Elva vs oMLX: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of Elva and oMLX — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.
Elva
Theneo
Reads your repositories to discover every API, scores and governs them, then exposes them to developers and AI agents via hosted MCP servers.
Key features
- 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.
Best for
- 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.
oMLX
jundot
An open-source LLM inference server for Apple Silicon with continuous batching and tiered KV caching, managed from the macOS menu bar.
Key features
- Tiered KV Caching: Persists past context across a hot in-memory tier and a cold SSD tier, so cached context stays reusable across requests even when the conversation context changes mid-session.
- Continuous Batching: Serves concurrent requests through a batched scheduler rather than one-at-a-time, keeping throughput up when several clients or agent loops hit the server together.
- Menu Bar Management: Controls the server, pinned models, on-demand model swapping and context limits from a native macOS menu bar app with in-app auto-update.
- Native Metal Custom Kernels: Ships precompiled kernels in the official DMG that give large speedups on affected model families — roughly 30x faster fused DSA prefill for GLM 5.2 (845 vs ~29 tok/s measured on an M3 Ultra) with lower memory use.
- OpenAI-Compatible Endpoint: Exposes every discovered model at http://localhost:8000/v1 so existing OpenAI clients, coding agents and SDKs connect without modification.
- Multi-Modality Model Support: Auto-discovers and serves text LLMs, vision-language models, OCR models, embedding models and rerankers from subdirectories of the model directory.
- Admin Dashboard: Provides a web UI at /admin for real-time monitoring, model management, chat, benchmarking and per-model settings in eight languages, with all CDN dependencies vendored for fully offline operation.
- Experimental Multi-Mac Inference: Source builds can split one model across unequal-memory Macs using MLX pipeline ranks over Ring or Thunderbolt RDMA, with a cluster dashboard for peer discovery and SSH/runtime verification.
Best for
- Local Coding Agents: Back Claude Code, OpenCode, Codex or Copilot with an on-device model where cached context makes repeated agent turns fast enough to be usable.
- Private Inference: Keep prompts, code and documents entirely on the Mac with no cloud provider in the path and no per-token billing.
- Serving a Team from One Mac: Run the OpenAI-compatible endpoint on a high-memory Mac so other machines on the network can use larger models than they could host themselves.
- Model Benchmarking: Compare throughput and per-model settings across quantizations and families from the built-in benchmark tools in the admin dashboard.
- Multi-Modal Local Pipelines: Serve embeddings, rerankers and OCR alongside chat models from a single endpoint to build local RAG without extra infrastructure.
- Running Oversized Models: Use experimental cluster mode to split a model that will not fit on one machine across several Apple Silicon Macs.