AI Tool Comparison
Comparing as AI Code Generation & AutocompleteGoogle Gemini API vs Factory
Google Gemini API
VS
Factory
Verdict by Category
Detailed Comparison
Feature
Google Gemini API
Factory
Pricing
FreemiumThe Gemini API uses a three-tier structure. Free is for developers and small projects, offering limited access to select models with free input and output tokens, Google AI Studio access, and no billing account required, though content is used to improve Google's products. Paid unlocks higher rate limits for production, context caching, the Batch API (roughly 50% cost reduction), access to Google's most advanced models, and a guarantee that content is not used to improve Google's products. Pricing is billed per million tokens and varies by model: for example, Gemini 3.1 Pro Preview costs $2.00 input and $12.00 output per million tokens for prompts under 200K tokens, while cost-efficient options like Gemini 3.5 Flash-Lite start as low as $0.30 input and $2.50 output per million tokens, with additional Flex and Priority billing modes available for different latency and cost tradeoffs. Enterprise is for large-scale deployments through the Gemini Enterprise Agent Platform, adding dedicated support channels, advanced security and compliance certifications (HIPAA, SOC 2, FedRAMP), provisioned throughput, volume-based discounts, and MLOps tooling, available by contacting Google's sales team.
FreemiumFactory offers five tiers. Pro is $20/month for individuals, including desktop, CLI, and SDK access, cloud and local background agents, and a billing/usage dashboard. Plus is $100/month with roughly 5x the usage of Pro plus Droid Computers, Factory-managed cloud computers for remote Droids. Max is $200/month with roughly 10x the usage of Pro and early access to new features. Business is custom-priced for growing teams up to 150 seats, adding custom usage limits, dedicated onboarding, SSO, SAML/SCIM provisioning, Zero Data Retention, audit logging, and basic admin controls. Enterprise is custom-priced with unlimited team members, dedicated compute with a partitioned inference pool, an Agent-readiness Improvement Program, on-premise deployment, sub-organizations, full admin controls, customer-managed encryption keys, data residency, and a dedicated account manager with SLA-backed support.
Categories
AI Developer APIs & PlatformsAI Coding Assistants
AI Coding Assistants
Summary
Build with Google's multimodal Gemini models via API and AI Studio
Autonomous Droid agents that build, test, and ship software
Google Gemini API Pros & Cons
Pros
- Genuinely native multimodal models covering text, image, video, and audio in one API
- Google AI Studio offers a real, usable free prototyping environment with no billing account required
- Google Search and Google Maps grounding help reduce hallucinations with live information
- Batch API and Flex pricing modes offer substantial cost savings for non-latency-sensitive workloads
- Clear upgrade path from free prototyping to enterprise-grade deployment via the Gemini Enterprise Agent Platform
Cons
- Pricing structure is complex, with per-model, per-mode (Standard/Batch/Flex/Priority) rates that require careful reading to estimate real costs
- Free tier usage is used to improve Google's products, so privacy-sensitive projects need to upgrade to the Paid tier for that guarantee to apply
- Frequent model churn (previews, deprecations, shutdown dates) means integrations need occasional migration work to stay current
- Full enterprise-grade features like fine-tuning, VPC Service Controls, and CMEK live on the separate Gemini Enterprise Agent Platform, not the Developer API itself
- Advanced capabilities like Computer Use and some agent tooling remain in preview with more restrictive rate limits
Factory Pros & Cons
Pros
- Droids execute full tasks (editing files, running commands, opening PRs) rather than just suggesting code
- Genuinely model-agnostic and interface-agnostic, avoiding lock-in to one IDE or LLM provider
- #1 ranking on Terminal Bench, a widely used industry benchmark for coding agents
- Sovereign deployment options including on-premise and air-gapped environments for regulated industries
- Strong enterprise traction with named customers like Nvidia, Adobe, EY, and Morgan Stanley
Cons
- Best suited to teams with a real backlog of well-specified work and enough review capacity to absorb the resulting pull requests
- Not ideal for solo developers wanting lightweight autocomplete, or teams whose work is mostly ambiguous product design
- Business and Enterprise pricing is fully custom, requiring a sales conversation rather than transparent self-serve rates
- Heavy multi-agent or long-context usage can run up consumption costs quickly on usage-based components
- As a younger platform (founded 2023), its track record is shorter than more established coding agent competitors