AI visibility report
AI visibility report for Sference in LLM Inference & Serverless GPU.
Outside the top three on 23 of the 25 prompts buyers actually ask.
RunPod is cited on 10 of those losses.
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Start free trialStill absent from 100% of tracked prompt responses
Top-3 citations across 125 prompt × platform pairs
Peer Ranking
Key Metrics
Platform Breakdown
ChatGPT
Google AI Mode
Gemini Search
Bing Copilot
PerplexityHow to read this. Sference appears in 0% of tracked prompt responses. Presence is absolute coverage; share of voice is relative citation share; sentiment measures tone only when the brand appears.
Where Sference is losing
Prompts where competitors are visible and Sference is not.
These prompt-level losses are the first prompts to track and repair.
Where Sference is winning
No clear strengths identified yet.
Where Sference is losing5
What LLM inference platforms offer the best SDK and API ergonomics for a Python-first engineering team shipping a conversational AI feature?
Competitors on 3 platforms
Track this promptWhich serverless GPU inference platforms have the lowest cold-start latency for a customer-facing chat app that needs sub-second first-token response times?
Competitors on 3 platforms
Track this promptWhich serverless GPU inference platforms have the best developer experience for iterating quickly on prompt templates and sampling parameters without redeploying?
Competitors on 3 platforms
Track this promptWhat are the fastest serverless GPU inference platforms to go from an open-source LLM to a live production API endpoint with no GPU infrastructure to manage?
Competitors on 3 platforms
Track this promptWhich serverless GPU platforms give engineering teams the most visibility into per-request latency and token throughput right out of the box?
Competitors on 2 platforms
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Track these gapsResearch dossierCapabilities, use cases, sources, reviews, pricing, and FAQ
Overview
Sference is an early-access async AI inference platform built for regulated EU industries. It aggregates excess and preemptible GPU capacity across multiple EU providers into a federated compute pool, enabling batch workloads to run at up to 75% below real-time inference costs by trading latency for savings. Two delivery windows are offered — Priority (~1 hour) and Overnight (~24 hours) — alongside support for open-weight models from the Qwen, Mistral, and Llama families and bring-your-own fine-tuned models compatible with vLLM or SGLang. An OpenAI-compatible batch API and CLI tool ease integration. Sference's core differentiation is combining spot-GPU economics with EU data sovereignty, full compliance audit trails, DORA and EU AI Act readiness, and BYOM — targeting SaaS companies in FinTech, LegalTech, HealthTech, and InsureTech whose customers require regulatory auditability.
Sference is an async batch AI inference service running on federated EU spot and preemptible GPU capacity. It delivers up to 75% cost savings versus real-time inference by accepting configurable latency trade-offs, and combines EU data sovereignty, an OpenAI-compatible batch API, BYOM for fine-tuned models, and a compliance runtime (audit trails, DPA, DORA/AI Act readiness) in a single platform aimed at regulated EU SaaS verticals.
Key Facts
- HQ
- EU
- Founders
- Jernej Strasner, Aleksander Pejcic, Benjamin Dobnikar
- Status
- Private (Early Access)
Target users
Key Capabilities10
- Async batch AI inference on federated EU spot and preemptible GPU capacity
- Delivery windows: Priority (~1 hr, up to 50% off) and Overnight (~24 hr, up to 75% off)
- Bring-your-own-model (BYOM): upload fine-tuned weights, loaded per job and released after completion
- OpenAI-compatible batch API with JSONL-based CLI submission tool
- Hardware-agnostic GPU federation across multiple EU providers with no single-vendor dependency
- Fault-tolerant batch orchestration with checkpoint resumption on spot-instance preemption
- EU data residency: all requests processed on EU GPUs, zero US CLOUD Act exposure
- Compliance runtime: full request audit trail, configurable retention, exportable reports, DPA included
- DORA enforcement readiness and EU AI Act (August 2026 deployer obligations) readiness built in
- On-demand model loading per batch job — no persistent GPU memory reservation required
Key Use Cases8
- Batch KYC extraction and transaction classification for FinTech compliance pipelines
- Contract corpus analysis and document review for LegalTech
- Medical record digitization and clinical data extraction for HealthTech
- Insurance claims processing and underwriting document analysis
- Large-scale model evaluations and synthetic data generation for AI/ML teams
- Fine-tuning dataset preparation on sensitive or proprietary data
- Invoice, contract, and form processing at scale for document-heavy workflows
- Embedding generation for legal and regulated-domain RAG systems
Recent Trend
How AI describes Sference
No concise AI response excerpt is available for this brand yet.
Most cited sources
No cited source mix is available for this brand yet.
Alternatives in LLM Inference & Serverless GPU6
Sference targets the intersection of async batch AI inference, EU data sovereignty, and regulatory compliance — a combination it claims no single competitor offers in full.
- While US-based platforms such as Together AI and Modal Labs provide batch APIs or spot-GPU economics, Sference differentiates on three axes: (1) federated EU-only GPU infrastructure eliminating US CLOUD Act exposure; (2) bring-your-own-model (BYOM) support for fine-tuned weights with the same compliance guarantees as catalog models; and (3) compliance tooling — full audit trail, exportable reports, DPA, DORA and EU AI Act readiness — built into the runtime rather than added post-hoc.
- It positions as purpose-built for regulated EU SaaS verticals (FinTech, LegalTech, HealthTech, InsureTech) rather than as a general-purpose inference platform.
Reviews
No third-party reviews are available. Sference is in early access and has no presence on G2, Gartner Peer Insights, or other public software review platforms as of the research date.
Pricing
Three tiers billed per token consumed; no credit card required and no minimum spend. Dev Mode: real-time delivery at full price, intended for prompt iteration and testing. Priority: ~1-hour delivery at up to 50% off real-time rates. Overnight: ~24-hour delivery at up to 75% off real-time rates. Specific per-token rates are not published on the website.
Limitations
- Not suitable for real-time or low-latency applications (chat interfaces, live agents, interactive products).
- EU-only infrastructure limits global deployment options.
- Pre-launch / early access status means no production track record, published SLAs, or independent performance benchmarks are available.
- Per-token pricing rates are not disclosed on the website.
- Model catalog limited to open-weight Qwen, Mistral, and Llama families plus BYOM; closed-model APIs (e.g.
- GPT-4o) are not supported.
- Spot and preemptible capacity means scheduling is non-deterministic within stated delivery windows.
Frequently asked questions
Topic coverageCoverage by buyer topic
Topic Coverage
Prompt-Level Results
| Prompt | ChatGPT | Google AI Mode | Gemini Search | Bing Copilot | Perplexity |
|---|---|---|---|---|---|
Capability0/5 cited (0%) | |||||
What LLM inference platforms handle streaming token responses well and support long context windows for document-processing use cases? | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited |
Which LLM inference platforms let enterprise teams bring their own fine-tuned model weights and enforce strict data isolation with private deployments? | A competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited |
Which serverless inference platforms support running large multimodal models — handling both text and image inputs — on high-end GPUs at production scale? | A competitor was cited | A competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited |
Which serverless GPU platforms support batch inference jobs for offline processing pipelines in addition to real-time API endpoints? | Neither your brand nor a competitor was cited | A competitor was cited | A competitor was cited | A competitor was cited | A competitor was cited |
I'm looking for an inference platform that supports custom CUDA kernels and speculative decoding — what are my options for a performance-critical chatbot? | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited |
Developer Experience0/5 cited (0%) | |||||
Which serverless GPU platforms give engineering teams the most visibility into per-request latency and token throughput right out of the box? | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited |
What are the best LLM serving platforms for a small ML team that needs built-in request logging and usage dashboards without wiring up a separate observability stack? | A competitor was cited | A competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited |
What LLM inference platforms offer the best SDK and API ergonomics for a Python-first engineering team shipping a conversational AI feature? | A competitor was cited | A competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited |
Which serverless inference platforms make it easiest to manage multiple open-source model versions in parallel across staging and production environments? | Neither your brand nor a competitor was cited | A competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited |
Which serverless GPU inference platforms have the best developer experience for iterating quickly on prompt templates and sampling parameters without redeploying? | A competitor was cited | A competitor was cited | A competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited |
Integrations & Ecosystem0/5 cited (0%) | |||||
Which LLM inference platforms integrate natively with vector database services for building retrieval-augmented generation pipelines without extra glue code? | A competitor was cited | A competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited |
What serverless GPU inference providers work best alongside AI orchestration frameworks so teams can chain model calls and tool use cleanly? | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited |
Which inference platforms expose an API compatible with the standard chat completions format so switching providers requires minimal code changes? | Neither your brand nor a competitor was cited | A competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited |
What LLM inference platforms integrate with cloud object storage for loading large model weights at deploy time without manual upload steps? | A competitor was cited | A competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited |
Which serverless GPU platforms support webhook callbacks or event-driven triggers for async inference jobs in a data pipeline built on a workflow orchestrator? | Neither your brand nor a competitor was cited | A competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited |
Performance & Reliability0/5 cited (0%) | |||||
Which serverless inference providers deliver the highest tokens-per-second throughput for a high-volume API serving thousands of concurrent users? | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited |
Which serverless GPU inference platforms have the lowest cold-start latency for a customer-facing chat app that needs sub-second first-token response times? | A competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited |
What are the most reliable LLM serving platforms for an enterprise use case that requires 99.9% uptime SLAs and geo-redundant deployments? | A competitor was cited | A competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited |
What LLM inference platforms can handle sudden traffic spikes — say 10x burst load — without throttling for a mid-sized SaaS product? | A competitor was cited | A competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited |
Which serverless GPU platforms have the best cost-per-token at scale for a startup burning significant GPU budget on a document summarization product? | A competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited |
Setup & First Run0/5 cited (0%) | |||||
Which LLM inference platforms have the easiest onboarding for a solo developer deploying a fine-tuned open-source model for the first time? | A competitor was cited | A competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited |
Which LLM inference platforms support deploying quantized open-source models with minimal setup for a backend engineer with no MLOps background? | A competitor was cited | A competitor was cited | A competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited |
What's the quickest serverless GPU platform to get an image-generation model behind a REST API with autoscaling out of the box? | A competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited |
I'm evaluating serverless inference platforms for a small startup — which ones let you deploy a custom open-source LLM without writing any infrastructure config? | Neither your brand nor a competitor was cited | A competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited | Neither your brand nor a competitor was cited |
What are the fastest serverless GPU inference platforms to go from an open-source LLM to a live production API endpoint with no GPU infrastructure to manage? | A competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited |
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Track prompt changesVertical Ranking
| # | Brand | PresencePres. | Share of VoiceSoV | DocsDocs | BlogBlog | MentionsMent. | Avg PosPos | Sentiment |
|---|---|---|---|---|---|---|---|---|
| 1 | RunPod | 15.2% | 29.5% | 1.6% | 0.0% | 40.0% | #4.2 | +0.32 |
| 2 | Fireworks AI | 12.0% | 17.0% | 1.6% | 6.4% | 36.8% | #3.4 | +0.46 |
| 3 | Beam | 11.2% | 17.9% | 0.0% | 0.0% | 12.0% | #4.8 | +0.29 |
| 4 | Baseten | 8.8% | 16.1% | 6.4% | 3.2% | 42.4% | #2.6 | +0.50 |
| 5 | Modal | 8.0% | 9.8% | 0.0% | 2.4% | 0.0% | #3.2 | +0.55 |
| 6 | Together AI | 5.6% | 7.1% | 2.4% | 0.8% | 44.8% | #2.1 | +0.30 |
| 7 | Cerebrium | 2.4% | 2.7% | 0.8% | 0.0% | 8.0% | #2.0 | +0.60 |
| 8 | Lepton AI | 0.0% | 0.0% | 0.0% | 0.0% | 0.0% | — | — |
| 9 | Replicate | 0.0% | 0.0% | 0.0% | 0.0% | 32.8% | — | — |
| 10 | Sference | 0.0% | 0.0% | 0.0% | 0.0% | 0.0% | — | — |
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