code-memory vs. Cloud-Based Code Intelligence

February 24, 2026 · View on GitHub

A comparison of local-first vs. cloud-dependent code intelligence tools.

The Fundamental Difference

code-memoryCloud Tools (Sourcegraph, Cody, Cursor)
Data LocationYour machine onlyTheir servers
Network RequiredNo (after initial setup)Yes, always
API Key NeededNoYes
Code Leaves Your MachineNeverYes
Works OfflineYesNo
Air-gapped CompatibleYesNo
TelemetryZeroVaries

Feature Comparison

Core Capabilities

Featurecode-memorySourcegraphCodyCursor
Semantic code search
Symbol definitions
Cross-references
Git history search
Documentation search⚠️ Limited
Multi-language AST

Privacy & Security

Aspectcode-memoryCloud Tools
Code sent to external servers❌ Never✅ Required
API keys to manage❌ None✅ Required
Telemetry/tracking❌ Zero⚠️ Varies
SOC 2 compliance needed❌ No✅ Often required
Data residency concerns❌ None✅ Considerations apply
Works in restricted networks✅ Yes❌ No

Deployment

Aspectcode-memoryCloud Tools
Installationuvx code-memoryAccount + API key
Setup time~1 minuteVaries
InfrastructureNoneTheir cloud or self-hosted
Air-gapped support✅ Yes❌ No
Self-hosted optionN/A (already local)✅ Often available

When to Choose code-memory

Ideal For:

  • Proprietary codebases — Your code never leaves your machine
  • Security-conscious organizations — Zero external data transmission
  • Air-gapped environments — Works in completely isolated networks
  • Offline development — Full functionality without internet
  • Privacy-focused developers — Zero telemetry, zero tracking
  • Quick setup — No accounts, no API keys, no configuration

Consider Cloud Tools If:

  • You need team-wide code search across repositories
  • You want cloud-based AI code generation
  • Your workflow benefits from cloud sync
  • You're comfortable with code being processed externally

Technical Deep Dive

How code-memory Stays Local

  1. Embeddings: Uses sentence-transformers running locally on your CPU/GPU
  2. Vector Search: SQLite with sqlite-vec extension — no external database
  3. Code Parsing: Tree-sitter runs entirely in-process
  4. Git Operations: Local git repository access only
  5. Model Storage: Downloaded once to ~/.cache/huggingface/

Network Activity

code-memory network footprint:
├── Initial setup only (optional):
│   └── Model download (~600MB to local cache)
└── Runtime: ZERO network calls

Compare to cloud tools which require persistent network connections for every operation.

Air-gapped Deployment

code-memory can run in completely isolated environments:

  1. Pre-download the embedding model on a connected machine
  2. Transfer the model cache directory (~/.cache/huggingface/)
  3. Install code-memory via offline pip or standalone binary
  4. Run — no network required

See AIRGAPPED.md for detailed instructions.

Cost Comparison

code-memoryCloud Tools
Monetary costFree (MIT license)Often subscription-based
Compute costYour hardwareTheir infrastructure
Hidden costsNoneAPI usage, overages
Privacy costZeroYour code on their servers

Zero Telemetry Guarantee

code-memory contains no telemetry, no analytics, no tracking code.

This isn't a configuration option — it's architectural. The codebase has:

  • No HTTP clients for analytics
  • No usage tracking
  • No error reporting to external services
  • No "phone home" functionality

You can verify this yourself by examining the source code.

Summary

PriorityRecommended Tool
Privacy & securitycode-memory
Offline/air-gapped workcode-memory
Zero setup frictioncode-memory
Team collaborationCloud tools (Sourcegraph)
Cloud AI featuresCloud tools (Cody, Cursor)

code-memory is the only option that guarantees your code stays on your machine.