10 best open-weight AI inference providers for startups

If you're choosing an open-weight inference provider as a seed to Series A company, you're committing to an infrastructure before you can accurately forecast what you’ll need. You can’t predict next quarter's traffic, product shape, or even which model you'll be running by then.

The best AI inference providers for startups are the ones whose price and performance hold up for your realized workload in the months when your forecast hits the mark and when it misses. Choosing the right provider depends on six variables that go beyond the rate card:

• Cost and utilization: what a token costs once you count idle capacity.
• Latency under your workload: time-to-first-token, generation pace and aggregate throughput, measured at your prompt lengths and load.
• Scaling and reliability: how fast new capacity serves traffic spikes, and what uptime providers commit to.
• Operational burden: the configuration, integration, monitoring and model-loading work your team owns.
• Model portability: the effort it takes to switch models or move fine-tuned weights between providers.
• Forecast and commitment risk: a commitment's term and flexibility, weighed against your expected setup and usage.

Six criteria that decide which open-weight inference provider fits

Each criterion below explains what to measure and how it changes the way you deploy your model. Shared serverless trades latency consistency for zero setup and usage-based billing. Dedicated deployments give you greater control over performance on the model you choose, but typically bill by the GPU-hour (unless you’re running Parasail’s Elastic Endpoints).

Bare metal GPUs and self-managed GPU deployments offer the most control, but also take the most work because your team runs the whole serving stack. Settle the kind of capacity you need first with our guide to choosing a managed inference architecture.

Cost and utilization: calculate what a token costs at your real utilization

Whether to pay per token or per GPU-hour comes down to utilization. GPU-hour pricing can win when the hours you pay for are consistently busy.

Take a meeting-notes app that runs near capacity through the workweek and goes quiet on weekends. An always-on dedicated GPU bills all 168 hours of the week, even though 48 of them are sitting idle. Some providers like Baseten can scale to zero during quiet periods, avoiding ongoing compute charges, but deploying and scaling remain billable. After scaling to zero, the first requests have to wait for the model to start again.

Offline work can move to a batch queue at 20% to 50% off on most providers, but it can't handle the requests your product answers in real time.

There's no universal threshold at which dedicated capacity beats per-token pricing. Model size, hardware generation, the headroom you keep to meet latency targets and KV-cache hit rates all move the break-even.

Our recommendation: Run per token for a representative stretch of your business cycle, then price the same traffic on GPU-hours. For example, two H100s reserved on Together for 91 to 180 days cost $3.19 an hour each, or about $4,657 a month. At Together's serverless rate for gpt-oss-120b, $0.15 per million input tokens and $0.60 per million output, the same budget covers roughly 19.4 billion tokens a month at a 4:1 input-to-output mix. Run above that volume and renting the GPUs is cheaper, as long as you can keep both busy at your latency target. Land at a third of it and they work out to three times the serverless rate.

Latency: which speed metrics does your product depend on

The speed at which your product responds is measured in three numbers:

• Time to first token (TTFT) is the wait between sending a request and receiving the first output token. Longer prompts can increase that wait. For models that stream their reasoning, also measure how long it takes for the actual answer to begin.
• Generation pace is how fast decode streams the rest of the response to one user.
• Aggregate throughput is the total output tokens per second a deployment produces across all users.

Batching lets a system process several requests together. This can increase the total number of tokens produced per second, but larger batches can also slow individual responses. The trade-off depends on the workload and how the system processes prompts and generates replies. A voice assistant needs to start answering quickly and speak smoothly. An overnight document-processing job can prioritize how much work finishes by morning.

Public benchmarks can help you choose which providers to test. Check the test conditions: different prompt lengths or numbers of simultaneous requests can produce different results.

Our recommendation: Set your own limits on TTFT and per-token pace, then replay a sample of your real prompts against each shortlisted provider at your expected peak concurrency and record the request rate each one holds within those limits. Pay attention to the tail p95 to p99, since the median hides the slow requests your users notice.

Scaling and reliability: what happens when a traffic spike demands more capacity

Scaling starts before the first token. When traffic outgrows your warm replicas, a new one has to move model weights out of storage and into GPU memory before it can answer anything. Our engineers measured cold-start latency at 235 seconds for Llama-3.3-70B and 929 seconds for DeepSeek-R1 across eight GPUs, then cut it by 3 to 5 times with faster weight loading.

How many replicas you need depends on goodput, or the highest request rate each GPU can serve while meeting your latency target. DistServe researchers found that serving systems tuned for total throughput must over-provision compute resources to meet latency requirements, and that higher goodput per GPU directly translates into lower cost per query. That makes goodput, not throughput, the number to compare providers on.

Cold-start time and goodput describe how a deployment behaved under the load. Neither one promises that capacity will be there next time, or that the endpoint stays up. That exists only in the contractual uptime providers state. Baseten publishes a 99.9% monthly availability SLA for its dedicated inference, while Parasail commits to 99.9% uptime for Dedicated Deployments.

Our recommendation: Ramp each shortlisted provider from normal traffic to your highest spike, and record how long new replicas take to serve and the highest request rate that still meets your p95 target. Then confirm uptime and capacity commitments in writing before you sign.

Operational burden: a model is only part of the work

A provider's price leaves out the work your team does to keep a model serving, and at a startup that work competes with product engineering. How much you own depends on the serving mode.

Most providers sell one or two: Parasail, Baseten and Fireworks pair a serverless API with dedicated deployments, while GPU clouds like Nebius and CoreWeave add managed inference on top of raw GPUs. The engineering time depends on your workload, so confirm this typical split with each provider.

A managed dedicated endpoint can cost more per GPU-hour than a GPU cloud because the provider takes jobs off your list. Nebius rents an on-demand H100 for $4.50 an hour as of October 1, 2026, while Fireworks charges $8 an hour for an H100 deployment it runs for you, so compare the GPU savings with the added engineering cost for the same workload. Platforms like Modal and RunPod sit further along the same line, cheaper per GPU-hour again but leaving the whole serving stack to you.

The "Shared" cells vary most between providers. For self-service deployments on Baseten and Fireworks, you set replica counts and scaling targets yourself, while Parasail assigns performance engineers to build each Dedicated Deployment and keep tuning it against your SLAs.

Our recommendation: determine the jobs your team can own and when paying a higher rate for inference costs less than hiring more engineers or slowing product work to manage the stack.

Model portability: model choice is not the same as easy model switching

Portability comes down to checks at four levels, the model, the adapter, the serving precision and the message format. An OpenAI-compatible endpoint doesn't automatically solve all of them.

If you've fine-tuned, the first check is whether the provider will serve your own weights. Parasail's dedicated endpoints deploy models from private Hugging Face repositories. If you serve adapters on a shared base model, check support for your exact base model and adapter configuration. The S-LoRA research shows that adapter rank, memory, loading and batching all shape how adapters serve, so one supported setup doesn't guarantee yours.

Check the precision each provider serves at. The same weights quantized to FP8 or FP4 can return different answers, and precision can vary by model, so a provider can run one model at full precision and the next at FP4.

Also, check the message format. Hugging Face's chat-template documentation shows that models from the same base can expect different formats, so find out whether the service applies the template or your application has to.

Our recommendation: before you switch, ask what precision the provider serves your model at, then run your exact base model, adapter configuration and a sample of production prompts through its endpoint and compare the outputs with your current deployment. Keep those prompts as a suite to rerun on every model or provider change.

Forecast and commitment risk: are you committing spend or locking into hardware

A commitment trades flexibility for a discount, and a startup makes that trade before it can forecast what it will run. Missing the forecast raises your effective cost per token without automatically making the commitment the more expensive choice. A rate discounted 40% that you use two-thirds of still works out below list price.

The bigger risk is what the commitment ties you to. A Fireworks reservation fixes a GPU type and count typically for a year, and bills until the term ends whether you use it or not. DeepInfra's DeepCluster commits you to a dedicated cluster for three to five years. If you move to a model or GPU generation those reservations don't match, you keep paying for capacity you've stopped using.

Parasail's flexible commitments keep the discount without locking you into a specific architecture. They draw down across any model or hardware, and unused spend rolls into the next quarter.

Recommendation: before you sign, ask each provider what the commitment is tied to, whether you can move it to a different model or GPU, and what happens to capacity or spend you don't use. Keep the term no longer than you can predict which model and hardware you'll run, since that's the risk a discount doesn’t remove.

The 10 best open-weight AI inference providers for startups

Each card answers which workload fits each provider best, how they bill, what their commitment structure looks like, and what to look out for. Routers such as OpenRouter, LiteLLM, and Portkey hand your request to providers like these, so they aren't on the list.

Parasail

• Best for: dedicated open-weight endpoints billed per-token, with Day 0 support for frontier LLMs and engineering access on every committed deployment.
• Pricing model: per token on Serverless and Elastic Endpoints (early access), per GPU-hour on Dedicated Deployments, and Batch at the lowest per-token rate.
• Commitment structure: no minimum on Serverless. Committed customers sign flexible contracts with a quarterly spend commit that draws down across any model or hardware, with true-up and rollover.
• Watch out for: new organizations start with a quota of four GPUs shared across Batch and Dedicated, and serverless caps each user at 500 requests per minute, so plan a quota request before launch. Parasail isn't a training platform.

Baseten

• Best for: custom and fine-tuned models on dedicated GPUs where latency is the product and enterprises that want inference inside their own cloud or billed against existing cloud commitments.
• Pricing model: a free Basic plan with pay-as-you-go usage, and Pro and Enterprise plans with volume discounts. Dedicated GPUs bill per minute for deploying, scaling and serving, and compute charges stop after scaling to zero. Model APIs bill per token.
• Commitment structure: none required on the Basic plan. Pro and Enterprise add volume discounts, and Enterprise can bill against your existing cloud commitments.
• Watch out for: bills that are hard to predict under variable traffic, a recurring complaint in G2 reviews. Replica startup is billable, and the default scale-down settings add a 15-minute delay after the autoscaling window, so tune autoscaling before launch.

Cerebras

• Best for: coding agents and real-time reasoning where output speed decides the product.
• Pricing model: a free trial with $5 in credits and tight limits, then pay-as-you-go per token. Cerebras Code subscriptions run $50 and $200 a month, both listed as sold out. Dedicated endpoints and batch go through sales.
• Commitment structure: pay-as-you-go by default, with Code as a monthly subscription. Dedicated endpoints reserve capacity on terms set with sales.
• Watch out for: Users report hitting limits and errors on pay-as-you-go, and cached tokens count toward total tokens per minute. As of September 2026, the public catalog is two models, and six others have been retired since February.

CoreWeave

• Best for: teams that already hold CoreWeave reserved GPU capacity and want to point part of it at inference, egress-heavy workloads, and custom weights on a GPU class you choose.
• Pricing model: serverless inference billed per token through W&B Inference, from $0.03 per million input tokens on gpt-oss-120b. Dedicated Inference bills per GPU-hour against your chosen GPU class, with no ingress or egress fees. On-demand compute is priced by the eight-GPU node, at $49.24 an hour for an HGX H100, and storage bills separately.
• Commitment structure: on-demand or reserved. Reserved capacity cuts up to 60% off on-demand rates, and existing reserved nodes can be redirected to Dedicated Inference at contracted rates.
• Watch out for: self-serve only goes so far. Serverless runs on Weights & Biases, with default spending caps of $100 a month on the free tier and $6,000 on Pro. Dedicated Inference starts with a sales call, and either way your model artifacts have to live in CoreWeave object storage.

DeepInfra

• Best for: low per-token price on open-weight models, cheap embeddings and batch, and zero-retention inference in US data centers.
• Pricing model: per token, with Priority scheduling at 1.5x the base rate and Flex at 0.8x. Batch runs 20% below real-time. Custom models bill per GPU-hour in minute increments, invoiced weekly.
• Commitment structure: no long-term contract for the API or custom deployments. DeepCluster commits you to 256 to 5,000 B300 GPUs for three or five years.
• Watch out for: Priority, which schedules requests ahead of standard traffic for faster time to first token, costs 1.5x the base rate, which made Parasail cheaper on two of three Priority models in our DeepInfra comparison. Many models also run quantized, some at FP4, so check each model page's precision before you pick on price.

Fireworks AI

• Best for: coding products that need new open models fast and teams that fine-tune a model and serve it at low latency with zero data retention.
• Pricing model: per token in Standard, Priority and Fast tiers, with batch at half the Standard serverless rate. Supervised and preference fine-tuning bills per training token, and on-demand GPUs bill per second. Spend tiers from $50 to $50,000 set your rate limits.
• Commitment structure: none for serverless or on-demand GPUs. Enterprise accounts can reserve GPUs, typically for a year, billed until the term ends.
• Watch out for: serverless models get retired on short notice. Fireworks posted its latest removals 13 days before they took effect. Dedicated instances aren't affected, but idle ones scale to zero after an hour and return errors while they restart.

Groq

• Best for: fast, cheap speech-to-text for voice products, fast gpt-oss inference, and prototyping on a free tier.
• Pricing model: a free plan with low rate limits, then a pay-as-you-go Developer plan billed per token, with batch at 50% off. Some models, including Llama 3.3 70B, are available only on enterprise contracts.
• Commitment structure: pay-as-you-go on the Developer plan, and committed-spend contracts for enterprise customers.
• Watch out for: a shrinking self-serve catalog. Groq retired its Llama 3.1 8B and Llama 3.3 70B from the free and Developer plans in August 2026, keeping them for enterprise contracts, and preview models "may be discontinued at short notice."

Hugging Face Inference Endpoints

• Best for: deploying any model from the Hub, including private and gated ones, onto AWS, Azure or GCP without running multi-cloud infrastructure, and teams that need SOC 2 Type 2 compliance or a private link.
• Pricing model: hourly instance rates billed by the minute, from $0.033 an hour for a CPU and $0.50 for a T4, up to multi-GPU A100 and H200 instances. You pay while an endpoint is initializing as well as running. PRO costs $9 a month, and Team starts at $20 per user per month.
• Commitment structure: pay as you go with a card on file, billed monthly. Enterprise adds custom annual contracts, volume pricing and uptime guarantees.
• Watch out for: scaling from zero. Hugging Face's docs say it "can take a few minutes depending on the model" and is "typically not recommended if your application needs to be responsive," and its FAQ tells you to run at least two replicas to avoid it. There's no published uptime percentage outside enterprise agreements.

Nebius

• Best for: inference that has to stay in EU or US data centers, high-volume batch jobs, and startups that want token APIs and GPU clusters from one vendor.
• Pricing model: per token on Token Factory in Base and Fast flavors, with batch at 50% off. Dedicated endpoints bill per GPU-hour while a replica runs. Nebius AI Cloud rents an H100 for $4.50 an hour on demand as of October 1, 2026. Storage bills separately, and the first payment has a $25 minimum.
• Commitment structure: pay-as-you-go by default. Reserving clusters for multiple months cuts up to 35% off on-demand rates.
• Watch out for: self-serve dedicated endpoints don't guarantee capacity above your minimum replicas, and they carry no formal SLA unless a contract adds one. The 99.9% SLA with reserved capacity sits on Nebius's enterprise tier.

Together AI

• Best for: one account for serverless inference, fine-tuning and GPU clusters, for a team that expects to move from tokens to its own cluster.
• Pricing model: prepaid credits with a $5 minimum. Serverless bills per token, batch runs up to 50% off, and fine-tuning bills per training token. Dedicated endpoints bill per second, at $5.49 per H100 hour. GPU clusters come preemptible, on demand or reserved.
• Commitment structure: prepaid credits by default. Reserved H100 clusters run from $3.69 an hour for 7 to 30 days down to $3.19 for 91 to 180 days, and longer terms go through sales.
• Watch out for: rate limits that move. Together sets serverless limits from each model's live capacity and your recent usage, may throttle sudden spikes, and points teams that need a fixed limit to dedicated endpoints.

The best AI inference provider for each workload

These picks come from each provider's published rates and from where its endpoints sit on public speed rankings today. Public benchmarks don’t replace testing your own weights against the criteria above.

Speed and price rankings move as providers change hardware and rates. Check the current numbers on Artificial Analysis and price your own workload before you sign anything. GPU rates from each provider's pricing page, September 2026.

Test your workload with Parasail

Start on per-token capacity, run it through a full business cycle, and let that traffic decide what you commit to. If you want dedicated performance while you find out, Parasail's Elastic Endpoints bill per token, and serverless has no minimum spend.