reachpad
reachpad exposes a REST API at router.reachpad.dev/v1 that accepts CPU, RAM, disk, and region parameters and provisions a cloud VM by routing the request to the lowest-priced matching offer across providers. Machines are metered per second at raw provider cost and terminated automatically when a TTL expires, a max_spend budget is reached, or a DELETE request is issued. Additional endpoints support running shell commands on live machines via HTTP exec, exposing HTTP and WebSocket ports through provider bridge URLs, and managing API keys and credit balances programmatically. Credits are purchased with USDC over x402, with a single 5% fee applied at top-up. A skill.md endpoint packages the full provisioning, exec, and teardown capability set for direct consumption by AI agents.
reachpad is an AI infrastructure platform, priced at $0.5 a month on the Build every call plan. InnovaAI rates it 3.8 of 10 for agency adoption, best for DevOps Engineer, AI Agent Developer and Operations Manager roles.
Agency Audit
Reachpad Router provisions cloud VMs on-demand via REST API, routing requests across providers to find the lowest-cost option and billing per second. Agencies building AI agents, automating infrastructure, or running DevOps consulting workflows benefit most by embedding VM provisioning directly into agent logic or client automation scripts, eliminating manual cloud account management and reducing compute spend through real-time price routing. Adoption pays off when your team spins up temporary compute resources 5+ times per week as part of agent development or infrastructure delivery.
3recommended
36/mo
$2,700/mo
Moderate
Illustrative scenario. Not a guarantee. Net capacity is the value of reclaimed time at $75/hr, less the lowest verified paid base plan (flat plan cost is shared). Hours saved come from the service estimate; implementation, taxes, and unprovided usage charges are excluded.
- DevOps Engineer handling ephemeral vm provisioning for build and test pipelines
- AI Agent Developer handling ai agent compute task execution
- Operations Manager handling cloud compute cost reconciliation
- Your team rarely provisions VMs programmatically and instead uses cloud provider UIs for long-lived infrastructure; Reachpad's per-second billing and API-first design add complexity without time savings.
- Your agency does not employ engineers or technical PMs capable of integrating REST APIs into internal tools or agent code; Reachpad has no managed UI and requires code-level adoption.
- Your infrastructure is locked into a single cloud provider with negotiated discounts; Reachpad's multi-provider routing offers no cost advantage and adds API integration overhead.
Internal Adoption Path
$0.50/mo
$0.50/mo flat plan
36 hr/mo
3 seats × 12 hr each
$2,700/mo
modeled at $75/hr labor rate
$2,700/mo
value − subscription cost
In this model, 3 seats reclaim 36 hours of team time each month. Valued at $75/hr that is $2,700/mo, and after the $0.50/mo subscription it leaves $2,700/mo of capacity for billable client work.
Illustrative scenario. Not a guarantee. Uses the lowest verified paid base plan. Implementation, taxes, and unprovided usage charges are excluded.
Platform Features
Core capabilities of reachpad
Cross-Provider VM Routing
Each provisioning request is routed across cloud providers to find the lowest-priced machine matching the specified CPU, RAM, disk, and region. DevOps engineers stop manually checking provider consoles before every build or test run.
Per-Second Metering at Raw Cost
Machines are billed by the second at raw provider cost with no markup layer. Operations leads get predictable, granular compute spend data without reconciling inflated reseller rates.
Automatic Machine Teardown
Machines self-destruct when TTL expires, when accrued spend hits max_spend, or on a DELETE call. This eliminates idle instance costs that accumulate when engineers forget to terminate VMs after QA or agent task runs.
HTTP Exec Endpoint
POST /v1/machines/:id/exec runs a shell command on a live machine and returns stdout, stderr, exit code, and wall time over HTTP. Engineers can trigger remote execution from agent pipelines or CI scripts without maintaining an SSH session.
Idempotent Machine Creation via client_ref
Passing a client_ref on a create request returns the existing machine if one was already provisioned with that reference, preventing duplicate instances from retry logic. This is particularly useful for AI agent workflows that may retry failed provisioning steps.
HTTP Port Exposure via Bridge URLs
POST /v1/machines/:id/ports exposes an HTTP or WebSocket port and returns the provider's bridge URL. Engineers can surface agent-hosted services or test endpoints without configuring separate reverse proxies or firewall rules.
What Makes reachpad Different
Unique advantages vs similar tools in this niche
Routes compute requests across providers to find the best price
vs Manually comparing cloud provider pricesThe router automatically selects the cheapest provider for each machine spec.
Per-second billing at raw provider cost
vs Hourly billing with provider markupMeters usage per second and charges only the raw provider cost, with no additional margin.
Automatic teardown on TTL or budget
vs Manual VM terminationMachines are destroyed automatically when TTL or max_spend is reached, preventing cost overruns.
Value Equation
Outcome-likelihood-time-effort assessment for reachpad
Limited agency channel
reachpad scored below the agency-resellability threshold (agency_fit_score < 50). The Value Equation projects agency-side outcomes, which don't apply to tools without a clear resell pathway.
Contact reachpadPricing
reachpad platform cost to your agency
Build every call: $0.50/mo
Build every call
- POST`/v1/machines`Create a machine: the shape, the region, the access, the limits.
- `ram_gb`
- `disk_gb`
- `region`
No verified white-label program for reachpad: client-facing delivery runs under the platform's native branding.
Market Intelligence
Offer + scale economics for reachpad
Limited agency channel
reachpad scored below the agency-resellability threshold (agency_fit_score < 50). It's a useful tool but not designed for white-labeled or retainer-based reselling, so we don't publish productized offer economics for it.
Contact reachpadInvestment Decision Framework
Strategic vetting analysis for reachpad
Situational Fit
Fit depends on your client mix
Buy If
4Your infrastructure automation consultants deliver client projects that require dynamic VM provisioning and want to offer clients a unified API instead of managing separate cloud accounts, reducing onboarding friction by 4+ hours per engagement.
Your DevOps or infrastructure team provisions test or production VMs more than 10 times per week and currently uses multiple cloud provider consoles, wasting 3+ hours per week on manual region and instance-type selection across AWS, GCP, and Azure.
Your AI agent development team builds agents that need ephemeral compute resources and currently hardcodes cloud credentials or manually spins up machines, adding 2+ hours per week to agent iteration cycles.
Your Founder or Operations lead tracks cloud spend across multiple providers and wants a single billing interface with per-second metering to audit and optimize compute costs without spreadsheet reconciliation.
Skip If
4Your team rarely provisions VMs programmatically and instead uses cloud provider UIs for long-lived infrastructure; Reachpad's per-second billing and API-first design add complexity without time savings.
Your agency does not employ engineers or technical PMs capable of integrating REST APIs into internal tools or agent code; Reachpad has no managed UI and requires code-level adoption.
Your infrastructure is locked into a single cloud provider with negotiated discounts; Reachpad's multi-provider routing offers no cost advantage and adds API integration overhead.
Your team uses Infrastructure-as-Code tools like Terraform or CloudFormation as the primary provisioning method; Reachpad's API does not replace IaC workflows and would duplicate tooling.
Bottom Line
Reachpad Router provisions cloud VMs on-demand via REST API, routing requests across providers to find the lowest-cost option and billing per second. Agencies building AI agents, automating infrastructure, or running DevOps consulting workflows benefit most by embedding VM provisioning directly into agent logic or client automation scripts, eliminating manual cloud account management and reducing compute spend through real-time price routing. Adoption pays off when your team spins up temporary compute resources 5+ times per week as part of agent development or infrastructure delivery.
Reality Check
Reachpad requires engineers or technical PMs to integrate its API into internal workflows or agent code; it is not a UI-driven tool. Teams without API-first infrastructure automation practices will see minimal ROI. Setup involves funding a USDC wallet and managing API keys across team members.
Moderate effort: standard configuration with some customization needed
Academy for reachpad
Work through it in order: the course for this service first, then the modules behind it.
Course for this service
Reachpad Agency Implementation, Selling Compute Automation to DevOps Teams
Learn how to package Reachpad's cross-provider VM routing and per-second metering into productized services for agencies serving DevOps shops. This course covers building retainer offerings around automated infrastructure provisioning, cost optimization reporting, and agent-driven compute workflows that reduce manual cloud management overhead.
Open the courseNo Academy modules are published for this service yet. Browse the full Academy
Why this category matters
The commercial case before the tooling.
Core concepts
The mental model you need to price and scope the work.
- Concentration Risk LedgerConcept
Concentration Risk Ledger is a framework for tracking how much of an agency's delivery capacity depends on any single model provider, region, or price tier. The unit of analysis is not the vendor relationship but the retainer: for each client engagement, list which workflows break if one provider raises prices, degrades quality, or restricts access. Forrester warned in October 2026 that AI supply chains hide single points of failure in plain sight, and the same week Anthropic cut Claude Haiku 5.5 to $0.10 per million input tokens while OpenAI shipped GPT-6 to 1.2 billion weekly users, both reminders that pricing and capability floors move fast. An agency running every client summarization job through one API has an unpriced liability. The ledger converts that into a number: percentage of monthly delivery hours exposed, and the cost of a routing layer that reduces it.
- Provider Concentration RatioConcept
Provider Concentration Ratio is the share of an agency's live client delivery that depends on a single model vendor, measured in billable projects rather than API keys. A retainer running three client builds on one provider's API looks diversified on an invoice and is not: a pricing change, a deprecation, or a guardrail shift hits all three at once. The ratio matters because infrastructure cost is a pass-through that agencies rarely reprice mid-contract, so a vendor move lands directly on margin. The practical test is to count distinct providers behind each deliverable and flag any client where one vendor carries more than half the inference load. Routing layers such as OpenRouter, Portkey, and Helicone exist to make that count adjustable without rewriting application code. The same discipline applies to compute: industry executives expect only 7 to 10 of hundreds of AI cloud providers to survive consolidation, so GPU capacity booked with a single neocloud carries the same concentration risk as a single-model stack.
- Inference Cost FloorConcept
Inference Cost Floor is the practice of tracking the lowest available price per million tokens for a capability tier, then treating every drop as a trigger to re-price client retainers rather than a windfall to bank. Agencies that price AI work on today's model economics get undercut the moment a cheaper tier ships, because the client's procurement team reads the same launch posts. Anthropic's Claude Haiku 5.5 arrived at $0.10 per million input tokens with a 1 million token context window, which resets what high-volume document summarization and campaign analysis should cost a client. The framework has three moves: benchmark your current blended cost per deliverable, set a review cadence tied to model releases, and pre-agree with clients that savings split rather than vanish. Agencies running fixed-fee AI retainers without a floor review are quietly donating margin every quarter.
Decision and risk
How to judge the fit, and the ways it goes wrong.
- AI Infrastructure Rule: Price the Exit Before You Price the InferenceEvaluation Rule
Treat provider portability as a delivery requirement: put a gateway or routing layer in front of every model call, and price the migration path into the retainer before the first token is billed.
- AI Infrastructure Rule: Route Through a Gateway Before You Sign a Second Model ContractEvaluation Rule
Stand up a gateway layer that logs, caches, and routes requests across at least two providers before any single-model contract becomes load-bearing for a client retainer.
- AI Infrastructure Decision: Multi-Model Orchestration Layer vs Single-Provider Direct IntegrationDecision Framework
IF your agency runs more than two client AI workloads in production and any single provider exceeds roughly 40% of inference spend, THEN build a routing layer that abstracts model calls behind one interface. IF client work is confined to one deliverable type, one model family, and under $2,000 monthly inference, THEN integrate the provider API directly and revisit the decision when either number doubles.
- The Single-Provider Trap: Why AI Infrastructure Stalls When One Model Vendor Owns the StackFailure Pattern
- The Token Bill Trap: Why AI Infrastructure Costs Outrun Agency RetainersFailure Pattern
Delivery system
Blueprints and procedures for running it as a service.
- Multi-Model Orchestration Layer Build (10-15 days)Implementation Blueprint
A productized engagement that puts a routing and failover layer between client applications and frontier model providers, so agencies can swap models on price or capability shifts without rewriting delivery code. The offer converts a one-provider dependency into a governed, observable, multi-vendor stack the client keeps paying a retainer to maintain.
- Model Routing and Fallback Gate (Delivery)Operating Procedure
- Provider Concentration Audit (Retention)Operating Procedure
- Inference Cost Baseline and Margin Guardrail (Onboarding)Operating Procedure
13 modules selected for reachpad
Frequently Asked Questions
Answers about pricing, setup, implementation
reachpad is a REST API that provisions cloud VMs by routing each request across providers to find the best price for the specified CPU, RAM, disk, and region. Machines are metered per second at raw provider cost and destroyed automatically on TTL expiry, budget cap, or a DELETE call. Engineers can also run shell commands on live machines via an HTTP exec endpoint and expose HTTP ports through provider bridge URLs.
reachpad operates on a credit model rather than a per-seat subscription. The Build every call plan charges $0.50 per month as a base, with machines spending at raw provider cost metered per second. Credits are topped up with USDC over x402, and a single 5% fee applies at top-up. There is no separate per-seat license fee listed.
DevOps engineers benefit most directly, replacing manual provider comparisons and console-based provisioning with a single API call. Engineers building AI agent pipelines get autonomous compute provisioning by handing the skill.md endpoint to a funded agent. Operations leads gain a single credit ledger for compute spend instead of multiple provider invoices. Project Managers overseeing infrastructure-heavy sprints reduce ticket-queue coordination by enabling engineer self-service provisioning.
A conservative estimate for a DevOps engineer who currently spends time on manual provider price comparisons, instance provisioning, and teardown tracking is 2 to 4 hours per week. For AI agent development workflows where provisioning is currently a manual human step, the savings compound per agent run rather than per engineer session. These figures are estimates based on the workflows the tool automates, not vendor-reported measurements.
Initial rollout is low-friction for teams already comfortable with REST APIs. A DevOps engineer can mint an API key via POST /v1/accounts, top up credits, and run a first machine provisioning call in under an hour. Integrating reachpad into existing CI pipelines or agent frameworks adds time depending on the complexity of those systems, but the API surface is small and well-documented.
reachpad exposes a JSON REST API, which means it integrates with any system capable of making HTTP requests, including CI/CD pipelines, internal dashboards, and AI agent frameworks. There are no native plugin integrations listed for specific tools like GitHub Actions or Terraform, so integration requires writing API calls directly or wrapping them in scripts.
Machines are destroyed automatically on TTL expiry or via DELETE, so active instances do not persist after the team stops issuing commands. reachpad does not publish a policy on credit balance refunds or data retention on cancellation, so Operations leads should confirm those terms directly with the team at hello@reachpad.dev before committing significant credit balances.
reachpad does not publish a HIPAA, SOC 2, or equivalent compliance certification. Agencies whose internal infrastructure workflows touch regulated data should verify compliance posture directly with reachpad before routing any sensitive workloads through the API.