{"id":3679,"date":"2026-03-20T08:13:51","date_gmt":"2026-03-20T08:13:51","guid":{"rendered":"https:\/\/rssfeedtelegrambot.bnaya.co.il\/index.php\/2026\/03\/20\/the-saas-observability-era-is-ending-why-byoc-is-the-future-of-telemetry\/"},"modified":"2026-03-20T08:13:51","modified_gmt":"2026-03-20T08:13:51","slug":"the-saas-observability-era-is-ending-why-byoc-is-the-future-of-telemetry","status":"publish","type":"post","link":"https:\/\/rssfeedtelegrambot.bnaya.co.il\/index.php\/2026\/03\/20\/the-saas-observability-era-is-ending-why-byoc-is-the-future-of-telemetry\/","title":{"rendered":"The SaaS Observability Era is Ending: Why BYOC Is the Future of Telemetry\u00a0"},"content":{"rendered":"
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For years, observability was supposed to be the great equalizer<\/a>. The way every team could understand their systems, debug faster, and ship with confidence. But somewhere along the way, it became the opposite: Complex, expensive, and increasingly constrained.<\/span>\u00a0<\/span><\/p>\n

What was meant to empower developers has become a system governed by egress costs, ingestion pricing, and sampling limits. Teams do not stop observing because they want to. They stop because they are forced to make tradeoffs to stay within budget.<\/span>\u00a0<\/span><\/p>\n

The good news? The pendulum is swinging back. A quiet architectural revolution is already underway. One that puts observability back inside your cloud, under your control. It\u2019s called bring your own cloud (BYOC) and it\u2019s redefining how telemetry is stored, processed, and paid for.<\/span>\u00a0<\/span><\/p>\n

The Problem: Observability Got Too Expensive and Too Centralized<\/span><\/b>\u00a0<\/span><\/h3>\n

In the early days, sending all your telemetry to a SaaS platform felt like a superpower. Datadog, New Relic and Dynatrace turned opaque systems into living dashboards. You could see everything, but only if you could afford it.<\/span>\u00a0<\/span><\/p>\n

In 2026, that model hit a wall.<\/span>\u00a0<\/span><\/p>\n

Modern workloads like Kubernetes clusters, ephemeral pods, serverless functions and agentic AI workflows all generate orders of magnitude more telemetry than monoliths ever did. Observability platforms built on the SaaS ingestion model simply can\u2019t keep up.<\/span>\u00a0<\/span><\/p>\n

Every metric, log, and trace gets shipped out of your cloud and into theirs. You pay for egress, storage, retention and queries on your own data. And when usage spikes, your visibility drops because your CFO asked you to sample only 10% of your traces this quarter.<\/span>\u00a0<\/span><\/p>\n

What used to be \u201cmonitor everything\u201d has quietly turned into \u201cmonitor what you can afford.\u201d<\/span>\u00a0<\/span><\/p>\n

The more data you have, the less you can see.<\/span>\u00a0<\/span><\/p>\n

How We Got Here: The SaaS Plateau<\/span><\/b>\u00a0<\/span><\/h3>\n

SaaS observability was not a bad idea, and it\u2019s still a strong product delivery model. But for data-intensive solutions, SaaS no longer makes sense. It was built for a different era, one where data volumes were manageable.<\/span>\u00a0<\/span><\/p>\n

Centralizing all telemetry made sense when simplicity was the priority. You shipped your data to a vendor. They stored it, indexed it, and showed you clean dashboards. Early on, the cost model felt straightforward.<\/span>\u00a0<\/span><\/p>\n

Over time, the hidden costs emerged in the pricing itself. Logs, traces, and metrics became separate SKUs. Cardinality became a surcharge. Retention became a negotiation. What looked like a single observability platform turned into multiple meters running at once.<\/span>\u00a0<\/span><\/p>\n

As data volumes exploded, teams were forced into artificial tradeoffs. Collect logs or traces, but not both. Drop high-cardinality metrics. Sample aggressively and hope the signal survives. To control spending, many teams split their observability stack, pushing parts of it into open-source tools.<\/span>\u00a0<\/span><\/p>\n

That move introduced another hidden cost. Not just labor and infrastructure, but loss of coherence. Data was no longer unified. Logs lived in one system, traces in another, and metrics somewhere else. Correlation became manual. Context was lost. The system became harder to reason about, even though it was cheaper on paper.<\/span>\u00a0<\/span><\/p>\n

Meanwhile, systems evolved. Kubernetes introduced extreme dynamism.<\/span>\u00a0<\/span><\/p>\n

Microservices created high-cardinality chaos. AI workloads added even more data. This data is not a proxy or an approximation. It is the signal itself: Token usage, accuracy, and outcomes. It cannot be meaningfully sampled, because once you sample it, you no longer know what actually happened.<\/span>\u00a0<\/span><\/p>\n

Observability vendors have not evolved their architectures to address this reality. They evolved their pricing pages. More dashboards, more SKUs, higher costs.<\/span>\u00a0<\/span><\/p>\n

A Shift in Architecture: Bring Your Own Cloud (BYOC)<\/span><\/b>\u00a0<\/span><\/h3>\n

BYOC is not a feature. It\u2019s a re-architecture of the observability model itself. Instead of sending all your telemetry data out to a vendor\u2019s cloud, BYOC lets the observability platform run inside your own environment.<\/span>\u00a0<\/span><\/p>\n

Your data never leaves your VPC. Your compute scales with your workloads. Your costs are predictable.<\/span>\u00a0<\/span><\/p>\n

The vendor doesn\u2019t host your data. They ship you the software that runs where your data already lives.<\/span>\u00a0<\/span><\/p>\n

This model mirrors what we\u2019ve seen in other parts of the cloud ecosystem. Snowflake offers hosted compute and private deployment. Vercel and Cloudflare have shifted workloads closer to the user. AI companies are building on top of VPC-deployed models for compliance and latency.<\/span>\u00a0<\/span><\/p>\n

The pattern is clear: As infrastructure gets more distributed, so does the software that runs it. Observability is next.<\/span>\u00a0<\/span><\/p>\n

BYOC turns observability from a centralized SaaS service into a cloud-native primitive that is deployed, managed, and scaled like any other workload you own.<\/span><\/p>\n

Why BYOC Solves What \u201cData Lakes\u201d and \u201cAI Observability\u201d Don\u2019t<\/span><\/b>\u00a0<\/span><\/h3>\n

The industry\u2019s answer to ballooning data volumes has been twofold:<\/span>\u00a0<\/span><\/p>\n

    \n
  1. Push users to build data lakes.<\/span>\u00a0<\/span><\/li>\n
  2. Add AI on top of dashboards.<\/span>\u00a0<\/span><\/li>\n<\/ol>\n

    Neither solves the real problem.<\/span>\u00a0<\/span><\/p>\n

    Data lakes just move the pain. You still have to ship and normalize data before you can query it. You trade one ingestion pipeline for another, and end up with cold, static data that\u2019s days old before you can analyze it.<\/span>\u00a0<\/span><\/p>\n

    AI-driven insights on top of a broken architecture only amplify noise. You can\u2019t \u201cchat with your observability data\u201d if most of it never leaves your cluster in the first place.<\/span>\u00a0<\/span><\/p>\n

    BYOC cuts through this entirely. There\u2019s no external ingestion, no multi-tenant bottleneck, no delayed indexing. Your telemetry is processed in real time, right where it\u2019s generated.<\/span>\u00a0<\/span><\/p>\n

    It\u2019s the difference between having to ask for your data and simply owning it. With BYOC, observability finally behaves like the workloads it\u2019s meant to monitor: Fast, elastic, cloud-native, and private.<\/span>\u00a0<\/span><\/p>\n

    The Broader Implications: Observability Becomes Infrastructure Again<\/span><\/b>\u00a0<\/span><\/h3>\n

    BYOC is more than an optimization. It\u2019s a philosophical shift.<\/span>\u00a0<\/span><\/p>\n

    In a BYOC world, observability is not a separate SaaS tool. It\u2019s part of your infrastructure as fundamental as your container runtime or your CI\/CD pipeline.<\/span>\u00a0<\/span><\/p>\n

    It aligns naturally with the trends shaping modern engineering:<\/span>\u00a0<\/span><\/p>\n