We are Jason, Todd and LV - we're building Taloflow (https://www.taloflow.ai) to help developers find and evaluate cloud and API products based on their use case, requirements, and budget.
Have you ever wondered whether you should move a particular workload off of AWS to GCP? Or which database to build on? Or which APM is best for your size, architecture, and budget?
A little over a year ago, we launched a cloud management tool for AWS. A few months after launch, we learned that many of the dev teams we were working with were actively looking for alternative cloud products and dev tools to improve their stack. They generally found the investigation and testing process to be error-prone and a time suck (finding what fits the use case requirements, reading docs, sitting on sales calls, projecting costs in complex spreadsheets, etc.).
Similarly, while building our own product, we implemented several cloud and dev tools into our own stack. Some worked, and some were terrible. Time after time, we were fooled by the marketing language, lacked use case-based information to inform our decision, and generally found third-party review sites not as helpful when it comes to buying a cloud or dev tool. Why? Comparing options with info relevant to our specific use case was the missing piece, and there were several dozen dimensions to consider.
We built two ways to help you pinpoint which products best fit your use case.
The first walks you through a "Quick match" questionnaire that is designed with the help of domain experts for particular categories (e.g.: object storage, data pipelines, APM, etc.) to help you define your requirements and then rank options within a product category based on the inputs.
For example, in object storage, we collect use case info (Do you provide information to customers frequently and quickly? Do you perform intensive ML tasks? Do you work with large graphical or video objects?, etc.), integration info (CDN, Redshift/BigQuery/ or other Data Abstraction Tools), budget, compliance, etc. to build a holistic use case profile to help filter and rank products.
We then scour our database of developer docs, pricing pages, private pricing, reviews from experts, and internal tests for various types of use cases to find the best use case fit.
The second way we solve this is by actually ingesting telemetry from various cloud applications (AWS, GCP, Azure, Twilio, Datadog, etc.) and providing a cost-benefit analysis on the performance and cost trade-offs of implementing various products. We can answer questions like "What are the egress and hidden fees of moving from Azure Blob to Backblaze B2, Storj or Wasabi?" or "What's the most performant APM for my architecture?", etc.
Weβre currently experimenting with pricing, but we make money by selling more advanced or ongoing analysis to our members, up-selling our SaaS solutions for cloud infra and dev tool management, and occasionally collecting a standardized referral fee from vendors who happen to be good matches for you.
We're currently live for Object Storage and will soon launch for AI/ML tools, CPaaS (e.g.: Twilio, MessageBird, etc.), CI/CD tools (e.g.: Jenkins, Buildkite), and APM (e.g.: DataDog). If you don't mind getting ad-hoc analysis for categories we don't fully support yet, we have an option for that too. We'd love your feedback on our product and want to hear from you what product categories you need help researching.
by Andrey
Azimov