# Telemetryaborted

*/Startups/Telemetryaborted*

## Startup Overview

This telemetry filtering engine intercepts distributed traces before they reach downstream ingestion points. Engineering and site reliability teams constantly pay exorbitant indexing costs for millions of successful, repetitive observability spans that provide zero diagnostic value. By evaluating telemetry streams in transit, the system identifies and drops useless spans at the edge, drastically reducing the volume of data sent to downstream monitoring dashboards and storage tiers.

Traditional alternatives rely on static sampling configurations that blindly discard data or heavy routing pipelines like Cribl that require complex rule management. Instead, this engine operates with native awareness of distributed transaction aborts, guaranteeing that failed execution paths and critical errors are always preserved while routine success data is purged. The service operates on an outcome-based pricing model tied directly to the anomalies it retains, aligning observability costs strictly with diagnostic value rather than raw ingestion volume.

## Startup Founding Hypothesis

**Approach**: that drops useless observability spans before downstream ingestion
**Competitors**:
- [Cribl](/Competitors/Cribl)
- [Datadog](/Competitors/Datadog)
- [Static Sampling Configs](/Competitors/Static_Sampling_Configs)
**Differentiator2x2**: outcome-priced for retained anomalies and natively aware of distributed transaction aborts

## Startup Solution Coordinate

**Solution**: [Trace Sieve](/Software/Trace_Sieve)

## Startup Position2x2

```mermaid
quadrantChart
    title Telemetryaborted Market Position
    x-axis Blind to Transaction State --> Natively Abort-Aware
    y-axis Volume-Based Pricing --> Outcome-Priced for Anomalies
    quadrant-1 High Context, Value Pricing
    quadrant-2 Low Context, Value Pricing
    quadrant-3 Low Context, Volume Pricing
    quadrant-4 High Context, Volume Pricing
    Static Sampling Configs: [0.15, 0.15]
    Cribl: [0.25, 0.45]
    Datadog: [0.60, 0.20]
    Telemetryaborted: [0.85, 0.85]
```

## Startup Offer

**Proof**:
- Targeting a 70%+ reduction in downstream Datadog or Splunk ingestion costs for high-throughput microservices.
- Aiming for zero dropped spans on critical HTTP 5xx or gRPC aborts.
- Targeting sub-millisecond evaluation overhead at the collector node to prevent pipeline backpressure.
**Tiers**:
- Name: Pay-Per-Anomaly · Price: ~$0.50–$1.50 per 1,000 retained anomalous traces · Inclusions: Unlimited span evaluation, out-of-the-box OpenTelemetry collector compatibility, and guaranteed retention of all aborted, failed, or timed-out distributed transactions.
- Name: Enterprise Throughput · Price: ~$2,500–$5,000/mo base + ~$0.20 per 1,000 anomalies · Inclusions: Custom span evaluation heuristics, dedicated tenant isolation, complex sampling rules for successful traces, and automated circuit breakers to cap costs during massive outages.
**Guarantee**: If the system drops spans for a failed, aborted, or error-flagged distributed transaction, we will refund the anomaly processing cost for that entire billing cycle.
**Business Function**: ProvideService
**Objection Handlers**:
- Concern: We still need successful traces to establish latency baselines. Rebuttal: Designed to pass through a configurable, randomized sample of successful traces while keeping 100% of anomalies.
- Concern: Another complex infrastructure piece to manage and scale. Rebuttal: Built to deploy as a lightweight, drop-in binary replacement for your existing OpenTelemetry collector.
- Concern: If we pay per anomaly, a massive cascading outage will spike our bill. Rebuttal: Includes automated billing circuit breakers that cap daily usage costs during severe infrastructure incidents.
- Concern: Cribl already lets us drop data before ingestion. Rebuttal: Instead of requiring you to write and maintain complex regex pipelines, it natively understands distributed tracing protocols to automatically detect aborts.
**Pricing Architecture**: UsageMeter
**Agent Checkout Support**:
- agentic-commerce-protocol

## Startup Brand

**Voice**: Technical and blunt, prioritizing exact data reduction over marketing jargon.
**Tagline**: Drop useless traces before you pay to ingest them.
**Icon Concept**: sieve
**Palette Intent**: electric-signal
**Visual Identity**: A high-contrast aesthetic using terminal black and neon cyan, featuring monospaced typography and harsh geometric cropping to emphasize precision data reduction.
**Archetype Reference**: the-ruler

## Startup Buyer Chain

**Chain**: B2B → Platform Engineering Leader → Site Reliability Engineering Team
**Gtm Motion**: Bottom-up adoption where SREs deploy a lightweight staging proxy to prove span reduction without losing trace fidelity, expanding to enterprise production clusters through outcome-based pricing tied strictly to the volume of retained transaction aborts.
**Agent Channel**: Designed to publish a structured tool schema to OpenTelemetry-compatible AI registries and Auto-SRE agent integration catalogs, allowing autonomous FinOps or remediation agents to discover the routing endpoint and dynamically drop noisy spans.
**Primary Channel**: Developer-focused technical articles and open-source GitHub repositories capturing direct search intent for 'OpenTelemetry tail-based sampling' and 'reduce Datadog ingest costs'.

## Startup Customer Journey

```mermaid
flowchart LR; A[Tech Article] --> B[Staging Proxy]; B --> C[Ingest Cost Metric]; C --> D[Pay-Per-Anomaly Subscription]; D --> E[Production Cluster]; E --> F[Agent Catalog]; F --> G[FinOps Case Study];
```

## Startup Proof Points

_Illustrative — target and order-of-magnitude estimate figures, not an achieved track record (this Thing is concept-stage)._

**Pilot Goals**:
- A 14-day shadow deployment mirroring staging traffic to prove accurate anomaly retention without requiring custom regex pipeline maintenance.
- A 30-day single-service production pilot to quantify the exact reduction in trace ingestion bytes sent to existing downstream observability platforms.
**Target Metrics**:
- Target: 70%+ reduction in downstream Datadog or Splunk ingestion trace volume
- Aim: Sub-millisecond span evaluation overhead at the collector node
- Target: 100% retention rate for HTTP 5xx and gRPC aborted distributed transactions
**Target Case Studies**:
- A mid-market e-commerce platform targeting a massive reduction in Datadog ingestion costs by routing only 5xx errors and a minimal baseline of 200s.
- A high-throughput fintech API provider aiming to secure zero dropped traces for payment aborts while dramatically cutting overall APM volume.
- A streaming infrastructure SaaS replacing manual trace sampling rules with automated anomaly detection natively at the collector level.
**Testimonial Targets**:
- A VP of Engineering expressing satisfaction at reducing APM vendor bills without losing critical diagnostic data for production outages.
- A Principal Site Reliability Engineer praising the immediate ease of swapping in the binary replacement for their existing OpenTelemetry collector.
- A Director of Infrastructure highlighting peace of mind regarding the automated billing circuit breakers that successfully capped costs during a cascading system failure.

## Startup Top Risks

**Risks**:
- Severity: existential · Description: Incumbent observability platforms natively bundle intelligent distributed transaction dropping into their agents, nullifying the need for a standalone pre-ingestion pipeline. · Mitigation Status: unmitigated
- Severity: high · Description: The filtering engine incorrectly drops spans tied to a critical production incident, causing a severe visibility blindspot and immediate enterprise churn. · Mitigation Status: in-progress
- Severity: high · Description: The outcome-based pricing model fails to generate predictable revenue due to customer disputes over what technically qualifies as a billable retained anomaly. · Mitigation Status: unmitigated
- Severity: moderate · Description: Deploying the pre-ingestion filter introduces unacceptable latency or memory overhead into the customer's high-throughput telemetry pipeline. · Mitigation Status: in-progress

## Startup Competitors

- [Cribl](/Competitors/Cribl) — Observability Pipeline
- [Datadog](/Competitors/Datadog) — Incumbent
- [Static Sampling Configs](/Competitors/Static_Sampling_Configs) — Status Quo
- [Honeycomb](/Competitors/Honeycomb) — Modern Observability
- [Splunk Observability](/Competitors/Splunk_Observability) — Enterprise Incumbent

## Startup Solution Stack

- [Anomaly Retention Service](/Services/Anomaly_Retention_Service) — Service-as-Software
- [Transaction Sieve Agent](/Agents/Transaction_Sieve_Agent) — Agent
- [Span Evaluation Agent](/Agents/Span_Evaluation_Agent) — Agent
- [Abort Awareness Engine](/Software/Abort_Awareness_Engine) — Software
- [Ingestion Filtering SDK](/Software/Ingestion_Filtering_SDK) — Software

## Startup Story Brand

**Hero**:
- **Need**: to be the system architect who eliminates waste without losing visibility into failures
- **Want**: to stop paying for millions of useless traces in Datadog or Splunk
- **Identity**: the site reliability engineer managing high-throughput microservices
**Plan**:
- Step: Deploy binary · Detail: Swap your standard OpenTelemetry collector with our drop-in binary to start evaluating spans locally.
- Step: Validate retention · Detail: Verify that 100% of HTTP 5xx and gRPC aborts are retained while successful traces are sampled down.
- Step: Reduce ingestion · Detail: Watch your downstream observability bill drop as useless successful spans are discarded before ingestion.
**Guide**:
- **Empathy**: Does your OpenTelemetry collector still dump thousands of successful spans into storage while you struggle to find the one aborted trace?
**Problem**:
- **Villain**: static sampling configs
- **External**: High-throughput services generate terabytes of redundant HTTP 200 spans that balloon ingestion costs while critical gRPC aborts get sampled out.
- **Internal**: You feel like you are being taxed for your service's success while flying blind during outages.
- **Philosophical**: Why should an engineering team accept a 70% waste tax when only the failed transactions actually matter for debugging?
**Success**: You retain every single anomalous trace for debugging while cutting your observability spend by 70%. Your dashboards show precise failure data without the noise of a million successful hits.
**One Liner**: Instead of paying to ingest millions of healthy traces, Telemetryaborted drops useless spans before they reach your backend — ensuring you only pay for the failures that matter.
**Positioning**:
- **So That**: eliminate 70% of trace waste without losing critical failure data
- **Unlike**: Cribl or Datadog ingestion
- **For Whom**: site reliability engineers at high-throughput microservice companies
- **Category**: Intelligent Telemetry Pipeline
**Call To Action**:
- **Direct**: Process traces
- **Transitional**: View span evaluation schema
**Failure Stakes**:
- Six-figure monthly ingestion overages
- Losing the root-cause trace during a critical outage
- Budget-draining redundancy in Splunk
**Transformation**:
- **To**: the architect who automates precision observability
- **From**: the SRE manually tuning complex Cribl regex pipelines
**Controlling Idea**: Observability costs must scale with system anomalies, not total transaction volume.

## Startup Token Hero

**Genre**: founding-hypothesis
**Rendered**: Instead of paying to ingest millions of healthy traces, Telemetryaborted drops useless spans before they reach your backend — ensuring you only pay for the failures that matter.
**Mechanism**: spine-derived-v1
**Template Id**: spine-founding-hypothesis
**Vocab Fingerprint**: edc3415eb09fff77

## Startup Token Positioning

**Genre**: moore-positioning
**Rendered**: Intelligent Telemetry Pipeline for site reliability engineers at high-throughput microservice companies. Unlike Cribl or Datadog ingestion — eliminate 70% of trace waste without losing critical failure data.
**Mechanism**: spine-derived-v1
**Template Id**: spine-moore-positioning
**Vocab Fingerprint**: b6756dae6a1713e8

## Startup Token Pitch Deck

**Genre**: pitch-deck
**Rendered**: Problem: High-throughput services generate terabytes of redundant HTTP 200 spans that balloon ingestion costs while critical gRPC aborts get sampled out.
Solution: Instead of paying to ingest millions of healthy traces, Telemetryaborted drops useless spans before they reach your backend — ensuring you only pay for the failures that matter.
Customer: site reliability engineers at high-throughput microservice companies
Unlike: Cribl or Datadog ingestion
**Mechanism**: spine-derived-v1
**Template Id**: spine-pitch-deck
**Vocab Fingerprint**: 406d81dce1a5385b

## Startup Token M E D D P I C C

**Pain**: High-throughput services generate terabytes of redundant HTTP 200 spans that balloon ingestion costs while critical gRPC aborts get sampled out.
**Metrics**: Target: You retain every single anomalous trace for debugging while cutting your observability spend by 70%. Your dashboards show precise failure data without the noise of a million successful hits.
**Rendered**: Pain: High-throughput services generate terabytes of redundant HTTP 200 spans that balloon ingestion costs while critical gRPC aborts get sampled out.
Economic buyer: Platform Engineering Leader
Metrics: Target: You retain every single anomalous trace for debugging while cutting your observability spend by 70%. Your dashboards show precise failure data without the noise of a million successful hits.
Competition: Cribl or Datadog ingestion
**Mechanism**: spine-derived-v1
**Competition**: Cribl or Datadog ingestion
**Economic Buyer**: Platform Engineering Leader
**Vocab Fingerprint**: 9b37f8b9abdff1e0

## Startup Token Cold Email

**Genre**: cold-email
**Rendered**: Subject: Intelligent Telemetry Pipeline for site reliability engineers at high-throughput microservice companies

site reliability engineers at high-throughput microservice companies — High-throughput services generate terabytes of redundant HTTP 200 spans that balloon ingestion costs while critical gRPC aborts get sampled out. Instead of paying to ingest millions of healthy traces, Telemetryaborted drops useless spans before they reach your backend — ensuring you only pay for the failures that matter.
**Mechanism**: spine-derived-v1
**Template Id**: spine-cold-email
**Vocab Fingerprint**: 3c9b0037f39a123b

## Startup Token Agent Spec

**Genre**: ai-agent-spec
**Rendered**: Intelligent Telemetry Pipeline. Instead of paying to ingest millions of healthy traces, Telemetryaborted drops useless spans before they reach your backend — ensuring you only pay for the failures that matter. Serves site reliability engineers at high-throughput microservice companies.
**Mechanism**: spine-derived-v1
**Template Id**: spine-ai-agent-spec
**Vocab Fingerprint**: 944ef0322f99683b

## Neighborhood

### Candidate solutions

- [API Integration Drop-Off](/Problems/API_Integration_Drop-Off) — candidate solution for · Problems

### Composed of

- [Ingestion Filtering SDK](/Software/Ingestion_Filtering_SDK) — composes · Software
- [Abort Awareness Engine](/Software/Abort_Awareness_Engine) — composes · Software
- [Anomaly Retention Service](/Services/Anomaly_Retention_Service) — composes · Services
- [Transaction Sieve Agent](/Agents/Transaction_Sieve_Agent) — composes · Agents
- [Span Evaluation Agent](/Agents/Span_Evaluation_Agent) — composes · Agents

### Competitors

- [Datadog](/Competitors/Datadog) — competes with · Competitors
- [Honeycomb](/Competitors/Honeycomb) — competes with · Competitors
- [Static Sampling Configs](/Competitors/Static_Sampling_Configs) — competes with · Competitors
- [Splunk Observability](/Competitors/Splunk_Observability) — competes with · Competitors
- [Cribl](/Competitors/Cribl) — competes with · Competitors

### Embodies

- [Software](/Theses/Software) — embodies · Theses

### What it offers

- [Trace Sieve](/Software/Trace_Sieve) — offers · Software

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