Key Takeaways
- Cost-Efficient Resource Pooling: Multi-tenant architecture and continuous batching eliminate GPU idle time, enabling true pay-per-token pricing.
- Guaranteed Data Security: Tenant data remains strictly isolated through stateless tokenization and per-request Key-Value (KV) cache boundaries.
- Tail Latency Trade-Off: While average (P50) speeds remain stable, queuing competition during multi-tenant traffic surges can cause worst-case (P99) latency to spike.
- Workload-Based Decision: Opt for Shared if traffic is bursty or low-volume; switch to Dedicated for sustained high utilization, strict latency SLAs, or hardware-level compliance needs.
Shared inference endpoints allow multiple teams or users to query a single Large Language Model (LLM) simultaneously without data overlap. By pooling traffic across a multi-tenant architecture, serverless inference eliminates GPU idle time and allows developers to pay only for the tokens they actually consume.
The Problem: Dedicated GPU Waste
If your team has ever provisioned a dedicated H100 GPU for an AI workload that peaks for just two hours a day and idles for the remaining twenty-two, you understand the core inefficiency of traditional hosting. A dedicated GPU doesn’t care whether you are processing five requests a second or zero; you pay for the hardware either way.
The Solution: Multi-Tenant Serverless Inference
Shared inference flips that mathematical equation. Instead of reserving a dedicated GPU solely for your workload, your requests join a multi-tenant traffic pool. An underlying AI scheduler dynamically decides, request by request, how to pack that combined traffic onto available compute resources.
This is the exact architecture powering every major "pay-per-token" API on the market. Whether you use OpenAI, Anthropic, or the serverless infrastructure tiers of platforms like Simplismart, you use a shared endpoint.
Why Engineers Choose Shared Endpoints
For engineering teams evaluating LLM infrastructure for cost optimization, the appeal of serverless endpoints is undeniable:
- Near-Zero Idle Costs: You stop paying for unused compute capacity.
- Instant Scalability: The system handles traffic spikes automatically without manual load balancing.
- No Hardware Bottlenecks: You bypass GPU shortages and procurement lead times entirely.
Under the Hood: What You Need to Know Before Production
While the benefits are clear, engineers need to understand what actually happens beneath the API call before committing production traffic to a shared model. When evaluating a serverless inference provider, you need to account for three critical technical factors:
- Continuous Batching: How the underlying scheduler efficiently batches your tokens with another tenant's traffic without causing bottlenecks.
- Data Security & Isolation: The architectural boundaries that guarantee your proprietary data and context windows never leak into another tenant’s request.
- Latency Trade-offs: Understanding exactly where the "cost of sharing" shows up, particularly regarding Time to First Token (TTFT) and overall generation speed during peak global traffic hours.
What is a Shared Inference Endpoint?
At its core, a shared inference endpoint is a single machine learning model deployment, utilizing one set of weights loaded onto one or more GPUs, that simultaneously processes requests from multiple independent tenants, applications, or customers. Instead of provisioning separate hardware and model copies for every single user, all incoming traffic is dynamically routed to the same running instance.
Infrastructure platforms, such as Simplismart, position shared endpoints as the ultimate cost-effective solution for teams that want to invoke ML models without paying for idle hardware.
The mechanism that makes this multi-tenant sharing viable is how the hardware handles the traffic. A shared endpoint is not just a slow, first-in-first-out queue where requests wait their turn. Instead, GPU memory and compute are shared dynamically at the batch level.
Here is why that architectural distinction matters:
- Merged Forward Passes: Multiple requests from different tenants are actively grouped together and processed through the model in the exact same forward pass.
- Enterprise-Grade Throughput: By interleaving traffic at the compute level rather than stacking it in a waiting line, shared endpoints deliver the same optimized throughput-per-dollar ratios utilized by major API providers.
- The Dedicated Alternative: If a workload cannot tolerate shared compute, the alternative is a dedicated endpoint. This trades the sheer cost-efficiency of a shared pool for exclusive infrastructure, guaranteeing isolated performance, predictable latency, and absolute reliability.
How Continuous Batching Drives LLM Efficiency
To understand why shared endpoints are so cost-effective, you first need to break down the two-phase structure of every LLM inference request:
- The Prefill Phase: The model processes the entire input prompt simultaneously across every layer. This pass produces the very first output token and generates a Key-Value (KV) cache for every single prompt token.
- The Decode Phase: The model generates subsequent output tokens one at a time. Each forward pass reads the accumulated KV cache to produce exactly one new token.
The Bottleneck: Static Batching
The naive approach to handling multi-tenant traffic is static batching: collecting a set number of requests and running them together until every single one finishes before starting the next batch.
The fatal flaw here is that real-world requests rarely finish at the same time. If one request in your batch requires 2,000 tokens while the rest finish at 100, the GPU sits idle waiting for the longest task. Real-world workload measurements reveal a massive 60–80% padding overhead under static batching, with Streaming Multiprocessor (SM) utilization often stagnating around 30–40%.
The Fix: Iteration-Level Continuous Batching
Continuous batching (also known as iteration-level batching, initially proposed in the Orca paper) eliminates this hardware waste. Instead of operating per batch, the scheduler operates per decoding step.
At every single decoding iteration, the scheduler dynamically adds new requests to the batch as compute slots free up and instantly removes completed sequences. This approach is now the industry standard across major serving frameworks like vLLM, TGI, and TensorRT-LLM. Because this happens entirely on the backend, developers get massive throughput gains without having to change a single line of client-side code.
The practical impact on shared endpoints:
- Interleaved Processing: A request from Team A and a concurrent request from Team B do not wait in a serial queue. They are interleaved into the same GPU forward passes, with each generation step processing a fraction of both.
- Simultaneous Latency & Throughput Gains: Anyscale’s original benchmarking revealed that continuous batching delivers up to a 23x throughput improvement while simultaneously reducing p50 latency. Both metrics improve because the GPU
hardware stops sitting idle. This is exactly why shared endpoints can be priced drastically lower than dedicated GPU rentals.
Advanced Optimization: Chunked Prefill
Newer architectural refinements push this efficiency even further. Chunked prefill, implemented as SplitFuse in DeepSpeed-FastGen and stall-free batching in Sarathi-Serve, splits massive prompts into smaller chunks.
These chunks are scheduled alongside ongoing decode requests. For a shared endpoint, this is a critical safeguard: it ensures that one tenant sending a massive 10,000-token document-processing prompt cannot stall every other tenant’s chat generation mid-response.
Data Security & Tenant Isolation on Shared GPUs
When evaluating shared infrastructure, security is usually the first question an engineering team asks: If requests from multiple tenants are merged into the same batch and processed through the same forward pass, what prevents Tenant A's data from leaking into Tenant B's output?
It is a critical question. The answer lies in the architecture; true isolation happens at a foundational layer below the batching logic. Here are the three structural guarantees that keep tenant data secure:
- Stateless Tokenization: Tokenization and prompt processing are handled strictly on a per-request basis. No shared session context or memory persists across requests from different callers. Tokenization is a deterministic, stateless mapping applied completely fresh to every single incoming prompt.
- Isolated KV Cache Allocation: The KV cache (the attention keys and values computed during the prefill phase) is what allows decode steps to attend back over a prompt’s own tokens. Even when using memory-efficient frameworks like PagedAttention, which allocates the KV cache in fixed-size, non-contiguous blocks to reduce fragmentation, each sequence’s blocks are logically owned by that sequence alone. Batching multiple sequences into one GPU kernel call is purely a compute optimization; it does not allow one sequence to read another’s cached keys. In fact, transformer attention mathematics explicitly requires per-sequence computation; if a model attended across unrelated sequences, it would output complete gibberish.
- Platform-Layer Governance: On top of the compute layer, the platform itself enforces strict account-level separation. Enterprise platforms like Simplismart explicitly build their infrastructure to enforce data residency, multi-tenant isolation, and performance SLAs without slowing developers down. Behind the scenes, API routing, authentication, quotas, and token-level logging are securely scoped per API key. Your compute is batched, but your account context remains entirely firewalled.
The Compliance Trade-Off: Shared vs. Dedicated Endpoints
If your workload involves highly sensitive proprietary information, strict data residency laws, or compliance frameworks like HIPAA, the batching-level isolation of shared infrastructure is mathematically sound, but it can make for a complicated compliance audit.
This is precisely why dedicated endpoints exist. A dedicated endpoint eliminates the shared compute question entirely, providing exclusive infrastructure with zero multi-tenant overlap. It is a simpler, cleaner compliance story to present to regulators, trading the extreme cost-efficiency of shared batching for absolute, physical hardware isolation.
Latency Trade-Offs: Where Queuing Overhead Actually Shows Up
The primary cost of utilizing shared LLM infrastructure is not a lack of compute power; it is queuing delay. On a shared endpoint, your request will occasionally arrive when the GPU’s current batch is already full, or when a sudden burst of multi-tenant traffic temporarily saturates the AI scheduler's queue.
This queuing overhead impacts performance asymmetrically. It rarely affects your average generation speed, but it can significantly stretch your worst-case generation times.
The Divide Between P50 and P99 Latency
This dynamic is a well-documented pattern in inference serving research. Published benchmarking consistently shows that median (p50) latency remains remarkably stable under bursty traffic, while tail (p99) latency climbs sharply. The tail is driven by long prompts and unpredictable burst arrivals colliding in the queue, not by the average request.
Consider a concrete inference-benchmarking scenario measuring Time to First Token (TTFT):
- At 32 concurrent requests, a system achieved 980 tokens/sec throughput with a p99 TTFT of ~520ms. This is a highly balanced, performant state.
- Pushing concurrency to 64 requests doubled the throughput to 1,450 tokens/sec, but the p99 TTFT spiked to 1.8 seconds.
The operational implication is clear: if your application has a strict Service Level Agreement (SLA) requiring sub-one-second responses, your concurrency ceiling is dictated by that tail latency, even if the raw throughput of the GPU could handle more traffic.
Expected Latency Ratios on Shared Endpoints
While exact figures depend on your specific workload and provider, the table below illustrates the standard framework for how latency behaves under different traffic loads on shared infrastructure:
Note: Enterprise platforms like Simplismart provide native SLA layers that track real-time P50/P95 latency, pod startup times, and throughput by model to help engineers monitor these exact thresholds.
Actionable Advice for Engineering Teams
When evaluating shared endpoints, never benchmark against a single "average latency" number listed on a vendor's marketing page.
You must measure the p99 latency at the exact concurrency level and burstiness your production traffic actually produces.
- If your workload features steady, predictable request arrivals, shared-endpoint latency will behave almost identically to a highly expensive dedicated GPU.
- If your workload features sharp, unpredictable spikes (e.g., viral marketing moments, end-of-quarter batch jobs, or dynamic chat applications), shared-endpoint queuing overhead will become visible. During these bursts, your traffic is actively competing with every other tenant for the exact same scheduler slots.
Cost Analysis: Shared Endpoints vs. Dedicated GPUs
When evaluating LLM infrastructure, the decision ultimately comes down to unit economics. Simplismart’s published pricing models provide a perfect case study for comparing serverless, pay-per-token pricing against dedicated hardware.
- Shared Endpoints (Pay-Per-Token): You are billed solely for generated tokens. Current rates include $0.13 per 1M tokens (Llama 3.1 8B), $0.74 per 1M tokens (Llama 3.1/3.3 70B), and $3.00 per 1M tokens (Llama 3.1 405B).
- Dedicated GPUs (Flat Hourly Rate): You pay for the hardware regardless of utilization. Rates range from $1.20/hour for a T4 and $4.00/hour for an H100, up to $5.20/hour for an H200.
The Crossover Framework: When to Switch
At what usage volume does it make financial sense to stop paying per token and rent the GPU outright? The answer depends entirely on your achieved throughput (tokens generated per GPU-hour).
Instead of looking for a single magic number, engineering teams should evaluate their workloads against these three traffic tiers:
Tier 1: Light, Bursty Traffic
- The Winner: Shared Endpoints.
- Why: If you only process a few requests per minute with unpredictable spikes, a dedicated GPU will spend most of its time sitting idle. Pay-per-token pricing wins decisively because you only pay for actual generation, and continuous batching ensures your latency remains competitive.
Tier 2: Steady, Moderate Traffic (The Crossover Zone)
- The Winner: It depends on utilization.
- Why: To find your break-even point, multiply your model’s sustained tokens/sec by 3,600 to calculate your hourly token capacity. Then, divide your dedicated GPU hourly cost by that capacity (in millions). If this effective $/1M-token cost undercuts the shared endpoint rate, and you can keep the GPU constantly busy, dedicated hosting becomes cheaper. If utilization drops, shared remains the better deal.
Tier 3: High, Sustained, Latency-Sensitive Traffic
- The Winner: Dedicated Endpoints.
- Why: At massive scale, the decision shifts from raw cost-per-token to strict Service Level Agreements (SLAs). Dedicated hardware guarantees your traffic won't compete with other tenants' bursts. Furthermore, bulk GPU reservations often secure lower effective hourly rates, making dedicated deployments highly cost-competitive while ensuring predictable p99 tail latency.
The Simplismart Ecosystem: India-Hosted Open-Weight Inference
For teams building AI applications in regions with strict data residency requirements, Simplismart’s India-hosted, NVIDIA-backed infrastructure provides localized access to top-tier open-weight models.
However, when architecting your application, it is critical to distinguish between models that are Generally Available (GA) and those in closed beta. Shared endpoints only deliver peak cost and latency benefits once a model is fully integrated into the production continuous-batching stack.
Current Model Availability on Simplismart
The Infrastructure Takeaway:
Do not budget or build against a beta model’s future pricing and latency profile before it reaches GA. Models in closed beta have not yet undergone the aggressive multi-tenant hardening, such as FP8 quantization, advanced KV-cache management, and multi-node tensor parallelism, that makes shared endpoints so efficient. Wait for the official GA release to lock in your production SLAs.
Choosing Between Shared and Dedicated: A Practical Checklist
Boil the decision down to a few concrete questions before defaulting to either option:
- Is your traffic bursty or steady? Bursty, low-average-volume traffic almost always favors shared endpoints on cost, since you're never paying for idle GPU time.
- What's your actual p99 latency requirement, and at what concurrency? If you need a hard latency guarantee under load, that's a dedicated-endpoint argument regardless of raw cost math, because shared infrastructure's queuing behavior is, by design, subject to other tenants' traffic.
- How close to full GPU utilization would your sustained volume actually get you? Run the tokens-per-hour math from the section above with your real throughput numbers, not assumed ones; this is where the switch from cost-optimal shared to cost-optimal dedicated actually happens.
- Does your compliance posture require exclusive compute, independent of whether the underlying isolation is technically sound? Some regulatory or contractual requirements specify dedicated infrastructure by name, in which case the isolation-guarantees discussion above is academic; you need the dedicated endpoint regardless.
- Is the model you need actually GA on the platform you're evaluating, or still in beta? Confirm this before architecture decisions lock in around a model that isn't production-ready yet on your chosen provider.
None of these questions has a universally right answer; that's precisely why both endpoint types exist on the same platform rather than one replacing the other. The right call is workload-specific, and it's worth re-running the cost and latency math above every time your traffic pattern changes materially, rather than treating the shared-vs-dedicated decision as a one-time setup choice.
Conclusion
Choosing between shared inference endpoints and dedicated GPUs is not a one-size-fits-all decision; it requires continuously aligning your infrastructure with your specific workload dynamics.
- Opt for Shared Endpoints if your traffic is bursty or low-to-moderate. The pay-per-token model and continuous batching allow you to eliminate idle compute costs and scale instantly.
- Opt for Dedicated Endpoints if you have high, sustained traffic that maximizes GPU utilization, strict P99 latency SLAs, or rigorous regulatory compliance that mandates physical hardware isolation.
Ultimately, the best choice is highly dependent on your current scale and requirements. Engineering teams should regularly re-calculate their unit economics, latency thresholds, and throughput rather than treating the shared-versus-dedicated debate as a one-time setup decision.
Frequently Asked Questions
What exactly is a shared inference endpoint?
A shared inference endpoint is a single deployed model (running on one or more GPUs) that processes requests from multiple independent users simultaneously. Instead of provisioning separate hardware for each user, traffic is dynamically pooled and routed to the same running instance, allowing developers to pay only for the tokens they consume.
Is my proprietary data secure if it is processed on the same GPU as another company's data?
Yes. While compute power is shared, true isolation happens beneath the batching logic. Security is guaranteed through three mechanisms:
- Stateless Tokenization: No session context or memory persists across requests.
- Isolated KV Cache: The memory blocks holding your prompt's attention keys are logically owned by your sequence alone.
- Platform Governance: Strict account-level firewalls manage API routing, authentication, and quotas.
How do shared endpoints handle traffic efficiently without causing massive queues?
They rely on a technique called continuous batching (or iteration-level batching). Instead of waiting for an entire batch of requests to finish before starting the next one, the AI scheduler dynamically adds new requests and removes completed ones at every single decoding step. This eliminates GPU idle time and drastically increases throughput.
What is the main performance trade-off when using shared infrastructure?
The primary cost of sharing is queuing delay, which impacts your tail (P99) latency. During sudden, bursty traffic spikes from multiple tenants, your requests compete for scheduler slots. While average (P50) generation speeds usually remain stable, the worst-case generation times can stretch significantly during peak loads.
When does it make financial sense to switch to a dedicated GPU?
You should switch to a dedicated GPU when your workload shifts from light and bursty to high and sustained. To find your break-even point, calculate your effective cost per 1 million tokens on a dedicated GPU (based on your actual sustained throughput and the hourly rental rate). If that number is lower than the platform's pay-per-token rate, and you can keep the GPU constantly busy, a dedicated endpoint is cheaper. Dedicated endpoints are also required if you have strict sub-second P99 latency SLAs or strict hardware-level compliance needs (like HIPAA).
Should I build my application around the pricing of beta models?
No. You should wait until a model is designated as Generally Available (GA). Models in closed beta have not yet received the aggressive multi-tenant optimizations (like FP8 quantization and advanced KV-cache management) required to deliver the peak cost and latency benefits of a shared endpoint.
Ready to eliminate GPU waste and optimize your LLM inference?
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