Sapiom
by Sapiom
Agent infrastructure that routes, runs and meters AI agents in production
Sapiom is a production infrastructure platform for teams shipping AI agents, combining an OpenAI-compatible inference router, a local agent development studio and a managed runtime with retries, schedules and per-attempt traces. It targets engineering teams whose agent demos work but whose agent economics, reliability and audit trail do not survive contact with production.
Sapiom is a San Francisco company founded in 2025 by Ilan Zerbib that sells infrastructure for running AI agents in production rather than a framework for building them. The platform ships as three components. Router accepts OpenAI-compatible API requests, selects the most efficient permitted model for each task instead of defaulting to the most expensive one, and meters every call across cost, quality, latency and reliability. Agent Studio is a local development environment where agents are authored in TypeScript against the @sapiom/agent SDK, stored in private Git repositories the customer owns, and moved between Canvas, Steps and Code views; it connects to Claude Code, Cursor, Codex and any client speaking Model Context Protocol over stdio. Runtime is the managed execution layer, built on typed step graphs with explicit retries, signals, schedules and per-attempt traces, so every run attempt can be inspected after the fact. Sapiom also bundles roughly fifteen metered capabilities agents usually need separate vendor accounts for, including web search, scraping, browser automation, sandboxed compute, Postgres, Redis, vector and search storage, queues, image and audio generation, private Git repositories, email enrichment, and domain and DNS registration, with partners including YugabyteDB, Linkup and Prelude. The company reports more than 270 million transactions processed in roughly six months, over 100,000 agent runs a day, and one customer cutting inference cost 75%. It raised a $15 million seed led by Accel in February 2026 and a $35 million Series A led by Dragonfly in August 2026, totalling $50 million in under a year, and acquired machine-payments startup Fewsats in June 2026.
The platform or infrastructure engineering lead who already has agents working in a demo and now owns the bill, the uptime and the audit trail for them in production.
Per-task model routing plus a metered managed runtime, so agent inference cost and failure behaviour become measurable and enforceable instead of an unbounded line item.
At a Glance
- Category
- AI Agents & Orchestration
- Pricing
- Freemium, Subscription, Usage-based
- Target Market
- CTOs, Enterprise Developers, Platform Engineers, AI Engineering Leads
- Deployment
- Cloud-first, API-based
- Founded
- 2025
- Headquarters
- San Francisco, United States
Key Features
- ✓Router
Accepts OpenAI-compatible requests and picks the cheapest permitted model that meets the task's quality and latency bar, metering every call
- ✓Agent Studio
Local development environment with a real coding agent and Canvas, Steps and Code views, backed by private Git repositories the customer owns
- ✓Runtime
Managed execution on typed step graphs with explicit retries, signals, schedules and per-attempt traces for every run
- ✓Bundled metered capabilities
About fifteen agent side-services including search, scraping, browser automation, sandboxed compute, Postgres, Redis, vector search, queues and storage
- ✓MCP and coding-agent integration
Connects to Claude Code, Cursor, Codex and any Model Context Protocol client over stdio, so agents are authored in existing tools
- ✓Spending rules and policy controls
Account-level spending rules on every plan and per-agent rules from the Startup tier, with org-wide policies on Scale
- ✓Local-first development
Agents run locally for free against stubbed capabilities before any cloud execution is metered, keeping iteration cost at zero
Capabilities
Use Cases
- •Cutting agent inference spend
Route each model call to the cheapest capable model rather than a single premium default; one customer reported a 75% inference cost reduction
- •Running scheduled autonomous workflows
Use runtime schedules and signals to run recurring agent jobs with explicit retry semantics instead of cron plus bespoke error handling
- •Post-incident agent debugging
Inspect per-attempt traces and step-level execution history to find which step of a non-deterministic agent run actually failed and why
- •Consolidating agent tooling vendors
Replace separate accounts for search, scraping, browser automation, sandboxes and databases with one metered platform bill
- •Governing agent spend across a company
Apply account-level and per-agent spending rules so autonomous agents cannot quietly run up an unbounded inference bill
Ideal For
Best For
- ✓Teams whose agent inference bill is growing faster than usage and who need per-task model routing rather than a single expensive default model
- ✓Shipping customer-facing agentic products that need retries, schedules and signals rather than a script that runs once and dies
- ✓Debugging non-deterministic agent failures using per-attempt traces and step-level execution history
- ✓Consolidating the long tail of agent side-services (search, scraping, browser automation, sandboxes, Postgres, queues, storage) behind one metered account
- ✓TypeScript engineering teams that want agents to live in their own private Git repositories rather than inside a vendor's no-code canvas
Not Ideal For
- ✗Python-first ML teams — the agent SDK is TypeScript, so LangGraph, CrewAI or Pydantic AI stacks would require a rewrite rather than an integration
- ✗Regulated buyers needing on-premise or air-gapped deployment; the platform is a hosted cloud runtime with no published self-hosted option
- ✗Organisations that need SOC 2, SSO, org-wide policy and audit-trail export from day one — those are gated behind the custom-priced Scale tier, not the $200/month plan
- ✗Business users looking for a no-code agent builder; this is developer infrastructure with a coding agent and a git-backed workflow
Integrations
Deployment
Market Analysis
Pros
- ✓Publishes real list pricing including per-capability unit rates, which makes agent unit economics modellable before signing anything
- ✓Durable-execution primitives (typed step graphs, explicit retries, signals, schedules, per-attempt traces) are genuine production infrastructure, not a demo wrapper
- ✓Agent source lives in private Git repositories the customer owns, limiting lock-in relative to canvas-based agent builders
- ✓Reported scale is non-trivial for a company under a year old: 270M+ transactions and 100,000+ agent runs a day
- ✓Investor list includes Anthropic and Okta Ventures alongside Dragonfly and Accel, which suggests model-vendor and identity-vendor alignment
Cons
- ✗No independent user reviews exist anywhere — G2, Capterra, TrustRadius and PeerSpot have no listing, and a Hacker News search returns nothing substantive about the product
- ✗The agent SDK is TypeScript-only, which excludes the large Python-first ML and data engineering population without a rewrite
- ✗SOC 2, SSO, org-wide policy, audit trail and SLAs are all gated behind the custom-priced Scale tier, so the published $200/month plan is not enterprise-ready
- ✗No named reference customers have been published; the headline 75% inference cost saving is attributed to an unnamed customer and is vendor-reported
- ✗Per-run pricing is hard to forecast for bursty or long-running agents, since a run is billed regardless of how much work it does
- ✗The company is under a year old with no on-premise or self-hosted option, which is a hard blocker for regulated or air-gapped buyers
Pricing
Developer
$0
- ✓50 runs/day for accounts created by 31 Aug 2026, then 10 runs/day
- ✓$1 per run overage
- ✓Sapiom-hosted intelligence
- ✓Account-level spending rules
- ✓Community support
Startup
From $200/mo
- ✓2,000 runs/month included
- ✓$0.50 per run overage
- ✓Higher intelligence and capability limits
- ✓Per-agent spending rules
- ✓Priority support
Scale
Contact for pricing
- ✓Custom run volume with volume pricing
- ✓Committed spend and inference
- ✓Org-wide policies and audit trail
- ✓SSO and telemetry export
- ✓Multi-tenant management, SLAs, SOC 2, dedicated engineering
List pricing is published, which is unusual in this category: $0 Developer, $200/month Startup with 2,000 runs, custom Scale. Billing is two-layer — a per-run charge ($1 on free, $0.50 on Startup once the 2,000 included runs are used) plus separately metered capability calls at published unit rates such as roughly $0.006 per web search and $0.01 per browser extraction, with model calls billed per token. Governance features enterprises usually treat as mandatory — SSO, org-wide policy, audit trail, telemetry export, SOC 2 and SLAs — appear only on the custom-priced Scale tier. The free Developer allotment drops from 50 runs a day to 10 for accounts created after 31 August 2026.
Security & Compliance
Sources
This page was written from 7 sources, 4 on domains other than sapiom.ai.
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