. Tessa accesses your connected codebase workspace, diagnoses from 3D topology, makes the fix. You review, you own it. Human on the loop.
SigNoz now ships its own in-product AI assistant (Noz, listed on the Teams plan, in beta) and an MCP Server, so it is no longer a question of whether SigNoz has AI. The difference is what the AI can reach, and who you have to buy it from. SigNoz states the fork itself, in the first line of its AI overview: "Use Noz, the AI teammate built into SigNoz, or connect your own coding agents to your data", and the branch that reaches a fix is the second one, "Connect your own coding agents to SigNoz and go from alert to root cause to fix without leaving your dev environment" (source, verified 2026-08-19). Noz reasons over your observability data and writes SigNoz configuration. SigNoz closes the loop to code by sending its telemetry out to a coding agent you already own, which it calls being "a neutral data layer for AI-assisted debugging" (source, verified 2026-08-19). Tessa closes it inside one product, against the 3D topology, with no second vendor to buy and wire up.
Both platforms ingest OTel data the right way, and both now offer AI. Working in your code is no longer rare: SigNoz gets there through the coding agent you connect, and Tessa does it in-product. The sharper difference is where the investigation happens: in a 3D spatial topology that draws what stops reporting, with the AI included in one per-node price rather than an in-product beta whose post-beta pricing is not yet set.
Predictable pricing. AI and 3D included.
Pricing: The Full Picture
Scenario: 40 nodes, 10 engineers.
SigNoz uses usage-based pricing after a $49/month base credit. Costs scale with data volume. Here is what a realistic deployment looks like when you factor in actual telemetry volumes:
- SigNoz costs less at low volume. If your team generates minimal telemetry, SigNoz Cloud can be more affordable. We are honest about that. SigNoz is the cost-efficient choice if a broad, conventional 2D observability suite covers your need. DeepCube's higher price buys spatial topology and an in-product code-fix AI, so the comparison is break-even only if those two have no value to your team.
- DeepCube costs are predictable. Nodes x tier price = monthly cost. No surprises when log volume spikes during incidents.
- SigNoz's route to your code is a coding agent you bring. SigNoz reaches code by handing telemetry to a coding agent you license separately, and enumerates what SigNoz itself supplies: "traces, logs, metrics, service topology, and deployment history. Your codebase is already loaded" (source, verified 2026-08-19). Its own AI (Noz, listed on the Teams plan, in beta) and its MCP Server (43 tools, Cloud-hosted or self-hosted) work on observability data and SigNoz objects. Tessa is in the price of every DeepCube plan and works in your connected codebase workspace too. You review, you own it. (source, MCP, verified 2026-10-10)
- SigNoz's investigation surface is 2D at every price point. Dashboards, charts and the Service Map, a node-and-edge dependency graph we characterize as 2D (retrieved 2026-10-10), against DeepCube's 3D spatial topology. That is a fundamentally different investigation experience.
- Self-hosted is not free to run, and SigNoz sorts buyers on exactly that. Their own plan chooser puts Community under "Have DevOps expertise & want full control" and "Ideal for: Solo developers & small startups", and Cloud Teams under "Want zero operational overhead & quick setup" (pricing, verified 2026-08-19). Their capacity-planning guide sizes a Community deployment at 56 CPU cores and 152 GiB of memory across 10 nodes spanning ClickHouse, ZooKeeper, PostgreSQL and collectors, and their production guide notes that "SigNoz Cloud manages cluster isolation, scaling, retention, and TLS for you" before telling self-hosters to "Run SigNoz on a cluster separate from the applications it monitors", to "Set a retention period for each signal separately", to put SigNoz "behind a TLS ingress", to scale the Collector "horizontally", and to reduce trace volume with sampling (capacity planning, verified 2026-08-19; production readiness, verified 2026-10-10)
- The free edition is not the whole product, by their own matrix. SigNoz marks Community with a cross on rows including anomaly detection, SAML support, multiple ingestion keys, per-key rate limits, one-click AWS integrations, prebuilt integrations and dashboards, data centers in the US, EU and India, and email support; Team Training and Instrumentation Support are Enterprise-only (pricing, read from the matrix markup 2026-08-19).
| Line Item | DeepCube | SigNoz Cloud |
|---|---|---|
| Base Platform | Included | $49/month (includes $49 usage credit) (source: signoz.io/pricing, verified 2026-10-10) |
| Traces (est. 200GB/mo) | Included | $0.30/GB = ~$60 (source: signoz.io/pricing, verified 2026-10-10) |
| Logs (est. 500GB/mo) | Included | $0.30/GB = ~$150 (source: signoz.io/pricing, verified 2026-10-10) |
| Metrics (est. 50M samples/mo) | Included | $0.10/M samples = ~$5 (source: signoz.io/pricing, verified 2026-10-10) |
| AI Assistant | Included (Tessa: codebase access + code fixes) | Noz (listed on the Teams plan, in beta, Cloud-only): telemetry plus SigNoz configuration; codebase reach needs a coding agent you license separately (source, verified 2026-10-10) |
| 3D Visualization | Included | Not offered; SigNoz's investigation surfaces (dashboards and the Service Map) are 2D in our characterization (retrieved 2026-10-10) |
| Estimated Monthly Total | $1,575 (Analyze, with volume pricing) | ~$215+ (scales with data volume) (source: signoz.io/pricing, verified 2026-10-10) |
SigNoz pricing based on published rates from signoz.io/pricing, verified as of 2026-10-10. Actual costs vary by data volume. DeepCube pricing from deepcube.ai/pricing. All prices USD.
You don't have to rip and replace
Run Both. Compare Side by Side.
Same telemetry, two destinations, zero re-instrumentation.
- Dual-destination: Since both SigNoz and DeepCube are OTel-native, your OpenTelemetry Collector can fan out telemetry to both destinations simultaneously.
- No code changes: Keep SigNoz for your existing workflows. Point the same data at DeepCube. Compare the investigation experience with identical production data.
- True portability: Unlike migrating from proprietary agents, moving between OTel-native platforms is a config change, not a project.
- 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/signoz:
endpoint: "your-signoz-endpoint:4317"
service:
pipelines:
traces:
exporters: [otlp/deepcube, otlp/signoz]
metrics:
exporters: [otlp/deepcube, otlp/signoz]Standard OTel Collector config. Same telemetry, two destinations. No code changes required.
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