Question 1

Why evaluate Sift if our stack already works?

Accepted Answer

A working stack can still take more engineering work as data, teams, and review requirements grow. Owning the platform means committing to its roadmap, upgrades, and daily operation for as long as you run it. That can make sense when the platform itself is a strategic capability and you have a team committed to owning it. An evaluation helps you weigh that commitment against the hardware work it competes with, and shows what Sift could take on and what moving would involve.

Question 2

What does Sift manage, and what stays with us?

Accepted Answer

Sift develops and maintains the platform software. Your team keeps ownership of hardware decisions, engineering criteria, and the quality of your source data. Sift runs as a managed cloud service, in GovCloud, in a private VPC, on-premises, or air-gapped. Sift develops and maintains the platform software in every model, while hosting, upgrades, and daily operations are split differently in each, so confirm the split for yours.

Question 3

How should we compare the full cost?

Accepted Answer

Compare the same workload over the same period. Count each engineering hour once, and keep salaried time separate from cash savings. Cash expenditure: Cloud and infrastructure for an internal stack; Third-party tools, licenses, or contractors; Sift subscription; Implementation and migration costs. Engineering capacity: Internal platform development; Maintenance, upgrades, and support; Migration and integration work; Operating work retained on either path. Schedule impact: Time before a workflow is useful; Growth in data, programs, and users; Review work that waits on tooling.

Question 4

Can we keep our existing tools and scripts?

Accepted Answer

Yes, through Sift’s supported clients and integrations. The Python client returns data as DataFrames in your notebooks, and the Grafana plugin connects existing dashboards. Other tools and scripts may need configuration or migration, so confirm them against your stack during the evaluation.

Question 5

Can we try Sift alongside our current stack?

Accepted Answer

Yes. Start with one representative workload in Sift while your current stack keeps running. Agree on success criteria up front, such as time to find an event, run comparison, check setup, integration effort, and data access. Then compare results and assess the transition effort before you expand.

Question 6

Can we access and export our data?

Accepted Answer

Yes. Your team can query data through Sift’s REST and gRPC APIs and the official Python, Go, and Rust clients. You can export telemetry to files, such as CSV, Parquet, or WinPlot, from the Sift app or the Sift CLI, or run export jobs through the API. Confirm the retention, access controls, and any other data your program needs to move during the evaluation.

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