Elva vs Local: Features, Pricing & Which Is Better (2026)

A side-by-side comparison of Elva and Local — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.

Elva

Theneo

Freemium

Reads your repositories to discover every API, scores and governs them, then exposes them to developers and AI agents via hosted MCP servers.

Spec-Free API Discovery: Elva scans repository code directly to find endpoints and generates OpenAPI 3.1 as output, so no existing spec is needed to start.
Endpoint Scoring: Every collection is graded on design, developer experience, AI readiness, security and performance, with the weakest collection surfaced first.
AI Fix Pass: A one-click agent writes missing descriptions from code, types response schemas and documents auth, then rescores the collection.
API Contracts: Per-audience contracts pin the exact endpoints and fields a partner, internal team, public developer or MCP client receives, excluding PII and internal fields.
Breaking Change Enforcement: Each commit is diffed against published contracts, showing the schema diff, affected consumers and tools, and blocking publish by policy.
Hosted MCP Servers: Contracts generate MCP servers hosted behind Elva's gateway with OAuth2, scoped keys, per-tool authorization and exportable call logs.
MCP Playground and Agent Feedback: Test the server with a live model, then read the complaints agents file about confusing or failing tools, scored back into the catalog.
Multi-Target Publishing: One approved contract ships as OpenAPI spec, Theneo docs, MCP server, Postman collection and a typed TypeScript SDK in sync.

API Inventory Audit: Discover undocumented or forgotten endpoints across a large codebase and get a ranked list of what to fix first.
Agent Enablement: Expose an internal service to Claude, Cursor or ChatGPT as a governed MCP server instead of hand-writing tool wrappers.
Partner Integration Safety: Publish a restricted contract to an external partner and have Elva block commits that would break their integration.
PII Scoping: Keep customer emails and internal ops annotations out of a public or agent-facing surface while the same endpoints serve them internally.
Zombie Endpoint Retirement: Prove no active consumer references an endpoint before deleting it, using contract and call-log evidence.
Enterprise Security Review: Satisfy SOC 2, ISO 27001 and GDPR questions and wire agent access into an existing SSO and SCIM identity provider.
Documentation Drift Control: Keep docs, SDKs and Postman collections regenerated from code on every merge instead of maintained by hand.

Base Compute

Free

A macOS app that runs chat, coding and meeting AI entirely on your own Mac, with no cloud, no account and no per-token cost.

BaseRT Chip-Tuned Engine: Base Compute's inference runtime compiles for your specific Apple silicon on first launch, claiming up to 5.4x more tokens per second than engines other local apps ship.
Privacy Mode: Every request runs on the Mac and no data leaves the device — memories are stored locally, and facts marked sensitive are pinned to the machine permanently.
In-Folder Agentic Coding: Point Local at a project and it reads, edits and runs code in place without ever uploading the codebase.
On-Device Meeting Transcription: Meetings are transcribed locally with speakers labelled, so recordings and transcripts never reach a third-party service.
Memory You Can Edit: A short, fully visible list of lasting facts about you that you can review, edit or delete, rather than an opaque profile.
Memory-Aware Model Recommendations: Local reads your chip and RAM (8-16 GB, 24 GB, or 32-128 GB tiers) and suggests which open models will actually run well.
Office Mode: Serve the largest model from your fastest machine — Mac Studio, AMD Strix Halo, NVIDIA DGX Spark or an on-prem server — and reach it from Local on every laptop in the office.
Cost and Speed Analytics: A dashboard showing tokens processed, per-model throughput, and the equivalent cloud API cost you avoided.

Confidential Document Review: Drop a contract or PDF into chat and get a summary without the file ever touching a cloud provider.
Regulated-Industry Coding: Work agentically inside a proprietary codebase at a firm whose policy forbids uploading source to external AI services.
Private Meeting Notes: Transcribe internal calls with speaker attribution while keeping the audio on the laptop.
Zero-Marginal-Cost Experimentation: Run heavy prompt iteration without watching a per-token meter, since inference happens on hardware you already own.
Small-Office AI Server: Host a large open model on the one powerful Mac in the office and let the whole team query it from their own laptops.
Offline Fieldwork: Keep a full chat and coding assistant available on a flight or at a site with no reliable connectivity.
Hybrid Frontier Access: Keep everyday work local and connect your own OpenAI or Anthropic key only for the rare job that needs a frontier model.