Dial vs Kastra: Features, Pricing & Which Is Better (2026)

A side-by-side comparison of Dial and Kastra — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.

Dial

Freemium

Communication API that gives an AI agent a real phone number with voice, SMS and native iMessage on one line, provisioned in seconds.

Agent Phone Numbers on Demand: Real US, Canadian and international numbers are provisioned through a single API call, giving an agent its own phone identity in seconds.
Native iMessage with Fallback: Agents send blue-bubble iMessage from their own number, including inbound and cold outbound, with automatic RCS and SMS fallback.
Autonomous Outbound Calls: A call is placed with a plain-language instruction and constraints - what to ask, what to book, what price ceiling to hold - and runs on its own with real-time transcription and TTS replies.
Unified Multi-Channel Webhook: One webhook receives voice, SMS and iMessage events in the same format, so there is no per-channel plumbing to maintain.
MCP Server and Prompt Setup: Dial works out of the box as an MCP server with Claude Code, Cursor, Windsurf and Grok, and a single pasted prompt lets an agent complete the whole setup itself.
Self-Hosted or Managed Voice: Voice can run against your own LLM over WebSocket at a lower per-minute rate, or end to end on Dial's hosted model.
SDKs and REST API: Node.js and Python SDKs plus a LangChain integration and a direct REST API cover code-first integration paths.
Compliance Certifications: GDPR, CCPA, SOC 2 and HIPAA credentials are in place for security reviews.

Agent Errand Calls: Sending an agent to call a vendor, get a quote and book the earliest slot only if it stays under a stated budget, then reading the transcript afterward.
Customer Support Lines: Giving an AI support agent an inbound number that handles voice and text on the same line with transcripts routed to your systems.
Existing Chat Agents: Adding a real phone number to agents already running in WhatsApp, Telegram or a setup wizard, so SMS and voice work without building a telecom layer.
Autonomous Company Operations: Giving agent-run businesses a real phone identity so they can be reached and can transact by phone.
Verification and Notifications: Two-way SMS through the same number for confirmations, reminders and inbound replies routed to a webhook.
Prototyping by Prompt: Having a coding agent stand up a working phone number and place a test call within a minute, before writing any integration code.

Kastra Labs Inc

Freemium

Kastra is the runtime authorization layer for AI: it decides in sub-milliseconds what agents, models, and tools are allowed to do before they act.

Sub-Millisecond Policy Decisions: Every prompt, tool call, and API request is evaluated in under a millisecond so enforcement never becomes the bottleneck.
Deterministic Policy Engine: Rules are written and evaluated deterministically, not by an LLM judge, so the same input always produces the same allow/deny.
Kastra Edge (Local Enforcement): A local enforcement component that runs next to the agent so decisions happen even without a network round-trip.
Cryptographic Audit Trails: Signed logs of every decision give security and compliance teams tamper-evident evidence of agent behavior.
Coding Agent Integrations: First-class hooks for Claude Code, Cursor, Codex, and OpenClaw let policies wrap the tool calls those agents already make.
Kastra Recon: Discovers what actions an agent actually attempts in a codebase or environment, so policies can be authored from observed behavior instead of guesses.
Zero Implicit Trust Model: Nothing an agent asks to do runs until it is explicitly allowed by policy, aligning agent access with zero-trust principles.

Guardrails for Coding Agents: Prevent an autonomous coding agent from dropping tables, force-pushing to main, or leaking secrets during long runs.
Enterprise Rollout Approvals: Give security teams a control plane before letting employee-facing AI agents access internal APIs.
Regulated-Industry Agent Deployments: Provide the auditable authorization trail required in finance, healthcare, or government agent pilots.
Multi-Agent Systems: Enforce per-agent scopes so a research agent can read data but only a deploy agent can trigger production changes.
Incident Forensics: Reconstruct exactly what an AI agent was allowed or blocked from doing after a suspicious action.