4G mobile exits for cellular-anchored evaluation
Real carrier LTE sessions with live SIMs and rotating CG-NAT public IPs. Used by AI teams testing mobile-first response paths, cellular content policy on model APIs, and regional carrier-level variation.
- Pool type
- Live carrier SIMs
- Countries
- 10 (focused)
- Carriers
- Tier-1 per country
- Rotation
- Per-request or sticky (1–30 min)
- Protocols
- HTTP, HTTPS, SOCKS5
- Uptime SLA
- 99.5%
Why 4G matters for AI — and why it usually doesn't
4G mobile is premium infrastructure. Per-gigabyte it is 5–20× the cost of datacenter. For AI data collection, most of the volume does not justify that premium. Mobile is the right tool in a narrow set of cases where IP-layer authenticity and carrier/mobile fingerprinting matter.
Cases where 4G is correct
- Cellular content policy on model APIs. Some commercial LLM providers apply different content policy to requests that originate from mobile carrier ASNs in specific regions — not dramatically, but measurably. If your eval is measuring policy boundaries, you want a sample from carrier exits.
- Mobile-first rendering paths. Some news, social, and review sites serve materially different HTML to mobile UA + mobile ASN combinations. For RAG ingestion that wants the mobile-rendered variant, only a mobile exit gets you there reliably.
- Regional carrier variation. In some regions — notably APAC and parts of the Middle East — cellular networks peer differently to the open internet than residential broadband. A model evaluation that matters for a mobile-first deployment market wants cellular-origin traffic in the eval mix.
Cases where 4G is wrong
- Training corpus collection. Bandwidth economics alone disqualify mobile for TB-scale work. Use datacenter.
- Latency-sensitive eval loops. 4G adds 50–150ms over datacenter and jitter that breaks timing-sensitive tests.
- Multi-turn agent sessions. Mobile IPs rotate aggressively on CG-NAT. Sticky sessions help but top out at 30 minutes and sometimes fail mid-session as the SIM shifts towers.
Pool shape
- Live SIMs. We operate carrier-leased SIMs in managed multi-SIM modem arrays across our 10 focus countries. Not SDK-sourced mobile proxies — those have reliability and provenance issues that make them unsuitable for eval methodology.
- Carrier targeting. Tier-1 carrier per country: AT&T, Verizon, T-Mobile in the US; EE, Vodafone, Three, O2 in the UK; Deutsche Telekom, Vodafone DE, O2 DE in Germany; etc.
- Tower distribution. We distribute SIMs across metro and non-metro tower coverage so the IP diversity looks plausible across a campaign.
Session semantics
4G CG-NAT means multiple sessions share an IP at any given moment. Per-request rotation at the gateway level gives you a new IP reliably; sticky sessions bind you to a specific SIM for 1–30 minutes. Cycling SIMs (rather than IPs) is how we rotate without breaking cellular context.
Pricing
Pricing for 4g mobile
Every plan includes the 4g mobile pool across every country we operate.
Solo
For individual researchers running evaluation scripts and prototype RAG pipelines.
$149/ month
or $1,430/year (save 20%)
50 GB residential · unlimited datacenter · 200 concurrent sessions
- ✓Access to all 5 exit classes · 10 focus countries
- ✓50 GB residential · unlimited datacenter
- ✓5 static ISP IPs · 5 GB 4G mobile
- ✓1 seat · 200 concurrent sessions
- ✓Python + Node SDK + REST API
- ✓Per-request metering (not time-based)
- ✓Email support (24h response, business days)
- ✓Overage: $3/GB residential · $6/GB mobile
Best for
- Solo researchers
- Evaluation scripts
- Prototype RAG
Team
Most popularFor AI startups and mid-size labs splitting capacity between training and evaluation.
$699/ month
or $6,710/year (save 20%)
500 GB residential · unlimited datacenter · 1,000 concurrent sessions
- ✓Access to all 5 exit classes · 10 focus countries
- ✓500 GB residential · unlimited datacenter
- ✓25 static ISP IPs · 25 GB 4G mobile
- ✓10 seats ($29/mo per extra seat) · 1,000 concurrent sessions
- ✓City-level geo-routing + ASN targeting
- ✓99.9% uptime SLA
- ✓Priority Slack support (4h response, business hours)
- ✓Python + Node SDK + REST API + webhooks
- ✓Overage: $3/GB residential · $6/GB mobile
Best for
- AI startups
- Mid-size labs
- Model eval teams
Lab
For academic labs, eval consortia, and frontier model companies running sustained workloads.
$2,999/ month
or $28,790/year (save 20%)
2 TB residential · unlimited DC · 50 GB 4G + 20 GB 5G · 3,000 concurrent sessions
- ✓Access to all 5 exit classes · 10 countries on 4 continents
- ✓2 TB residential · unlimited datacenter
- ✓100 static ISP IPs · 50 GB 4G + 20 GB 5G mobile
- ✓50 seats ($19/mo per extra seat) · 3,000 concurrent sessions
- ✓Dedicated gateway lane (bypasses shared-pool queues on us-east-1 + eu-west-1)
- ✓99.95% uptime SLA
- ✓Dedicated Slack channel (1h response, business hours)
- ✓Custom BGP prefix on request (additional fees apply)
- ✓Overage: $2.50/GB residential · $5/GB mobile
Best for
- Academic labs
- Large eval consortia
- Frontier model companies
Enterprise
Custom contracts with dedicated infrastructure, volume pricing, and research-grade SLAs.
Custom pricing
Custom (from 5 TB/mo residential) · unlimited concurrent sessions
- ✓Volume pricing from 5 TB/mo residential
- ✓Dedicated BGP prefix + ASN announcement
- ✓Unlimited concurrent sessions · unlimited seats
- ✓99.99% uptime SLA with financial credits
- ✓Named Technical Account Manager + 24/7 on-call paging
- ✓Custom AUP, DPA, on-site deployment option
- ✓Research / academic discount (30–50% off Team or Lab)
- ✓Annual contract · wire, ACH, USDC/USDT/BTC settlement
Best for
- Frontier labs
- Eval consortia
- Enterprise AI
All plans include 14-day refund, single endpoint with regional failover, HTTP(S) + SOCKS5 on every exit class, access to all 5 exit classes and all 10 focus countries, and Python + Node SDKs. Concurrent sessions = simultaneous TCP sessions through the gateway. Overage warnings fire at 80% and 100%; traffic continues only if overage billing is enabled on your account.
Workloads that use 4g mobile
Use cases where this exit class is recommended
AI & Machine Learning
LLM Evaluation Across Regions
GPT, Claude, Gemini, and open models respond differently depending on IP-layer geography. SquadProxy gives you evaluation origins across 10 countries on residential, ISP, and mobile exits so your eval methodology reflects real deployment conditions.
AI & Machine Learning
Safety and Red-Team Testing
Testing model guardrails across jurisdictions, accessing geoblocked content to build adversarial sets, and running safety evaluations that reflect the real geographic distribution of end-users. Used by safety teams at labs, third-party auditors, and compliance review.
FAQ
4G Mobile Proxies FAQ
When should an AI team route through 4g mobile proxies instead of the other classes?
4G Mobile exits are the right tool when cellular-anchored model evaluation, mobile-first content path testing, regional carrier variation studies. For other AI workloads, check the residential, ISP, datacenter, or mobile pages — each exit class sits on a different tradeoff.What rotation and session settings do 4g mobile proxies support?
Rotation is Per-request or sticky (1–30 min). Sticky windows are set via the X-Squad-Session header.Which countries are covered in the 4g mobile pool?
4G Mobile coverage is live across 10 (focused). The US, GB, DE, FR, JP, NL, CA, SG, KR, AU pools are our most instrumented.Are 4g mobile proxies suitable for continuous LLM evaluation pipelines?
Yes — the 4g mobile pool is used in production for evaluation workloads against GPT, Claude, and Gemini APIs, particularly when the eval depends on cellular-anchored model evaluation. Our Team and Lab plans include the concurrency needed for sustained runs.
Start routing through 4g mobile
Real ASNs, real edge capacity, and an engineer who answers your Slack the first time.