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

A side-by-side comparison of Elva and Zero — 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.

Vercel Labs

Free

An experimental graph-first programming language where agents edit a compiler-checked program graph instead of raw source text.

Graph as the Program: A compiler-owned semantic graph of symbols, calls, types, effects and node IDs is the source of truth, so agents reason over program structure rather than parsing and regenerating text.
Hash-Guarded Patches: Every edit carries an expected graph hash and expected field values, so a stale or conflicting patch is rejected before it reaches the store instead of silently corrupting the program.
Compiler in the Loop: Shape, type, stale-state and repository metadata checks run as part of applying a patch, collapsing the write-build-test-inspect cycle into a single checked operation.
Readable Text Projections: The graph renders to reviewable .0 source projections so humans can read diffs, audit what an agent changed and make rare manual edits.
Structured JSON Diagnostics: The compiler emits machine-readable diagnostics rather than prose error text, so agents can act on failures without parsing terminal output.
Explicit Effects via World: Side effects are passed through an explicit World capability parameter, making what a function can touch visible in its signature.
Runtime Constraints by Design: Targets token efficiency, low memory, fast startup, fast builds, low latency and zero dependencies rather than relaxing systems goals for agent ergonomics.
Query and Patch CLI: zero init, zero query, zero patch and zero run give agents a direct command surface over the graph, with agent skills carrying the graph discipline instead of rigid human prompts.

Reliable Agent Code Edits: Let a coding agent make semantic changes that are rejected outright if its view of the program is stale, instead of producing plausible-looking but broken text diffs.
Reducing Agent Token Spend: Query the specific symbols, types and nodes relevant to a task rather than feeding whole files into context on every turn.
Outcome-Driven Development: Describe a desired result in conversation — add auth, fix a failing route, build a CRM API — and review the resulting projection rather than writing the code.
Auditable AI-Written Code: Review what changed through readable .0 projections and graph hashes, keeping a human checkpoint over agent-authored programs.
Language and Tooling Research: Explore what a compiler and program representation look like when machine editors, not human typists, are the primary writers.
Sandboxed Experimentation: Prototype agent-driven codebases in an isolated environment where breaking changes and pre-1.0 churn are acceptable.