Amp (Sourcegraph) vs Factory AI
Comprehensive side-by-side comparison — features, pricing, performance, and more.
Trust & Reliability
Amp (Sourcegraph)
Too Close to Call
Amp (Sourcegraph) (6.8) vs Factory AI (6.8) — difference of 0.0 points
Scores are AI-estimated from publicly available data — not an independent test or a verified user rating. How we rank →
Amp (Sourcegraph)
6.8
avg score
Factory AI
6.8
avg score
Both tools score very similarly overall — the best choice depends on your specific priorities.
Scores are AI-estimated from publicly available data — not an independent test or a verified user rating. How we rank →
Too close to call — it's a tie
Amp (Sourcegraph)
Factory AI
* Verdict is based on our algorithmic scoring of publicly available data. Learn about our methodology
Amp (Sourcegraph) is best for
Factory AI is best for
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Pros & Cons
Amp (Sourcegraph)Pros
- Provides complete codebase context to AI agents, reducing missed changes and risks
- Enables large-scale code migrations and refactors in hours instead of days
- Offers enterprise-grade security with SOC 2 Type II and ISO 27001 compliance
- Supports both single-tenant cloud and self-hosted deployment options
- Features zero data retention and no model training on user data
Cons
- Requires technical expertise for full utilization of advanced features
- Pricing for enterprise plans starts at $16K, which may be prohibitive for smaller teams
- No explicit free tier for individual users, only enterprise-focused plans
- Relies on external AI models for agent capabilities, not an inherent LLM
- No mention of specific SDKs or client libraries for custom agent development beyond APIs
Factory AIPros
- Accelerates feature development and project delivery significantly (e.g., 7x faster feature delivery, 18x faster project delivery)
- Reduces incident response times and context-switching time (e.g., 40% reduced incident response, 60% reduction in context-switching)
- Offers robust security and compliance features including SOC 2, ISO 42001, GDPR, CCPA, and single-tenant VPC hosting
- Supports a wide range of frontier and open-weight models, including custom BYOK models
- Provides flexible deployment options including cloud and on-premise for data residency and control
Cons
- SOC 2 certification is currently Type I, not Type II
- Requires explicit human approval for agent-proposed commands before execution
- Data retention policy for some Anthropic Mythos-class models is 30 days, requiring admin opt-in
- Pricing for Business and Enterprise tiers requires contacting sales, lacking transparency
- The platform is primarily developer-focused, potentially requiring technical expertise for full utilization
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Dimension Comparison
Amp (Sourcegraph)
6.8
/ 10
Factory AI
6.8
/ 10
Dimension Breakdown
Ease of Use
AIHow intuitive is onboarding, UI navigation, and day-to-day usage for the target audience?
Output Quality
AIHow accurate, reliable, and useful are the outputs this product generates?
Value for Money
AIHow well does the pricing match the features and output quality delivered?
Customization
CalculatedHow much can users tailor workflows, settings, prompts, or outputs to their needs?
Support
AIHow strong is the documentation, customer support, community, and learning resources?
Integration
CalculatedHow well does it connect with other tools, APIs, and workflows?
Accuracy & Reliability
AIFactual accuracy and hallucination resistance
Compliance & Data Protection
CalculatedCompliance certifications and data-protection posture, aggregated from verified compliance signals
Performance
CalculatedLatency + throughput speed
Task Completion
AIEnd-to-end task success rate
Tool Use Correctness
AIPicks the correct tool + correct arguments
Planning Quality
CalculatedMulti-step planning depth + replanning capability
Calculated = derived from structured signals (integration count, API/open-source config, compliance certs, response-time). AI = LLM-assessed from public website content. Methodology
Task Performance
Amp (Sourcegraph)
Task
Factory AI
* Task scores (1–10) are algorithmically generated from publicly available data.