Posted in Cloud & Infrastructure, Engineering

Edge Computing in 2026: Cloudflare Workers vs Vercel vs Deno Deploy, Where Your Code Actually Runs and What It Costs

“Run your code at the edge” was the serverless pitch of the early 2020s: put logic close to users and cut latency. In 2026 the three platforms most associated with that pitch have taken noticeably different paths. We checked each one’s official documentation and pricing on October 28, 2026 to see where code actually runs today, what limits apply, and what it costs.

At a Glance

SpecCloudflare WorkersVercel FunctionsDeno Deploy
Where code runsCloudflare’s global network, across hundreds of locationsConfigurable regions on Fluid compute; edge runtime runs nearest the request2 regions on the new platform, plus self-hosted regions
RuntimeV8 isolatesNode.js (recommended) or Edge runtime (V8)Deno 2, with Node app support
Free tier100,000 requests/day, 10 ms CPU per invocationHobby: 1M invocations, 4 hrs Active CPU/month (non-commercial)1M requests, 10 hrs Active CPU/month
Entry paid plan$5/month minimumPro: $20/month with $20 usage creditPro: $20/month
Edge direction in 2026Edge-first by designRecommends migrating from Edge to Node.jsFewer regions than Deploy Classic, which shut down July 20, 2026

Short Verdict

If “edge” is the requirement, meaning code that runs near every user by default, Cloudflare Workers is the platform still built around it, with a $5 paid plan and CPU-time billing that does not charge for time spent waiting on network calls. Vercel now steers developers away from its Edge runtime and toward Node.js on Fluid compute, which favors running functions near your data rather than near your users. The new Deno Deploy runs in two regions, so it is better viewed as a serverless platform than an edge network. Pick based on where your data lives: if your database sits in one region, compute next to it usually beats compute next to the user.

What “Edge” Means Here

Edge computing means processing a request in a location close to the user instead of in one central region. It shortens the network round trip for work that does not need a central database, such as redirects, authentication checks, personalization from cookies, A/B test assignment, and caching logic. It helps much less when every request must read from a single-region database, because the edge function then makes its own long round trip to that database.

Cloudflare Workers

Workers run on Cloudflare’s global network, which the company describes as thousands of machines across hundreds of locations, and a request arriving at any Cloudflare data center can invoke your Worker. Instead of a container or virtual machine per function, each Worker runs in a V8 isolate inside an existing runtime process. Cloudflare says an isolate can start around a hundred times faster than a Node process in a container or VM and uses an order of magnitude less memory at startup, which is why Workers avoid the classic cold-start penalty. Isolates can be evicted at any time, so global mutable state should not be relied on.

PlanRequestsCPU timePrice
Free100,000/day10 ms per invocation$0
Paid (Standard)10M/month included, then $0.30 per million30M CPU-ms/month included, then $0.02 per million CPU-ms; up to 5 minutes CPU per request (30 s default)$5/month minimum

Cloudflare does not charge for duration, only CPU time, so a Worker that spends most of its time waiting on a fetch is billed only for the milliseconds it actually computes.

Vercel: From Edge Runtime to Fluid Compute

Vercel’s Edge runtime still exists and, by default, executes in the region closest to the incoming request. But Vercel’s own documentation now recommends migrating from Edge to Node.js “for improved performance and reliability,” notes that both runtimes run on Fluid compute with Active CPU pricing, and states that starting in Next.js 16.3, setting runtime = 'edge' is no longer supported. The Edge runtime also has real constraints: a subset of Web and Node.js APIs, no filesystem access, no eval, a requirement to begin responding within 25 seconds, and code size limits of 1 MB (Hobby), 2 MB (Pro), and 4 MB (Enterprise) after compression.

ResourceHobby (included)Pro (beyond included credit)
Active CPU4 hours/monthFrom $0.128/hour
Provisioned memory360 GB-hours/monthFrom $0.0106/GB-hour
Function invocations1M/monthFrom $0.60 per million

Hobby is for personal, non-commercial use and cannot buy extra usage. Pro costs $20 a month, includes $20 of usage credit, and is pay-as-you-go beyond it.

Deno Deploy: Rebuilt, and Fewer Regions

Deno rebuilt Deploy on a new execution environment using Deno 2.0 that runs both Deno and Node apps, with first-class support for Next.js, Astro, and SvelteKit. Its documentation lists 2 regions for the new platform, compared with 6 for Deploy Classic, plus the option of self-hosted regions. Deno announced that Deploy Classic and the subhosting v1 API would shut down on July 20, 2026.

PlanPriceRequests/monthActive CPUEgress
Free$01M10 hours20 GiB
Pro$20/month5M, then $2 per million50 hours, then $0.10/hour200 GiB, then $0.20/GiB
Builder$200/month25M, then $2 per million500 hours, then $0.10/hour2,000 GiB, then $0.20/GiB
EnterpriseCustomCustomCustomCustom; 99.95% SLA

Pros and Cons by Platform

  • Cloudflare Workers pros: genuinely global by default, isolate-based startup, cheap $5 entry plan, no charge for waiting time. Cons: isolate runtime is not full Node.js, and the free tier’s 10 ms CPU cap is tight for heavier work.
  • Vercel pros: tight integration with Next.js and Fluid compute, configurable regions near your data. Cons: Edge runtime is being de-emphasized and is unsupported in Next.js 16.3+, and Hobby is non-commercial only.
  • Deno Deploy pros: generous free CPU allowance, Deno and Node apps, self-hosted regions, clear usage pricing. Cons: only 2 managed regions on the new platform, and teams on Deploy Classic had to migrate.

Bottom Line

The industry has quietly split “edge” in two. Cloudflare Workers still runs your code near the user everywhere, which is ideal for request-level logic that does not need a central database. Vercel and Deno have moved toward regional serverless compute placed near your data, which suits typical database-backed apps better. Decide which latency you are actually fighting, user-to-server or server-to-database, and the platform choice follows.

Frequently Asked Questions

  • Is Vercel’s Edge runtime deprecated? Vercel recommends migrating from Edge to Node.js, and setting runtime = 'edge' is no longer supported starting in Next.js 16.3.
  • How much does Cloudflare Workers cost? Free for 100,000 requests a day; the paid plan starts at $5 a month with 10 million requests and 30 million CPU milliseconds included.
  • Is Deno Deploy an edge platform? The new Deno Deploy lists 2 managed regions, so it behaves more like regional serverless; Deploy Classic, which had 6 regions, shut down on July 20, 2026.
  • When does edge computing not help? When every request must reach a single-region database, since the edge function then adds its own long round trip.

Sources

Posted in Artificial Intelligence, Cybersecurity

AI-Generated Code Security in 2026: The Risks, the Evidence, and a Review Checklist for Teams

AI coding assistants have become part of everyday development, and for many teams the security question has moved from “is this allowed?” to “how do we review it?” This guide pulls together public evidence on how often AI-generated code is insecure, maps the risks to the OWASP Top 10 for LLM Applications, and ends with a practical review checklist grounded in NIST’s secure development guidance.

At a Glance

QuestionShort answer
How often is AI-written code insecure?In Veracode’s 2025 test of 100+ models on 80 tasks, 45% of samples introduced an OWASP Top 10 vulnerability
Worst weaknesses in that testCross-site scripting (failed 86% of the time) and log injection (failed 88%)
Riskiest language in that testJava, with a failure rate above 70%
Do newer or bigger models fix it?Not according to Veracode: security did not improve with newer or larger models
Most relevant OWASP LLM risksLLM05 Improper Output Handling, LLM06 Excessive Agency, LLM03 Supply Chain, LLM01 Prompt Injection
Process framework to anchor onNIST SSDF (SP 800-218) plus its AI profile, SP 800-218A

Short Verdict

Treat AI-generated code exactly like code from a fast, confident junior developer who has never seen your threat model: useful, often correct, and frequently insecure in predictable ways. The evidence says model upgrades will not solve this on their own, so the fix is process. Keep a human reviewer accountable for every merge, run static analysis and dependency checks on every pull request, and limit what coding agents are allowed to do without approval.

What the Evidence Says

One of the most widely reported recent datasets is Veracode’s 2025 GenAI Code Security Report, released July 30, 2025. Veracode gave more than 100 large language models 80 curated code-completion tasks, each tied to a known weakness in MITRE’s CWE list, and checked whether the output was secure. In 45% of cases the model introduced a vulnerability from the OWASP Top 10; put another way, when a secure and an insecure approach were both available, the models chose the insecure one 45% of the time. Java fared worst, failing more than 70% of the time, while Python, C#, and JavaScript ranged from 38% to 45%. Two weakness classes stood out: models failed to defend against cross-site scripting (CWE-80) in 86% of relevant cases and against log injection (CWE-117) in 88%. Veracode also reported that newer models wrote more syntactically correct code without becoming more secure, and that larger models did not perform significantly better than smaller ones.

Two caveats keep this in proportion. These are vendor-run benchmark tasks, not a measurement of production codebases, and Veracode sells application security testing. Even so, the specific failures it reports, such as unescaped output and unsanitized log input, are exactly the kinds of flaws a review process can be designed to catch.

Mapping the Risks to the OWASP Top 10 for LLM Applications

OWASP’s 2025 list was written for applications built on language models rather than for coding assistants specifically, but several entries translate directly to AI-assisted development. The mapping below is our interpretation of how each risk shows up in a coding workflow.

OWASP risk (2025)How it shows up in AI-assisted coding
LLM05 Improper Output HandlingGenerated code is merged or executed without validation, carrying injection flaws straight into the codebase
LLM06 Excessive AgencyCoding agents with shell, file-write, or push permissions can make changes no one reviewed
LLM03 Supply ChainSuggested packages and dependencies may be outdated, malicious, or not exist at all
LLM01 Prompt InjectionUntrusted text in issues, comments, or repository files can steer an agent toward harmful actions
LLM02 Sensitive Information DisclosureSecrets or proprietary code pasted into prompts, or credentials hard-coded in suggestions
LLM09 MisinformationPlausible but wrong API usage or security advice accepted without verification

The Process Anchor: NIST SSDF and SP 800-218A

NIST’s Secure Software Development Framework (SP 800-218) is the most widely referenced baseline for secure development practices. In July 2024 NIST published SP 800-218A, “Secure Software Development Practices for Generative AI and Dual-Use Foundation Models: An SSDF Community Profile,” which adds AI-specific practices and is meant to be used alongside the SSDF, not instead of it. Its stated audience is producers of AI models, producers of AI systems that use those models, and acquirers of those systems. For a team that only uses coding assistants, the practical takeaway is simpler: AI-written code goes through the same SSDF controls as any other code, with no exemptions for where it came from.

The Review Checklist

  • A named human owns every merge. The person who accepts AI-generated code is accountable for it exactly as if they had typed it.
  • Static analysis on every pull request. Run SAST with rules for the weaknesses AI code gets wrong most often, especially output encoding (XSS) and log injection.
  • Verify every new dependency. Confirm a suggested package exists, is the one you meant, is maintained, and passes your software composition analysis before it lands in a lockfile.
  • Scan for secrets. Block commits that contain keys or tokens, and keep secrets out of prompts.
  • Limit agent permissions. Give coding agents the least privilege they need, require approval before they push, merge, or run destructive commands, and keep logs of what they did.
  • Treat repository text as untrusted input to agents. Issue bodies, comments, and files from outside contributors can contain instructions; do not let an agent act on them unreviewed.
  • Require tests for generated logic. Ask for tests alongside code, and review the tests as critically as the code.
  • Track provenance. Label or note AI-assisted changes so you can find them quickly if a pattern of flaws turns up later.

Pros and Cons of Leaning on AI for Code

  • Pros: faster first drafts, quick boilerplate and tests, and agents that can take on routine fixes, which frees reviewers to focus on design and risk.
  • Cons: predictable security gaps that benchmark data says are not shrinking with newer models, new supply-chain and prompt-injection exposure through agents, and a risk that reviewers rubber-stamp code that looks clean.

Bottom Line

AI code generation is a productivity tool, not a security control. The best available evidence suggests close to half of AI-written samples carry a well-known weakness, and that waiting for better models is not a plan. Put AI-generated code through the same SSDF-style gates as everything else, add dependency verification and agent permission limits on top, and keep a human accountable for every merge. For a look at one of the most widely used assistants, see our GitHub Copilot review.

Frequently Asked Questions

  • Is AI-generated code less secure than human code? The benchmark data here measures AI output against secure-coding tasks, not against human developers, so it shows AI code is often insecure but does not directly compare it with people.
  • Which vulnerabilities do AI assistants introduce most? In Veracode’s 2025 test, cross-site scripting and log injection had the highest failure rates, at 86% and 88%.
  • Will a newer model fix this? Veracode found security did not improve with newer or larger models, so plan on review and scanning regardless of model.
  • What framework should we follow? NIST’s SSDF (SP 800-218), with the SP 800-218A profile for organizations that build or acquire AI systems.

Sources

Posted in Engineering, software

GitHub Copilot Review: Plans, AI Credits, and Which Tier a Developer Actually Needs in 2026

Get GitHub Copilot: Visit the official GitHub Copilot plans page →

At a Glance

SpecValue
Free planYes — 2,000 inline suggestions/month, limited chat and agent use
Starting paid pricePro, $10/month
Individual tiersFree, Pro ($10), Pro+ ($39), Max ($100) per month
Business / EnterpriseNot priced on the plans page — contact sales
Usage unitGitHub AI Credits (1 credit = $0.01)
Best forDevelopers already on GitHub who want AI completions, chat, and an agent that opens pull requests

Short Verdict

GitHub Copilot’s free tier is a genuine trial of the product, with 2,000 inline suggestions a month and limited chat. For anyone coding most days, Pro at $10 a month is the sensible starting point: it removes the suggestion cap and adds code review and the cloud agent that turns assigned issues into pull requests. Pro+ and Max mostly buy a bigger monthly pool of AI credits plus the ability to hand tasks to third-party coding agents, so they only make sense if you regularly run out of credits on Pro. The big change to understand in 2026 is that usage is now measured in dollar-denominated AI credits rather than a simple request count.

What GitHub Copilot Is

GitHub Copilot is GitHub’s AI coding assistant. It suggests code inline as you type, answers questions in a chat panel, runs an agent mode that can make multi-file changes, works from the command line through the Copilot CLI, and on paid plans can act as a cloud agent: you assign it an issue on GitHub and it works in the background and opens a pull request for you to review.

Pricing Breakdown

PlanPrice/monthBase creditsFlex allotmentTotal monthly credit valueInline suggestions
Free$0Limited chat and agent usage2,000/month
Pro$10$10$5$15Unlimited
Pro+$39$39$31$70Unlimited
Max$100$100$100$200Unlimited
Business / EnterpriseContact sales; not priced on the plans page

GitHub now meters chat, agent, and other advanced features in GitHub AI Credits, with one credit worth $0.01. Each paid plan comes with base credits equal to its price plus a flex allotment on top, so Pro effectively includes $15 of monthly usage, Pro+ $70, and Max $200. The plans page we checked did not list annual pricing for any tier.

Features by Tier

The Free plan already includes the Copilot CLI, agent mode, MCP server support, and custom instructions, but not code review, the cloud agent, or app modernization. Pro adds Copilot code review on pull requests, the cloud agent, app modernization for Java and .NET projects, and support for repository instruction files. Pro+ and Max add delegation to third-party coding agents, such as Anthropic’s Claude and OpenAI Codex, which GitHub labels as a preview; beyond that, the two differ mainly in credit volume. Enterprise-grade security, IP indemnity, SAML SSO, usage metrics, and user management are not part of any individual plan and require Business or Enterprise. One caution: the plans page lists the same set of AI models across every tier but its FAQ says Free users get entry-level models, so check which models you can actually select before relying on a specific one.

Pros

The free tier is usable for evaluation and light coding without a credit card. Pro is inexpensive at $10 a month and includes the features that change day-to-day work: unlimited completions, PR code review, and a cloud agent that works directly inside GitHub issues and pull requests. Copilot sits inside the platform where many teams already host code, so there is no separate tool to connect. The credit system makes heavy usage possible on higher tiers without hard request caps.

Cons

Dollar-denominated credits are harder to predict than a flat request count, because how fast you spend them depends on which features and models you use. Business and Enterprise pricing is not published on the plans page, which makes team budgeting harder without a sales conversation. Third-party agent delegation is still a preview and is only on the two most expensive individual plans. The model-availability wording on the plans page is inconsistent, as noted above.

Who It’s a Good Fit For

Individual developers and freelancers who host their code on GitHub and want completions, chat, and an agent that can pick up issues and open pull requests. Pro covers most of them. Pro+ or Max fit developers who lean heavily on agents and regularly exhaust Pro’s $15 of monthly credit value.

Who Should Look Elsewhere

Teams that need SSO, usage metrics, or IP indemnity need Business or Enterprise and should get a quote before committing. Developers whose code lives outside GitHub lose the cloud agent and PR review integration, which are much of the paid plans’ value, so an editor-based AI assistant may fit better.

Bottom Line

Start on Free to see whether inline suggestions fit how you code, then move to Pro at $10 a month if you code regularly; it is the best value tier and includes the cloud agent and code review. Only step up to Pro+ or Max once Pro’s credit pool is clearly the bottleneck. Teams should treat Business and Enterprise as a sales conversation, since the plans page does not publish their prices.

Frequently Asked Questions

  • Is GitHub Copilot free? There is a free plan with 2,000 inline suggestions a month and limited chat and agent usage.
  • How much is Copilot Pro? $10 a month, which includes $15 of monthly AI credit value ($10 base plus a $5 flex allotment).
  • What is a GitHub AI Credit? The unit GitHub uses to meter advanced features; one credit equals $0.01.
  • How much do Business and Enterprise cost? The plans page does not list prices for them; GitHub directs teams to contact sales.

Sources