The Tool Desk
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The shortlist below is a set of fit hypotheses, not a ranking from a controlled benchmark. HostScore’s comparison, updated August 20, 2026, explicitly says it did not run a controlled cross-provider LLM test. Use the decision process here to validate a provider with your own model and workload.
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What “LLM hosting” can mean
People use LLM hosting for several different products. They are not interchangeable, and their billing and operational responsibilities differ.
Shared, provider-hosted inference API
You send prompts to a provider’s already-running models through an API and pay according to the provider’s usage meter, usually tokens. This is the quickest route to production and avoids renting or maintaining GPUs. The trade-off is that you are limited to the provider’s supported models, versions, context policies, and product terms.
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Managed model endpoint
You select a public, gated, private, or custom model and deploy it through a managed service. The platform handles part of the infrastructure while exposing controls for replicas, scaling, and runtime configuration. Scale-to-zero can save money on idle endpoints, but loading weights and initializing the runtime creates a cold start that may be unacceptable for interactive requests.
Custom serverless GPU inference
You supply inference code, a container, and a model-loading strategy; the service starts GPU workers when requests arrive. This gives more control over the engine and deployment logic than a shared API, while avoiding a permanently running machine. Startup time, image size, weight download time, and idle-time rules become part of both latency and cost.
Persistent or dedicated GPU capacity
You rent a GPU instance, pod, or dedicated deployment that remains available. This suits sustained traffic, strict runtime control, or workloads that cannot tolerate model initialization on the first request. You pay for capacity while it is allocated, including quiet periods, so utilization and autoscaling policy determine whether the arrangement is economical.
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1. Which exact model must run?
Record the model name, release or revision, license, and whether you need public weights, a gated download, private weights, or a fine-tune. A provider may support a model family but not the particular revision, architecture, adapter, or custom code your application uses. Treat model support as a deployment requirement, not a marketing category.
2. What precision and context length are required?
Write down the intended precision or quantization and the maximum prompt-plus-completion context. Lower-precision weights can reduce memory needs, but the compatible inference engine and quality target still matter. A host that accepts the model at one quantization may not support the exact format you have validated.
3. What does traffic look like?
Estimate requests per minute, peak concurrency, burst duration, and the share of batch or offline work. Bursty traffic favors a serverless design when startup delays are acceptable. A steady stream of concurrent requests often makes warm replicas or persistent GPUs easier to operate and predict.
4. What latency and reliability target is real?
Separate time to first token from sustained generation speed. Decide whether a cold start is acceptable, what rate-limit behavior your client can retry, and whether a fallback model or second endpoint is required. There is no cross-provider latency winner established by the available evidence, so benchmark your complete application rather than a provider’s generic claim.
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List data residency, retention, encryption, private networking, identity integration, audit logging, and procurement requirements. Confirm these for the exact service and region you will use. A technically suitable endpoint is not suitable if its data-handling terms do not meet your obligations.
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6. What is the complete cost unit?
For token APIs, include input and output tokens, retries, caching, and any minimum commitment. For GPU services, include GPU-seconds or hours, minimum replicas, idle billing, storage, data transfer, startup overhead, and the utilization you can actually sustain. A token price cannot be compared directly with a GPU-hour price without assumptions about throughput and utilization.
Provider shortlist: where each option fits
The following descriptions reflect HostScore’s August 20, 2026 editorial comparison. They are starting points for validation, not measured rankings.
| Option | Reported fit | Trade-off to verify |
|---|---|---|
| Runpod | Flexible serverless workers and persistent Pods, with substantial runtime control | More configuration and security/compliance checking; worker startup and idle-time rules affect billed time |
| Hugging Face Inference Endpoints | Hub-native managed deployment for public, gated, or private models | Scale-to-zero can introduce model-initialization delays; interactive workloads may need warm capacity |
| Modal | Python-led custom inference systems and deployment logic | Your team chooses the engine, container, loading strategy, and scaling settings |
| Together AI | Begin with shared inference and move to dedicated capacity for supported models or fine-tunes | Dedicated replicas bill while running, including quiet periods |
| Fireworks AI | Shared inference plus dedicated deployments, including custom models and LoRA variants | Confirm support for the exact deployment and its terms; the comparison reports no uptime or latency SLA for serverless |
| Amazon Bedrock | Hyperscaler integration for teams already operating in AWS | Check current model availability and pricing; ecosystem integration is not a lowest-cost guarantee |
Runpod: control when you can operate the runtime
Choose this path when you need both on-demand workers and the option of persistent Pods, or when you need to tune the runtime more deeply than a shared API allows. Plan for container hardening, network and identity configuration, model storage, worker startup, and the provider’s idle-time billing behavior. It is a stronger fit for teams that can own those decisions than for a team seeking a fully abstracted API.
Hugging Face Inference Endpoints: managed deployment close to the model Hub
This option is natural when the model is public, gated, private, or stored in a Hub workflow and you want a managed endpoint rather than a hand-built GPU service. Scale-to-zero reduces spend when traffic stops, but the first request after sleep may wait for the image, weights, and runtime to initialize. Keep capacity warm when the user experience cannot tolerate that delay, and measure the resulting idle cost.
Modal: code-first custom inference
Modal fits Python-oriented teams that want to define loading, preprocessing, batching, and scaling in code. The platform supplies building blocks; your team still selects the inference engine, creates the container, decides how weights are cached, and sets concurrency and scale rules. This flexibility is valuable for unusual pipelines, but it transfers more design and debugging work to the application team.
Together AI: a path from shared traffic to dedicated capacity
A shared endpoint can be a practical starting point for supported models and fine-tunes. If demand becomes predictable, dedicated replicas provide more control over availability and throughput. Because replicas are billed while running, model the quiet periods and minimum replica count before switching; sustained utilization is what can justify the move.
Fireworks AI: shared and dedicated deployments with adapter options
Fireworks AI is worth evaluating when you need shared inference or a dedicated deployment for a custom model or LoRA variant. Verify that the exact model, adapter, quantization, and serving mode are supported under the terms you will sign. The cited comparison does not report an uptime or latency SLA for its serverless offering, so define your own monitoring and fallback requirements.
Amazon Bedrock: choose it for an existing AWS operating model
Bedrock can reduce integration friction when identity, networking, logging, procurement, and billing already run through AWS. Treat model catalog and rate-card checks as a release requirement: availability and prices change, and being in the same cloud does not prove a lower total cost than another deployment type.
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How the cost picture changes by architecture
CloudZero’s comparison, published September 22 and updated September 23, 2026, reported Llama 3.3 70B input rates from $0.10 to $1.04 per million input tokens across the providers in its table. The figures were checked by that publisher in August–September 2026 and are a dated secondary-source snapshot, not a live quote. They do not establish output pricing, equal precision, equal throughput, or equal service quality. Rate cards can change within weeks, so verify the exact model variant and both input and output rates before committing.
| Billing approach | Costs that belong in your estimate | Common distortion |
|---|---|---|
| Shared token API | Input tokens, output tokens, retries, caching, and any commitment or minimum | Comparing input-only prices while ignoring completion tokens and retries |
| Managed endpoint | Running replicas, scale-to-zero transitions, storage, data transfer, and initialization overhead | Assuming scale-to-zero is free when cold-start delays cause retries or poor conversion |
| Serverless GPU workers | GPU execution time, startup and image-loading time when billed, storage, and idle timeout behavior | Using average traffic and overlooking short bursts that trigger repeated worker startup |
| Persistent GPU | Allocated GPU time, minimum replicas, storage, transfer, and unused capacity | Comparing a full GPU-hour with a token price without a measured tokens-per-second rate |
Build a workload estimate from your own traces: total input tokens, total output tokens, request distribution, peak concurrency, and acceptable first-token latency. For GPU options, add measured throughput at the chosen precision and concurrency, then divide allocated cost by completed requests or tokens. Include failed requests and retries; a lower nominal rate can lose its advantage if it causes more reprocessing.
A practical selection procedure
- Freeze the workload definition. Record model revision, quantization, context limit, average and maximum output, requests per minute, peak concurrency, and batch share.
- Classify traffic. Mark it as bursty, steady, offline, or mixed. Note whether a request can wait for a cold start and whether a queue is acceptable.
- Shortlist service types. Start with a shared API for fastest integration, a managed endpoint for a specific Hub or private model, serverless GPU for custom code, or persistent capacity for sustained load and runtime control.
- Check hard constraints. Verify model and adapter support, precision, context, regions, networking, data handling, authentication, rate limits, and maximum concurrency for the exact product.
- Model the full bill. Use current provider rate cards for token, GPU, replica, storage, transfer, and minimum-runtime charges. Keep the CloudZero range as historical context only.
- Run an application-level pilot. Send representative prompts, measure time to first token, generation throughput, error and retry rates, cold-start frequency, and quality at your chosen precision.
- Set failure behavior. Define timeout values, retry limits with backoff, queue limits, circuit breaking, and a fallback model or endpoint. A provider choice is incomplete without an outage and capacity plan.
- Recheck before launch. Confirm live pricing, model catalog, regional capacity, service terms, and any uptime commitment on the date you deploy.
Choosing by workload pattern
| Your situation | Starting point | What to validate first |
|---|---|---|
| Prototype or unpredictable bursts | Shared inference API or serverless workers | Supported model, rate limits, cold-start tolerance, and retry behavior |
| Public, gated, or private model from a Hub workflow | Managed inference endpoint | Revision and quantization support, scale-to-zero behavior, and warm-capacity cost |
| Custom preprocessing, batching, or inference engine | Code-first serverless GPU | Container startup, weight caching, concurrency settings, and observability |
| Predictable, sustained interactive traffic | Persistent GPU or dedicated deployment | Measured utilization, replica minimums, autoscaling limits, and idle spend |
| AWS-standard security and operations | Amazon Bedrock | Current regional model availability, pricing, quotas, and integration requirements |
Operational pitfalls and fixes
Cold starts become user-visible
Symptom: the first request after inactivity is much slower or times out. Fix: keep a minimum warm capacity, send a controlled warm-up request, increase client timeouts only when appropriate, or route interactive traffic to a continuously running replica while leaving batch work on scale-to-zero workers.
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The selected model loads but cannot sustain concurrency
Symptom: single-request tests look good, but queue time rises at peak. Fix: benchmark with the target concurrency and context length, measure tokens per second and time to first token separately, then increase replicas, use batching, or select a smaller quantization only after checking quality.
A low headline price produces a high monthly bill
Symptom: actual spend exceeds a token or GPU estimate. Fix: reconcile input and output tokens, retries, minimum replicas, startup time, idle periods, storage, and transfer against provider logs. Recalculate cost per completed request rather than cost per advertised unit.
Deployment depends on an unsupported variant
Symptom: the model family is listed, but your revision, adapter, or quantization is rejected. Fix: verify the exact artifact and runtime before migration; keep a tested fallback model or endpoint and avoid assuming family-level support covers custom weights.
Privacy and procurement checks arrive too late
Symptom: a technically successful pilot cannot be approved. Fix: collect region, retention, encryption, private networking, identity, audit, and contract requirements at the shortlist stage and test them in the same account and region intended for production.
What a defensible 2026 decision looks like
Document the chosen model revision and precision, traffic assumptions, latency target, concurrency test, provider configuration, current rate-card date, and fallback plan. Keep shared APIs, managed endpoints, serverless GPUs, and persistent capacity in separate comparison tracks; selecting among them is an architectural decision before it is a vendor decision.
Revisit the choice when the model changes, context grows, traffic becomes steady, or a provider changes pricing or capacity. The best host is the one that meets your measured quality and latency requirements at an acceptable cost while satisfying your governance and operational constraints.
Quick Recap
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