Tech Stack Advisor

Comparison Matrices

Language Comparison

• Evaluate languages against project-specific criteria: performance requirements, type safety, ecosystem maturity, team proficiency, and hiring pool availability.
• Core dimensions: runtime performance (throughput, latency, memory), developer productivity (compile times, tooling, debugging), concurrency model (threads, async/await, actors), and deployment footprint (binary size, container image size, cold start time).
• Document the comparison in a matrix with weighted scores. Share weights with stakeholders before scoring to avoid bias toward a predetermined conclusion.

Framework Comparison

• Assess frameworks on: learning curve, documentation quality, community activity (GitHub stars are vanity; recent commits and issue response times are substance), upgrade path stability, and production references at comparable scale.
• Test framework candidates with a representative spike (2-3 days). Focus on: how the framework handles the project's hardest problem (not its simplest CRUD operation).
• Avoid framework lock-in by isolating business logic from framework code. Use ports-and-adapters (hexagonal) architecture.

Database Comparison

• Match database type to access patterns: relational for complex joins and transactions, document for flexible schemas and nested data, key-value for high-throughput simple lookups, time-series for metric and event data, graph for relationship-heavy queries.
• Evaluate: consistency model (strong vs. eventual), scaling mechanism (vertical, sharding, replication), operational complexity (managed vs. self-hosted), backup/restore capabilities, and cost at projected data volumes.

Infrastructure Comparison

• Compare cloud providers (AWS, GCP, Azure) and self-hosted options on: service breadth, pricing model, compliance certifications, geographic availability, and existing team expertise.
• For specific services (e.g., managed Kubernetes, serverless functions, managed databases), compare on: feature parity, pricing, SLA guarantees, and migration effort.

TCO Analysis

• Total Cost of Ownership includes all costs over the analysis period (typically 3-5 years):
  • License costs: Per-seat, per-core, per-instance, or usage-based. Include annual increases.
  • Infrastructure costs: Compute, storage, network, CDN, DNS, monitoring, logging.
  • Operational costs: On-call burden (hours x engineer cost), incident frequency, patching cadence, upgrade effort.
  • Training costs: Onboarding new team members, certification programs, conference attendance.
  • Opportunity cost: Time spent on operational work instead of feature development.
• Present TCO as a range (optimistic, expected, pessimistic), not a single number. State assumptions explicitly.

Team Capability Assessment

• Audit the current team's skills against the proposed stack. Use a skill matrix: list each technology and rate team members on a scale (learning, proficient, expert).
• Identify skill gaps. For each gap, evaluate: can we train existing members (timeline and cost), do we need to hire (availability and compensation), or should we choose a more familiar technology?
• Consider the bus factor. If only one person knows a critical technology, that is a risk regardless of how good the technology is.
• Factor in team growth plans. A technology that is easy to hire for (JavaScript, Python, Go) may be preferable to a technically superior but niche option (Erlang, Haskell).

Ecosystem Maturity

Community Health

• Measure by: number of active contributors (not total), issue/PR response time, release cadence, and presence of a governance model or sponsoring organization.
• Check for corporate backing or foundation support. Projects with a single maintainer are a risk for production-critical dependencies.

Library Ecosystem

• Evaluate the availability and quality of libraries for common needs: HTTP clients, ORMs, authentication, logging, testing, and monitoring.
• Prefer ecosystems where essential libraries are stable and well-maintained over ecosystems where you must build everything from scratch.

Hiring Pool

• Check job posting volumes and candidate availability on major platforms for the technology.
• Consider geographic constraints. Some technologies have strong communities in specific regions.
• Factor in compensation expectations. Niche technology specialists often command premium salaries.

Migration Paths

• Before adopting a technology, plan the exit. What does it take to migrate away if the choice proves wrong?
• Evaluate migration cost along three dimensions: data (can you export?), code (how coupled is your logic to the framework/platform?), and operations (can you run both systems in parallel during migration?).
• Prefer technologies with open standards and data portability (e.g., PostgreSQL over a proprietary database, Kubernetes over a vendor-specific orchestrator).

Build vs. Buy Decisions

• Build when: the capability is a core differentiator, off-the-shelf products do not fit your requirements without heavy customization, or the buy option creates unacceptable vendor dependency.
• Buy when: the capability is commodity (authentication, email delivery, payment processing), the vendor has a strong track record, and the integration cost is lower than the build and maintenance cost.
• Quantify both options over 3 years: build cost (dev hours x rate + ongoing maintenance) vs. buy cost (license + integration + operational overhead).
• Consider the hidden costs of building: security patching, compliance certification, documentation, and on-call support for a homegrown system.

Vendor Lock-in Assessment

• Categorize lock-in risk by layer: infrastructure (cloud provider), platform (managed services), application (proprietary APIs), and data (proprietary formats).
• For each layer, identify the switching cost: code changes, data migration effort, retraining, and downtime.
• Mitigate lock-in with abstraction layers, but only where the abstraction does not sacrifice the primary benefit of the vendor service.
• Accept lock-in deliberately when the vendor's capability significantly exceeds what you can build or operate, and document the decision in an ADR.