Apache Maka vs Sim: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of Apache Maka and Sim — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.
Apache Maka
The Apache Software Foundation
Apache-licensed local-first agent workspace that runs tools in a sandbox and records every model message and tool call as a recoverable execution log.
Key features
- Append-Only Execution Record: Model messages, tool calls, tool results, permission decisions, and turn termination events are written down durably, so the transcript is evidence rather than a disposable chat buffer.
- Context Trimming Without Data Loss: Old tool output can be omitted from the next prompt to shorten context while the full saved history remains intact and inspectable.
- Single Runtime Host: Desktop, terminal, and evaluation all execute through one runtime, so behavior does not diverge between how you develop and how you benchmark.
- Sandboxed Tool Boundary: Built-in Read, Write, Edit, Bash, Glob, and Grep tools run under a sandbox; anything leaving that boundary requires approval, and Computer Use and catalog skills are opt-in.
- Crash Recovery and Resume: Runs can be aborted, failures are classified, and an interrupted turn can optionally be resumed rather than restarted from scratch.
- Session Branching and Search: The desktop workspace supports creating, archiving, searching, renaming, retrying, regenerating, and branching sessions from any turn.
- Bring Your Own Model: Connect a cloud API, a locally hosted model, or a compatible gateway, with streaming output, thinking, usage reporting, and clearer provider errors.
- Declarative Evaluation Harness: maka eval expands multi-arm experiments into task by repetition by subject cells with immutable per-cell attempts and a result kernel covering score, normalized usage, attributable cost, duration, and failure reason.
- Local-First Storage: Sessions, settings, artifacts, and run records stay on the machine by default, with local memory and optional web search when configured.
Best for
- Auditable Agent Runs: Keeping a defensible record of exactly what an agent did and which permissions were granted during a task.
- Long Coding Sessions: Working through a multi-turn refactor with branching and resume instead of losing state when a turn fails.
- Agent Benchmarking: Running reproducible multi-arm experiments comparing models, prompts, or external agent subjects on the same task set.
- Air-Gapped or Regulated Work: Running an agent workspace where sessions and artifacts must remain on local infrastructure.
- Cost and Usage Analysis: Attributing token usage, cost, and duration per experiment cell to decide which model configuration to ship.
- Terminal Workflows: Driving an agent from the current project directory or scripting a single non-interactive turn from CI or a shell.
- Open-Source Agent Research: Building on a permissively licensed runtime whose execution semantics and architecture are fully documented.
Sim
Sim
Open-source AI workspace to build, deploy and manage AI agents across 1,000+ integrations from one platform.
Key features
- Visual Agent Builder: Drag-and-drop canvas for wiring prompts, tools, memory and LLM calls into deployable agents.
- 1,000+ Integrations: Ready-made connectors to CRM, sales, support, dev and productivity tools so agents can act across a company's stack.
- Multi-model Support: Route each step to the best model from every major LLM provider (OpenAI, Anthropic, Google, open-weights).
- Runtime & Scheduling: Deploy agents as scheduled jobs, webhooks or API endpoints with retries and error handling.
- Team Workspace: Shared library of prompts, tools and agents with role-based access so teams reuse rather than rebuild.
- Open Source & Self-Hostable: Full source available so security-sensitive teams can run Sim in their own cloud.
- Observability: Traces every agent run with inputs, tool calls and outputs so builders can debug and improve prompts.
Best for
- Sales operations: Build agents that enrich inbound leads, sync them into the CRM and hand them to reps.
- Customer support: Deploy triage agents that read tickets, pull knowledge-base answers, and draft or send replies.
- Internal automations: Wire agents into Slack, Google Workspace or Notion to automate reports, briefs and reminders.
- Data workflows: Chain LLM steps with API tools to summarize dashboards, clean CSVs or extract structured data.
- Compliance-sensitive teams: Self-host Sim to keep prompts, tool credentials and traces inside the company's own cloud.