Introduction
Engineering teams in 2025 are battling two things at once: runaway telemetry costs and rising reliability expectations. The winning stacks are open-standard, cost-aware, AI-assisted, and workflow-integrated without locking you into one vendor’s format. Independent surveys show cloud-native adoption at record highs and a strong shift toward consolidated observability platforms over fragmented tools.
1. OpenTelemetry-first ingestion & processing
Why it matters: Open standards reduce risk and keep you flexible.
What to look for: Native OTel receivers, Collector-compatible processors, head/tail sampling, transform/redaction at ingest, multi-destination export. OpenTelemetry
Parseable fit: OTel HTTP 4318 ingest out of the box, schema-on-write to a columnar store, SQL-first analysis, and multi-dataset routing without the requirement of any proprietary agent.
2. Cost-aware telemetry pipelines
Why it matters: Telemetry growth is compounding; FinOps priorities now put waste reduction and commitment management at the top.
What to look for: Tail sampling for “keep the signal, drop the noise,” cardinality guardrails, compression, tiered/object storage, and spend dashboards. FinOps Foundation
Parseable fit: Designed for fast observability on S3-class object storage, plus dataset-level retention, partitioning, and downsampling, so you can scale without surprise bills.
3. AI-assisted detection and root cause (explainable)
Why it matters: Humans need help separating real incidents from chatter.
What to look for: Unsupervised anomaly detection, causal/topology-aware RCA, natural-language (NL) summaries you can share in tickets. See vendor exemplars like Datadog Watchdog and Dynatrace Davis for the shape of these capabilities. Read more
Parseable fit: AI summarization on datasets and Natural Language assist in the SQL editor help teams jump from “alert” to “explanation” faster.
4. High-cardinality, high-dimensional analysis
Why it matters: Modern systems demand slicing by user, build, region, feature flag, cohort—you can’t downsample your way out forever.
What to look for: Columnar or “wide-event” storage that makes group-bys fast and affordable; flexible indexing and late materialization. (Read more on Observability 2.0 and wide events.) Read more
Parseable fit: Wide events stored columnar; SQL-first queries handle gnarly group-bys across logs/metrics/traces without pre-baking dashboards.
5. Continuous profiling & eBPF visibility
Why it matters: Metrics tell you something’s slow; profiles tell you where. eBPF auto-telemetry lights up Kubernetes traffic and syscall paths with low overhead.
What to look for: Always-on CPU/memory profiles, flamegraphs, code-diffs over time, and eBPF-powered service maps. (See Uber’s journey to always-on profiling; explore eBPF via Cilium/Hubble.) Read more
Parseable fit: Pull in profiles and eBPF events alongside app telemetry so code-hotspots and network paths sit in the same investigative loop.
6. First-class SLOs & error budgets
Why it matters: Leadership buys outcomes. Teams need burn-rate alerts, SLO reports, and clean hand-offs into incidents/postmortems.
What to look for: SLO objects with time windows, multi-signal burn alerts, and joinable context (deploys, flags, tickets). (Market data shows continuing consolidation toward platforms that connect these dots.) Read more
Parseable fit: Use SQL to define error budget math over logs/metrics-like series and push alerts; workflows jump straight into investigation.
7. Governance, data privacy & redaction
Why it matters: Telemetry often carries secrets and PII; EU DORA is applicable and auditors will look at logging controls.
What to look for: Ingest time scrubbing, field-level redaction, RBAC/ABAC, customer-managed keys, immutable audit trails, aligned with OWASP guidance. Read more
Parseable fit: Redaction transforms in the pipeline; dataset-level RBAC; object-store encryption and lifecycle policies for compliance hygiene.
8. Regulatory readiness (EU DORA & EU Data Act)
Financial services already feel the DORA clock (applicable Jan 17, 2025). The EU Data Act is applicable from Sept 12, 2025 with staged obligations through 2026–2027—procurement increasingly asks how observability data is stored, moved, and exported. Read more
Parseable fit: Customer controlled storage regions, export friendly formats, and transparent data paths help satisfy residency and switching requirements.
9. Integrated workflows (detect → investigate → remediate)
Why it matters: Mean time to context kills incidents.
What to look for: One click pivots from an alert to the right log/trace slice, captured filters carried into the explorer, and shortcuts to rollbacks or feature-flag toggles. (Analyst and vendor materials depict this as the core promise of consolidated platforms.) Read more
Parseable fit: Proactive dashboards drill from charts to filtered logs/traces; create alerts from the same query; save charts straight from the SQL editor into shared dashboards for the team.
10. Real user signals & synthetic checks
Why it matters: The business impact is at the edge; stitching RUM and synthetics to backend traces gives end-to-end clarity.
What to look for: First-class RUM, session replay with privacy controls, and synthetics connected to service traces. Representative implementations in leading suites inform buyer expectations.
Parseable fit: Join front-door signals with backend telemetry in one queryable store; summarize impact with AI for faster triage.
Why teams pick Parseable as the go-to platform
- Zero-stack, SQL-first: ingest OpenTelemetry (4318), store in a high-performance columnar engine on S3-class object storage, and query with standard SQL.
- Proactive dashboards & workflows: export charts directly from the SQL editor to dashboards; one click to create alerts; investigate with filters preserved end-to-end.
- AI built-in: dataset summaries, NL query assist, and forecast friendly time series, designed for capacity planning and quieter alerts.
- Governance-ready: redaction at ingest, dataset RBAC, encryption, and residency friendly storage layouts that align with DORA/Data-Act era procurement.
If your current stack can’t do the above without bolt-ons and custom glue, you’re paying the “integration tax” twice, once in license, again in latency.
FAQs
Q1: Do I need OpenTelemetry if my vendor has their own agent? Yes. OTel keeps you portable and unlocks powerful pipeline controls (sampling, transforms, multi-export) without re-instrumenting later.
Q2: Does tail sampling drop important data? Used well, it preserves the right data—errors, long requests, specific endpoints, while shedding routine noise. For large systems, it’s the recommended strategy.
Q3: Are AI features just “alerting with ML”? No. Mature platforms use anomaly detection plus causal/topology context to produce explainable root-cause hints and NL summaries you can act on.
Q4: What about compliance in Europe (DORA, EU Data Act)? DORA applies from Jan 17, 2025; the EU Data Act is applicable Sept 12, 2025 with staged obligations. Expect procurement to ask about residency, switching, exportability, and auditability.
Q5: Why not just add more dashboards? Dashboards are great for known questions. But 2025 stacks lean on wide events + ad-hoc queries + AI summaries to answer new, high-cardinality questions fast.
Q6: Where does Parseable fit if we already have metrics elsewhere? Keep your metrics as is—use Parseable to centralize logs/traces/profiles/events on object storage, query with SQL, then export or link out. It plays well in hybrid setups.
Q7: How does Parseable cut costs? Object-storage economics, compression, dataset retention tiers, and sampling/filters at ingest—plus fewer hops thanks to SQL-first workflows.
Conclusion
The fastest path to better reliability at lower cost is getting your platform vetted against the features above. Standards + pipelines + AI + code-level signals all in one coherent workflow—that's exactly how Parseable is built. If you're evaluating platforms this quarter, use this feature checklist in your trials and watch the gaps reveal themselves.
