Reliability that depends on heroics and manual firefighting doesn't scale — it burns people out and still drops uptime. We apply engineering to operations — automated scaling, self-healing infrastructure, observability, error budgets — so the system stays up on its own.
uptime vs. SLO
MTTR
on-call pages
Reliability as an engineered budget
SLO target
99.95%
measured
99.98%
Uptime against SLO
Mean time to recovery
Alert noise
On-call pages
Why SRE
Heroic firefighting doesn't scale and burns out your best people. SRE makes uptime a measured, automated property of the system itself.
We define SLIs and SLOs from real user journeys, so uptime becomes a tracked target the whole team agrees on — not a vibe.
Common faults are detected and recovered automatically — failed nodes replaced, traffic rerouted — so the system stays up without a 3am page.
Observability, runbooks, and blameless post-mortems drive MTTR down and stop the same incident from recurring twice.
We tune alerting to user-impacting signals and kill the noise, so on-call responds to real problems instead of drowning in pages.
What you walk away with
SRE only sticks if it's built into how the system runs. You get the targets, the automation, and the practices that keep it reliable after we leave.
From SLOs to chaos tests
We map user journeys to SLIs, set SLO targets and an error-budget policy, and audit current reliability and alert noise.
Tracing, metrics, and centralized logging go in so failures are visible and debuggable in minutes, not hours.
Self-healing and autoscaling absorb common faults automatically, removing the manual toil that burns teams out.
Controlled failure injection proves the system recovers gracefully, then runbooks and post-mortem practice transfer to your team.
SRE, in production
A video-SaaS platform was paging engineers nightly and still missing its uptime promises. We introduced SLOs, observability, and self-healing automation.
Streamline Video
Video SaaS · USA
Uptime (was 99.5%)
MTTR
Alert noise
On-call pages
“Our on-call rotation was a burnout machine — pages all night and we still missed our uptime targets. pyronix gave us real SLOs and self-healing infra; the system recovers itself now, and the team finally sleeps.”
Straight answers
Site reliability engineering (SRE) applies software-engineering practices to operations to make systems more reliable, scalable, and efficient. Instead of relying on manual firefighting, SRE defines measurable reliability targets (SLOs), automates infrastructure and recovery, and uses error budgets to balance shipping speed against stability.
DevOps is a culture and set of practices for accelerating software delivery and automating infrastructure. SRE is a specific, measurable implementation focused on reliability — it adds SLIs, SLOs, error budgets, and self-healing systems on top of DevOps foundations. Put simply, DevOps asks 'how do we ship faster' and SRE asks 'how do we stay reliable while we do.'
We analyze your real user journeys to identify the metrics that reflect user happiness — Service Level Indicators like API success rate and latency — then set Service Level Objectives (targets such as 99.95% success) that match what users actually need. SLOs are deliberately not 100%; the gap becomes your error budget for safely shipping change.
An error budget is the small amount of unreliability your SLO permits — for a 99.95% target, that's the remaining 0.05%. It turns reliability into a shared, quantified decision: while budget remains, teams ship freely; when it's spent, the focus shifts to stability. It ends the endless 'features vs. reliability' argument with data.
Yes. We run controlled failure injections — terminating nodes, adding latency, killing dependencies — to verify the system fails over gracefully, and we build self-healing automation that detects and recovers from common faults without paging a human. Reliability you've tested under failure is the only kind you can trust.
Tell us your reliability targets and where the pages come from. We'll set SLOs, build the observability and self-healing, and prove it under chaos testing.
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