Every time a cloud infrastructure bill spikes, SRE teams reach for the same blunt instrument: trace sampling. For a decade, that was a reasonable compromise. You dropped 90% of your head-based traces, shrank log retention from thirty days down to seven, and capped metric cardinality to keep your vendor ingestion bill from outrunning your compute budget. It felt like smart financial hygiene.
It wasn't. It was tech debt dressed up as cost optimization.
When you truncate telemetry, you destroy your ability to diagnose unknown-unknowns—those unpredictable, cascading failures that distributed systems excel at generating. Now, as multi-step AI agents and complex large language model pipelines enter production stacks, that old sampling strategy is completely collapsing.
The Truncation Trap: How Volume Pricing Broke Telemetry
Traditional application performance monitoring platforms built their revenue models on data ingestion. The more gigabytes of logs, metrics, and distributed traces you send to their cloud, the higher your monthly bill. Datadog alone generated more than $3 billion in annual revenue in 2025 by charging per gigabyte, while Dynatrace, Cisco's Splunk business, New Relic, and Grafana Labs built massive platforms around similar ingest volume models.
In microservice and containerized environments, telemetry volume grows non-linearly with user traffic and system complexity. To avoid astronomical bills, engineering organizations defaulted to compromise. They implemented head-based or tail-based trace sampling, dropped fine-grained metric tags, and aggressively pruned log retention windows.
[Traditional Ingestion Model]
Telemetry Source ---> Per-GB Vendor Pipeline ---> Sampled Storage (90% Discarded)
[Host-Aligned BYOC Model]
Telemetry Source (eBPF) ---> Customer Cloud Storage (100% Retained) ---> Managed Control Plane
That compromise worked only as long as failures followed expected patterns. But as systems scale, incident root causes increasingly hide in tail latency spikes and rare edge-case executions. If a trace capturing a failing database query gets dropped by a random 10% sampling filter, your engineers spend hours hunting ghosts during an outage. In modern cloud setups, traditional monitoring tools frequently fail because static alerts cannot account for dynamic architecture shifts.
Multi-Step AI Workloads Blow Up Infrastructure Telemetry
Deploying autonomous AI agents, retrieval-augmented generation (RAG) pipelines, and multi-step inference chains amplifies telemetry volume by orders of magnitude. Monitoring an AI agent isn't just about CPU utilization, RAM pressure, or HTTP response rates.
AI operational telemetry introduces entirely new observability dimensions:
- Prompt and Completion Tokens: Retaining exact input and output token counts per execution step to track cost and context window saturation.
- Model Inference Latency: Segmenting queue time, time-to-first-token, and generation time across heterogeneous inference endpoints.
- Tool Invocation Sequences: Tracing every API call, SQL query, or bash script execution an agent triggers during a task.
- Vector Store Retrievals: Measuring embedding generation latency, top-k retrieval scores, and document re-ranking performance.
- Reasoning Chains: Tracking multi-turn agent decision paths to catch loops or invalid parameter generation before catastrophic execution.
Sampling an AI agent's execution trace is fatal to debugging. AI systems are non-deterministic. If an agent executes five consecutive tool calls and hallucinates a parameter on step four, dropping steps two and three via trace sampling leaves SREs with zero visibility into the context that triggered the hallucination. You can't fix a broken reasoning chain if half the chain was discarded to save three cents on ingestion.
According to research covered by VentureBeat, traditional volume pricing forces companies to silo or discard this rich telemetry context right when AI systems require full operational visibility.
Kernel-Level eBPF and Bring-Your-Own-Cloud Economics
Fixing this telemetry crisis requires changing both the underlying data collection mechanisms and the financial model of observability software.
On the collection side, Extended Berkeley Packet Filter (eBPF) technology has fundamentally altered how telemetry is gathered. Operating directly within the Linux kernel, eBPF sensors capture network traffic, system calls, application spans, and microservice communications automatically. Developers no longer need to spend weeks writing custom manual code instrumentation or updating SDK dependencies across dozens of repositories.
+-------------------------------------------------------------------+
| User Space Applications |
| Microservices AI Agents Vector Databases |
+-------------------------------------------------------------------+
| Linux Kernel Space |
| eBPF Probes ---> System Calls / Network / Socket Telemetry |
+-------------------------------------------------------------------+
On the economic side, Bring-Your-Own-Cloud (BYOC) architecture decouples the telemetry data plane from the vendor's control plane. In a BYOC model, raw telemetry—logs, full traces, and high-cardinality metrics—remains inside the enterprise's own cloud environment (AWS, GCP, or Azure).
Because storage inside native object stores (like AWS S3 or Google Cloud Storage) is orders of magnitude cheaper than SaaS ingestion markups, organizations can retain 100% of their operational data. Pricing shifts from per-gigabyte ingestion fees to predictable per-host infrastructure fees. As observability startup groundcover demonstrated by raising $100 million in a funding round led by One Peak (bringing total funding to $160 million), aligning costs with host counts rather than telemetry volume allows teams to keep full trace fidelity without unpredictable budget spikes. Across 250 paying enterprise customers, keeping the data plane inside customer-controlled infrastructure changes how teams treat operational data.
However, per-host pricing isn't a universal silver bullet. For organizations with lightly utilized fleets spread over thousands of instances, per-host models can be less compelling than dense Kubernetes environments pushing massive log volumes. Engineering leads need to audit telemetry density before assuming BYOC automatically lowers absolute spend.
Telemetry as Context for Autonomous Coding Agents
Observability is undergoing a shift from a post-production dashboard for humans into real-time context infrastructure for autonomous software agents.
Historically, telemetry served one main audience: human engineers sitting in front of dashboards during an on-call rotation. When an alert fired, humans queried logs, correlated traces, identified root causes, and wrote patch code.
In an agentic environment, telemetry becomes a continuous feedback loop. When automated code generators ship updates to staging or production, non-sampled telemetry supplies immediate feedback on system behavior. If an autonomous agent introduces a regression or a latent edge-case failure, high-fidelity traces provide the full execution context back to the AI developer agent to auto-remediate the issue.
As detailed in industry perspectives from IBM and Dynatrace, true observability goes beyond basic metrics, logs, and traces—it demands full topology, metadata, and automated root-cause analysis. As industries adapt, understanding why shipping agents to production breaks traditional deployment patterns becomes essential. When coding agents have access to full operational context rather than sampled fragments, autonomous remediation moves from a risky experiment to a reliable engineering workflow.
Stopping telemetry truncation isn't just about saving money on your APM invoice. It's about preserving the deep operational state required to build, monitor, and trust autonomous software systems.
