Observability without compromise
We built Sampleless because the observability market is fundamentally broken. Vendors charge more when you observe more, creating a perverse incentive to throw away the data you need most.
Our mission
The average mid-market enterprise spends $200K–$1M annually on observability tooling and still throws away 90% of their data to control costs. When incidents happen, the evidence needed to diagnose them has already been discarded.
We believe observability should work differently. You shouldn't have to choose between cost control and complete visibility. You shouldn't pay more just because your systems generate more data. And you shouldn't need a finance degree to understand your monitoring bill.
Sampleless exists to solve this. We built a platform that collects 100% of your telemetry, deploys in your own cloud to eliminate egress costs, uses ML to score every entity for anomaly and risk, and charges a flat annual fee, guaranteed to cost less than half your current bill.
What we believe
Full fidelity
We don't believe in sampling. When you throw away 90% of your data, you're throwing away 90% of your ability to understand your systems. We collect everything, every time.
Pricing without games
No per-GB fees. No per-host fees. No per-user fees. No custom metrics traps. No surprise bills. You know exactly what you pay before you sign.
Your data, your control
Your telemetry stays in your cloud. You keep full control over encryption, access, and retention. We never see your actual data, just the query results you request.
Open by default
Built on OpenTelemetry, ClickHouse, Kafka, and the open OpenALBA specification. No proprietary lock-in. Inspect everything, run it anywhere.
Built on OpenALBA
OpenALBA (Open Application-Layer Behavioral Analytics) is an open-source specification for behavioral anomaly detection in distributed systems. It defines how to calculate anomaly and risk scores for any entity (users, services, endpoints, sessions) in a consistent, auditable way.
We built Sampleless on OpenALBA because we believe behavioral analytics should be transparent and reproducible. You should be able to understand exactly why an alert fired, not just trust a black box.
OpenALBA Scoring Formula
Anomaly Score (Objective)
0.40 × Deviation
+ 0.25 × Rarity
+ 0.20 × Velocity
+ 0.15 × Persistence
Risk Score (Contextual)
AnomalyScore
× EntityCriticality
× DataSensitivity
× Environment
× ConsumerWeight
× TimeDecay
Why we built Sampleless
After years of running large-scale distributed systems, we learned a frustrating truth: the observability tools designed to help us understand our systems were creating new problems.
When bills hit $400K+ annually, the pressure to sample became overwhelming. We dropped traces, aggregated metrics prematurely, and filtered logs. Every time we did, we lost visibility. Every time we lost visibility, incidents lasted longer and caused more damage.
The breaking point came when a customer data issue required trace analysis, and we discovered the relevant traces had been sampled away. That's when we decided to build something different.
The insight was simple: if you process data where it's generated (in the customer's cloud), you eliminate the networking costs that make sampling necessary. No egress fees means no reason to throw data away.
We combined this architecture with behavioral analytics (OpenALBA) that actually understands what's anomalous, not just what crosses a threshold. The result is observability that works the way it should have from the beginning.
Ready to see everything?
Join the teams who've discovered that full-fidelity observability and predictable costs aren't mutually exclusive.