Conifer

Conifer (YC S26) builds Sage, a local-first AI runtime and workspace that runs models entirely on your own machine. Its inference engine is written from scratch in Rust, reaching up to 60% faster decode than llama.cpp on Apple Silicon, and pairs an editor, integrated terminal, file viewers, and an agent runtime inside a kernel-level sandbox so tool calls only touch what you allow. Conifer routes each request to the cheapest capable model, starting with free on-device inference and falling back to cloud only when needed, cutting AI spend by up to 80%. The desktop app ships as Juniper for macOS, Windows, and Linux with no accounts, no telemetry, and no cloud dependency. Conifer is proprietary and distributed as compiled binaries; it currently exposes no public web API.

Conifer is profiled on the APIs.io network. Tagged areas include Company, Artificial Intelligence, Local AI, Inference, and LLM.

Conifer’s developer surface includes pricing, changelog, and 12 more developer resources.

🌐 Visit website 📡 Profile data on GitHub

15.2/100 emerging ▬ flat Agent 0/100 human only Front door AI 4/6 moderate Heartbeat 100/100 alive library Full breakdown ↓
scored 2026-11-06 · rubric v0.27.0 · Show this score on your site →

AccessFreemiumOpen⚡ Free to try

0 APIs

Company Artificial Intelligence Local AI Inference LLM Developer Tools Model Routing Desktop Application Privacy Rust

Kin Score

Kin Score How this is scored →
scored 2026-11-06 · rubric v0.27.0

Composite quality — 15.2/100 · emerging

Contract Quality 0.0 / 21

Developer Ergonomics 0.0 / 17

Access Clarity 6.3 / 17

Operational Transparency 3.2 / 11

Contract Governance 0.0 / 10

Discoverability 4.7 / 9

Regulatory Posture 0.8 / 15

Agent readiness — 0/100 · human only

Machine-Readable Contract 0 / 18

Agentic Access Contract 0 / 10

Documented Reversibility 0 / 6

MCP Server 0 / 12

Machine-Readable Auth 0 / 10

Idempotency 0 / 9

Stable Error Semantics 0 / 8

Request/Response Examples 0 / 7

Rate-Limit Signaling 0 / 7

Typed Event Surface 0 / 6

Agent Skills 0 / 5

Well-Known Catalog 0 / 4

Consent & Bot Identity 0 / 3

A2A Agent Card 0 / 8

Dry-Run / Simulate Mode 0 / 4

Delegated User Identity 0 / 6

Protected Resource Metadata 0 / 5

Registration Without a Human 0 / 6

Agentic Commerce Well-Known Document 0 / 5

Pagination 5 / 5

Distinct Operations 3 / 3

MCP Tool Quality 6 / 6

Regulatory Posture applies to this provider. Its tags matched the Horizontal (data, software, accessibility, platform) regime, so Regulatory Posture carries 15 points of the composite. If this regime is wrong for your business, say so on your provider repo — the applicability map is public and we will correct it.

Create-or-Update Ergonomics could not be measured. We hold no machine-readable contract for this provider to read, so there is nothing to measure a write surface against. Excluded rather than scored zero: never-measured and measured-empty are different facts. Publishing an OpenAPI is what makes this facet — and several others — scorable at all.

The six quality facets above are damped to 85 points between them, because the conditional facet above carries the other 15. That is why each facet's contribution is shown against a damped maximum: raising a quality facet moves the composite by 85% of its nominal weight, not 100%. The full arithmetic is at apis.io/rating/.

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Agent readiness — checks not met, largest first (exact, from this profile):

Machine-Readable Contract +18 pts — api_specs includes an OpenAPI document
MCP Server +12 pts — deployment.mode + probe verdict on the mcp/ manifest, not the pointer; an unprobed slug falls back to authorship
Agentic Access Contract +10 pts — agentic_access artifact present (x-agentic-access), graded by author
Machine-Readable Auth +10 pts — a served openid-configuration/oauth-authorization-server carrying `issuer` (served), else the securitySchemes class in the provider OpenAPIs — bound (mutualTLS, DPoP, Signature) > negotiable (oauth2 authorizationCode/clientCredentials, openIdConnect) > bearer (http bearer, apiKey)
Idempotency +9 pts — OpenAPI mutating operations declare an idempotency key — a header, a request-body property, or a named de-duplication token whose own description says so (verified); else common[].type matches /idempoten/i (documented)
Stable Error Semantics +8 pts — OpenAPI $refs a shared error schema across 4xx/5xx responses (verified), else common[].type includes ErrorCatalog / ErrorCodes / DeclineCodes

13 more unmet checks in the Agent Readiness table above.

Kin Score — facets with the most composite points still available (weight × headroom):

Contract Quality scores 0.0 — up to +25.0 composite points
Developer Ergonomics scores 0.0 — up to +20.0 composite points
Access Clarity scores 36.8 — up to +12.6 composite points

Each check links to what satisfies it. Publish the artifact, or tell us where it already lives, and the score moves on the next scoring run.

Improve this rating by publishing the missing artifacts — every area above can be raised, and the full rubric is at apis.io/rating/. Every facet and dimension name above is a link: it opens that measurement's own page — what it means, the exact checks that feed it, how the whole catalog distributes on it, and the providers at the top of it. This rating is computed from the provider profile at artifacts.apis.io/conifer/apis.yml: open an issue on github.com/api-evangelist/conifer to ask a question or point us at an artifact you publish. Submit an artifact on GitHub — free → Manage your own listing — the Influence plan, $499/mo →

Conifer API pricing

Prices published by Conifer, read 2026-10-07 from www.conifer.build/models/.

Plan	Price
Claude Opus 5.5 (max) — blended	$8/ Mtok per million tokens (blended)
Claude Opus 5.5 (max) — input	$4 per million input tokens
Claude Opus 5.5 (max) — output	$20 per million output tokens
GLM-5.3-Flash — input	$0.15 per million input tokens
GLM-5.3-Flash — output	$0.50 per million output tokens
Llama 3.1 8B — input	$0.02 per million input tokens
Jev 1.13 (decisions) — input	$0.042 per million input tokens
Poolside Laguna S 2.1	$0.00 per million tokens (in and out)

Plan details, limits and sources →

Pricing Plans 1

Published pricing tiers and plan structures.

Conifer Plans Pricing

8 plans

PLANS

Security Posture 1

Authentication, domain security, vulnerability disclosure, and trust-center signals.

Conifer Domain Security

TLSv1.3 · DMARC

SECURITY

Resources

Documentation 1

Reference material describing how the API behaves

ContributionGuide

ContributionGuide

Agent Surfaces 1

MCP servers, agent skills, and machine-readable catalogs

LLMsTxt

LLMsTxt

Build 3

SDKs, sample code, and the tooling you integrate with

GitHubOrganization

GitHubOrganization

SourceCode

SourceCode

Packages

Packages

Access & Security 2

Authentication, authorization, and security posture

SecurityPolicy

SecurityPolicy

DomainSecurity

DomainSecurity

Operate 2

Status, limits, changes, and where to get help

IssueTracker

IssueTracker

ChangeLog

ChangeLog

Commercial 2

Pricing, plans, and the legal terms of use

Pricing

Pricing

Plans

Plans

Company 1

The organization behind the API

Website

Website

Other 2

Properties that don't map to a standard resource type

Download

Download

Source (apis.yml)

aid: conifer
name: Conifer
description: Conifer (YC S26) builds Sage, a local-first AI runtime and workspace that runs models entirely on your own machine.
  Its inference engine is written from scratch in Rust, reaching up to 60% faster decode than llama.cpp on Apple Silicon,
  and pairs an editor, integrated terminal, file viewers, and an agent runtime inside a kernel-level sandbox so tool calls
  only touch what you allow. Conifer routes each request to the cheapest capable model, starting with free on-device inference
  and falling back to cloud only when needed, cutting AI spend by up to 80%. The desktop app ships as Juniper for macOS, Windows,
  and Linux with no accounts, no telemetry, and no cloud dependency. Conifer is proprietary and distributed as compiled binaries;
  it currently exposes no public web API.
url: https://raw.githubusercontent.com/api-evangelist/conifer/refs/heads/main/apis.yml
x-type: company
x-source: vc-portfolio
x-backed-by:
- y-combinator
x-tier: stub
x-tier-reason: portfolio-lead
deliveryModel:
  model: library
  license_evidence:
    not_product:
    - coniferkit/sage
  open_source: unknown
  commercial: false
  callable_host: false
  label: Library · distributed through package registries, no callable service
  confidence: low
  source:
  - repository-not-product
  generated: '2026-09-25'
  method: derived
accessModel:
  pricing: freemium
  onboarding: open
  trial: false
  try_now: true
  public: true
  label: Freemium · Open access
  confidence: low
  source:
  - plans
  - security
  generated: '2026-11-06'
  method: derived
image: https://raw.githubusercontent.com/ConiferKit/sage/main/assets/sage-banner-light.png
specificationVersion: '0.23'
created: '2026-07-17'
modified: '2026-07-18'
tags:
- Company
- Artificial Intelligence
- Local AI
- Inference
- LLM
- Developer Tools
- Model Routing
- Desktop Application
- Privacy
- Rust
tags_raw:
- Company
- Artificial Intelligence
- Local AI
- Inference
- Large Language Models
- Developer Tools
- Model Routing
- Desktop Application
- Privacy
- Rust
apis: []
common:
- type: Pricing
  url: https://www.conifer.build/models/
- type: Plans
  url: plans/conifer-plans-pricing.yml
- type: IssueTracker
  url: https://github.com/ConiferKit/sage/issues
- type: SecurityPolicy
  url: https://github.com/ConiferKit/sage/blob/main/SECURITY.md
- type: ContributionGuide
  url: https://github.com/ConiferKit/sage/blob/main/CONTRIBUTING.md
- type: DomainSecurity
  url: security/conifer-domain-security.yml
- type: Website
  url: https://conifer.build
- type: GitHubOrganization
  url: https://github.com/ConiferKit
- type: SourceCode
  url: https://github.com/ConiferKit/sage
- type: Download
  url: https://github.com/ConiferKit/sage/releases/latest
- type: X
  url: https://x.com/coniferbuild
- type: Packages
  url: packages/conifer-packages.yml
- type: ChangeLog
  url: changelog/conifer-changelog.yml
- type: LLMsTxt
  url: llms/conifer-llms.txt
maintainers:
- FN: Kin Lane
  email: kin@apievangelist.com
- FN: APIs.json
  email: info@apis.io
x-enrichment:
  date: '2026-07-19'
  status: backfilled
  pass: local-v1
  note: backfilled from .gitignore signal + verified work evidence

Conifer API — frequently asked questions

How much does the Conifer API cost?

Conifer publishes its prices; its Claude Opus 5.5 (max) — blended plan is $8/ Mtok per million tokens (blended) as of 2026-10-07. APIs.io lists 8 pricing plans for Conifer, with each plan's price, limits and inclusions under Pricing on this page.

Is the Conifer API ready for AI agents?

Conifer has a Kin Score of 15.2 out of 100 (emerging) and an Agent Readiness score of 0.0 (human-only), scored by API Evangelist on 2026-11-06.

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