AI tool comparison
Azure Foundry Hosted Agents vs Sourcegraph Cody 3.0
Which one should you ship with? Here is the side-by-side panel verdict, pricing read, reviewer split, and community vote comparison.
Developer Tools
Azure Foundry Hosted Agents
Per-session isolated agent sandboxes on Azure — scale to zero, any framework
50%
Panel ship
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Community
Paid
Entry
Microsoft Azure's Foundry Agent Service now offers Hosted Agents in public preview — per-session isolated compute sandboxes purpose-built for running AI agents at scale. Each session gets its own container with a persistent filesystem, internet access (optional), and a Python environment pre-loaded with common agent dependencies. Sessions spin up in seconds and terminate — and stop billing — the moment the agent task completes. The design is framework-agnostic: it officially supports LangGraph, OpenAI Agents SDK, Claude Agent SDK, and Microsoft's own Agent Framework, with others planned. This removes one of the most awkward parts of deploying agents in production: figuring out where they actually run. The persistent filesystem per session means agents can read and write files across their task without external storage configuration. Pricing is $0.0994/vCPU-hour and $0.0118/GiB-hour — competitive with Lambda/Cloud Run for bursty workloads. The service is available in six Azure regions at launch. For enterprises already invested in Azure, this is a compelling "we just figured out the infra" moment. Independent developers can also use it without an enterprise agreement.
Developer Tools
Sourcegraph Cody 3.0
Autonomous PR reviews and codebase Q&A powered by your code graph
75%
Panel ship
—
Community
Free
Entry
Cody 3.0 upgrades Sourcegraph's AI coding assistant with an autonomous pull request review agent that posts contextual inline comments directly on PRs, and a conversational Q&A interface that draws on Sourcegraph's code graph for whole-codebase context. Unlike generic LLM coding assistants, Cody uses Sourcegraph's existing code intelligence graph to ground answers in actual symbol relationships, call chains, and repository history. It targets teams already running Sourcegraph who want AI-augmented code review without switching to a new platform.
Reviewer scorecard
“Framework-agnostic hosted sandboxes with scale-to-zero is exactly what I need for deploying agents without maintaining my own Kubernetes cluster. The per-session isolation eliminates a whole class of security concerns I was handling manually. The Claude Agent SDK support means I don't have to choose between Azure and my preferred model.”
“The primitive here is clear: a code-graph-grounded LLM that understands your codebase at the symbol level, not just the file level — and Cody 3.0 puts that to work in two specific places: PR review comments and Q&A. The DX bet is right. Rather than asking devs to context-stuff a chat window, Sourcegraph lets the graph do the retrieval, which means you get answers like 'this function is called from 14 places and three of them pass null' instead of hallucinated summaries. The skip risk is that autonomous PR comments require tuning to not be noise — if the signal-to-noise ratio on inline comments is bad in week two, devs will disable it. But the underlying graph primitive is genuinely not replicable with a Lambda and three API calls — it's years of indexing infrastructure that earns its keep here.”
“Public preview means production instability risk and pricing could change significantly at GA. The cold start time for agent sessions needs to be benchmarked against real workloads before committing. And six regions is thin coverage for global deployments — wait for broader availability.”
“Direct competitor is GitHub Copilot's PR review feature, which ships with zero additional infrastructure for teams already on GitHub. Cody's actual advantage is the code graph — Sourcegraph has spent years building precise cross-repo symbol resolution that GitHub's Copilot still doesn't match on large monorepos or multi-repo codebases. The scenario where this breaks: teams with fewer than 20 engineers on a single mid-size repo who are already paying for Copilot Business have no rational reason to add Cody's overhead. What kills this in 12 months isn't a competitor — it's GitHub shipping better cross-file context in Copilot Enterprise and erasing the graph advantage. Cody ships on the strength of the graph moat; the question is how long that moat holds.”
“The battle for agent infrastructure is the next cloud wars — and Microsoft just answered Google Cloud's agent platform launch with their own. Framework-agnostic compute that works with any model provider is a smart commoditization play: own the infrastructure layer, let the model battle play out above it.”
“This is squarely developer infrastructure — not directly relevant to creative workflows unless your studio runs its own agents. Worth watching for the ecosystem tools that get built on top of it.”
“The buyer here is engineering leadership at mid-to-large enterprises already running Sourcegraph — that's a narrow installed base selling into a budget line that already has GitHub Copilot, Cursor, or both. The moat is real: the code graph is defensible infrastructure that took years to build. But the pricing architecture is a problem — Free and $9/mo Pro don't cover the actual infrastructure cost of running autonomous PR review at scale, which means the business only works if enterprise deals convert, and the enterprise sales cycle for Sourcegraph is long and contested. When GitHub bundles better AI review into Copilot Enterprise at no incremental cost, the standalone Cody value prop collapses for everyone except the multi-repo power users. The expand story within existing Sourcegraph accounts is credible; the net-new acquisition story against GitHub's distribution is not.”
“The job-to-be-done is specific: 'give me a reviewer who actually understands the full codebase before commenting on my PR,' which is a real and painful gap — most AI review tools comment on diffs without knowing what changed downstream. Cody 3.0's graph-backed context directly attacks that gap. Onboarding for existing Sourcegraph users is presumably fast since the index already exists; for new users it's a longer setup tax that could kill early momentum. The completeness question is whether the PR review agent integrates into the GitHub/GitLab review UI natively enough that engineers don't need to context-switch — inline comments are the right surface, but the product lives or dies on whether those comments are precise enough that teams keep them enabled after the honeymoon period. The opinionated bet on graph-backed context over naive RAG is exactly the right product call.”
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