Investment Thesis

The Observability Market is Broken

$36B spent on tools that still require engineers to manually search logs. ungrep AI replaces the search bar with a conversation — at 1/3 the cost of incumbents.

$36B

Total Addressable Market

Observability market growing 12% CAGR. AI-native tools are capturing disproportionate share from legacy incumbents like Splunk and Datadog.

90%

Gross Margin Target

Embedding-based architecture is 10x cheaper to run than traditional full-text indexing. AI compute costs dropping 40% per year.

$0

Customer Acquisition Cost

White-label distribution through hosting providers gives us access to millions of servers without a direct sales team.

Why Now?

Three converging trends create a once-in-a-decade opportunity in observability:

The Thesis

  1. LLMs crossed the quality threshold. For the first time, AI can reason about log data with enough accuracy to be genuinely useful in production debugging. This wasn't possible 18 months ago.
  2. Embedding costs collapsed. Vector embedding and storage costs dropped 15x in the last two years. What was research-only is now commercially viable at scale.
  3. Incumbents are stuck. Datadog, Splunk, and Elastic built their architectures around full-text indexing. They can't pivot to embeddings without rewriting their core — that's a 3-5 year disadvantage.
  4. Hosting providers need differentiation. The hosting market is commoditized. AI-powered log analysis is a premium feature they can't build themselves — giving us a zero-CAC distribution channel.

Market Sizing

TAM $36B — Global Observability

All spending on log management, APM, and infrastructure monitoring. Growing 12% CAGR driven by cloud migration and microservices complexity.

SAM $8.4B — Log Management

Companies spending specifically on log ingestion, indexing, search, and analysis. Our direct competitive space.

SOM $420M — Hosting Channel

Revenue opportunity from white-label distribution through the top 200 global hosting providers, at 20% attach rate and $10/server/month ARPU.

Unit Economics

Built for capital efficiency from day one.

Revenue per Server
$10/mo
Blended ARPU across direct and channel
Gross Margin
~90%
Embedding inference at $0.80/server/month
Net Revenue Retention
160%
More services → more logs → more value
Payback Period
<3 mo
Channel deals have zero direct acquisition cost
Hosting Partner LOIs
3
Top-50 providers, 800K+ managed servers combined
Cost vs. Datadog
3x cheaper
Vector search eliminates expensive indexing

Defensible Moats

  • Custom embedding model: Fine-tuned on 50B+ log tokens. Accuracy improves with every customer — a data flywheel competitors can't replicate without matching our log corpus.
  • Hosting channel lock-in: White-label integrations are deeply embedded in partner control panels. Switching costs are high once deployed — partners won't rip out a revenue-generating feature.
  • Architecture advantage: Purpose-built for embedding-based log search. Incumbents are architected around full-text indexing and would need a ground-up rewrite to compete.
  • On-premise capability: Most AI log tools require cloud. Our architecture runs entirely on-premise — critical for GDPR compliance and the EU hosting market.

Go-to-Market Strategy

We go to market through two channels, sequenced for capital efficiency:

Phase 1: Hosting Channel (Now)

Sign 20 founding hosting partners who distribute ungrep white-label to their customers. Zero CAC, revenue-share model. Target: 50K servers by end of Year 1.

Phase 2: Direct Sales (Year 2)

Launch direct product for enterprise teams. Self-serve SaaS for mid-market, sales-led motion for enterprise. Use hosting channel data to build the best product before going direct.

Phase 3: Platform (Year 3)

Open the embedding layer as a platform. Third-party developers build custom queries, alerts, and automations on top of ungrep's semantic index.

The Ask

We're raising a seed round to accelerate partner onboarding and scale our embedding infrastructure.

Round
Seed
Runway Target
24 mo
40% Engineering
Infra
Scale embedding infrastructure, build self-serve SaaS product
30% Partnerships
Growth
Onboard 20 founding hosting partners, build integration toolkit
20% AI/ML
Models
Fine-tune embedding models, improve query accuracy
10% Operations
Ops
Legal, compliance (SOC2), and administration

Let's talk

We're selective about our investor partners. If our thesis resonates, we'd love to have a conversation.

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