What industries do you work with?

We work across a wide range of industries including finance, healthcare, e-commerce, logistics, and telecommunications. Our solutions are tailored to each client’s specific domain requirements and regulatory environment.

How long does a typical engagement take?

It depends on the scope. A focused observability deployment or automation workflow can be delivered in 4-6 weeks. Larger initiatives like full-scale LLM integration or platform builds typically run 2-4 months. We always start with a discovery phase to align on timelines.

Do you offer ongoing support after project delivery?

Yes. We offer flexible support and maintenance plans to ensure your systems stay healthy, updated, and optimized. We can also embed with your team on a part-time basis for continuous improvement.

Can you work with our existing tech stack?

Absolutely. We integrate with your current infrastructure and tools rather than forcing a rip-and-replace. Whether you’re on AWS, GCP, Azure, or on-prem, we adapt our approach to what works best for your environment.

What is your pricing model?

We offer both fixed-price project engagements and time-and-materials contracts depending on the nature of the work. Reach out through our contact form and we’ll provide a tailored estimate within 24 hours.

How do you handle data security and compliance?

Security is built into every engagement. We follow industry best practices for data handling, support GDPR and SOC 2 compliance requirements, and can work within your existing security policies and access controls.

Claude Code vs OpenCode — Two Philosophies of AI-Assisted Development

The Terminal Is the New IDE

The AI coding assistant landscape has split into two camps. On one side, Claude Code — Anthropic's proprietary CLI that goes deep on a single model family, betting that vertical integration beats breadth. On the other, OpenCode— an open-source agent with 140k+ GitHub stars that connects to 75+ models, betting that flexibility and community win long-term.

Both are terminal-native. Both are agentic — they read your codebase, run commands, and modify files autonomously. But their architectures, ecosystems, and trade-offs are fundamentally different. This article breaks down what matters for engineering teams choosing between them.

Architecture & Design Philosophy

Claude Code — Vertical Integration

Claude Code is a monolithic CLI with eager loading of built-in tools. It ships with file reading, editing, search, git operations, web fetching, and browser automation out of the box. The architecture is opinionated: one model family (Claude), one permission system, one context management strategy. Everything is designed to work together seamlessly.

The trade-off is lock-in. You run Claude models exclusively — Opus, Sonnet, or Haiku. No swapping in GPT or Gemini mid-session. In return, you get first-class access to every new Anthropic capability the moment it ships.

OpenCode — Universal Adapter

OpenCode uses a client/server architecture written in Go. The inference engine is decoupled from the interface, meaning you can route requests to any provider — or run models locally. Its YAML-based subagent system lets you define custom workflows declaratively: a Python analysis agent, a documentation writer, a test generator — each with its own model and system prompt.

The trade-off is integration depth. No single model gets the first-party treatment that Claude gets inside Claude Code. You gain flexibility but lose the tight feedback loops that come from vertical optimization.

Model Support & Cost

Dimension	Claude Code	OpenCode
Models	Claude Opus, Sonnet, Haiku only	75+ providers (Claude, GPT, Gemini, Llama, local)
Pricing	Anthropic API usage / Max subscription ($100-200/mo)	Free + BYOK (bring your own API keys)
Context window	Up to 1M tokens (Opus 4.6)	Depends on provider (32K–1M)
Cost optimization	Auto model selection (Opus/Sonnet/Haiku)	Route tasks to different models/providers per agent
Local inference	Not supported	Ollama, llama.cpp, vLLM — fully supported

For teams running sensitive workloads where code cannot leave the network, OpenCode's local inference support is a decisive advantage. For teams that want the best coding model available without managing infrastructure, Claude Code's managed approach eliminates operational overhead.

Extensibility & Ecosystem

Both tools support MCP (Model Context Protocol) for connecting to external tools. But their extension models diverge sharply beyond that.

Claude Code Extensions

Claude Code has five distinct extension layers, each solving a different problem:

MCP Servers— 3,000+ integrations. Tool results up to 500K characters. Connect to databases, APIs, Figma, GitHub, and more.

Hooks— 14+ lifecycle triggers (SessionStart, PreToolUse, PostToolUse, Stop, etc.) that fire deterministic shell scripts. Not AI — pure control flow.

Skills— custom slash commands that teach Claude domain-specific workflows.

Subagents— isolated Claude instances for parallel tasks with their own tools, models, and permission modes.

Agent Teams— multiple independent sessions that coordinate, message each other, and divide work in parallel. The most ambitious extension point, launched with Opus 4.6.

OpenCode Extensions

OpenCode takes a more Unix-like approach — fewer extension types, but each is composable:

MCP Servers— standard protocol support for connecting external tools and services.

LSP Integration— native Language Server Protocol support for Rust, TypeScript, Python, Swift, Terraform, and more. The LLM gets real compiler feedback, not just pattern matching.

Custom Commands— user-defined prompts callable as slash commands. YAML-based, versionable, shareable.

YAML Subagents— declarative agent definitions with per-agent model selection, system prompts, and tool access.

Note

OpenCode's native LSP integration is a standout feature. By feeding real compiler diagnostics to the LLM, it can catch type errors and missing imports that pure text-based agents miss entirely. Claude Code achieves similar results through MCP servers and its deeper model understanding, but the approach is architecturally different.

Agentic Capabilities

Capability	Claude Code	OpenCode
File operations	Built-in (Read, Edit, Write, Glob, Grep)	Built-in
Shell execution	Sandboxed Bash	Bash
Git operations	Full (commit, PR, push, rebase)	Full (commit, diff, push)
Web browsing	Built-in (WebSearch, WebFetch)	Via MCP
Multi-agent coordination	Agent Teams (native)	Multi-session (native)
Headless / CI mode	Yes (--print, --headless)	Yes
SWE-bench score	80.9% (Agent Teams)	Model-dependent

Claude Code's advantage here is depth, not breadth. Both tools can read, write, and execute. But Claude Code's multi-step task execution — where it plans, investigates, implements, tests, and iterates autonomously — is noticeably more reliable for complex refactoring and cross-file changes. This is partly model quality, partly tight integration between the agent loop and Claude's instruction-following capabilities.

Developer Experience

Getting Started

Both tools are fast to install:

# Claude Code
npm install -g @anthropic-ai/claude-code
claude

# OpenCode
curl -fsSL https://opencode.ai/install | bash
opencode

Claude Code requires an Anthropic API key or a Claude Max/Pro subscription. OpenCode works immediately with free included models, or you can connect your own API keys for premium providers.

Interface

Claude Code runs as a straightforward CLI with a streaming text interface. It's fast to type, reads well in small terminals, and integrates into VS Code, JetBrains, and the Claude desktop app.

OpenCode provides a full TUI (Terminal User Interface) built with Bubble Tea— with panels, session management, and an interactive Plan/Build agent switcher. It feels more like an IDE inside your terminal. Also available as a desktop app and IDE extensions.

Context Management

Claude Code uses automatic context compaction — as the conversation approaches the context limit, earlier messages are compressed transparently. Combined with up to 1M tokens on Opus 4.6, long sessions rarely lose coherence.

OpenCode manages context per-session. You can run multiple sessions in parallel on the same codebase, each with its own context. Sessions are shareable — generate a link to any session for debugging or review.

Privacy & Security

Claude Code

All code is sent to Anthropic's API for inference. Anthropic's data retention policy applies. The sandboxed execution environment prevents the agent from making destructive changes without permission. The hooks system provides deterministic guardrails around what the agent can and cannot do.

OpenCode

Code goes to whichever provider you configure — or nowhere at all if you use local models via Ollama or vLLM. OpenCode does not store code or context on its servers. Session sharing is opt-in. For regulated industries or air-gapped environments, the fully local option is a hard requirement that only OpenCode meets.

When to Choose Which

Choose Claude Code When

You want the most capable coding agent available today — 80.9% SWE-bench speaks for itself.

Your team is already on the Anthropic ecosystem (Claude API, Max subscriptions).

You need deep multi-file refactoring, autonomous PR creation, and complex agentic workflows out of the box.

You value having every new Anthropic feature immediately — Agent Teams, 1M context, hooks, and skills.

Choose OpenCode When

You need model flexibility — swapping between Claude, GPT, Gemini, or local models depending on the task and budget.

Privacy is non-negotiable — air-gapped environments, regulated industries, or policies that prohibit sending code to third-party APIs.

You want full source access and the ability to fork, modify, and self-host the tool.

You prefer native LSP integration for real-time compiler feedback in the AI loop.

Our Take

We use both daily. Claude Code is our primary tool for production-grade refactoring, complex multi-file changes, and anything that benefits from the sheer quality of Claude Opus. OpenCode fills the gap when we need a second opinion from a different model, when we're working in environments with strict data policies, or when we want to prototype with local models at zero marginal cost.

The real answer is not "pick one." The tools complement each other. Claude Code is the heavy lifter. OpenCode is the Swiss army knife. The best engineering workflows in 2026 use both — and tools like RTK to keep token costs under control across all of them.