LLM Inference Cost Comparison 2026: DigitalOcean vs. Together AI, Fireworks AI, Modal, Nebius, Baseten, and OpenRouter

The lowest-cost inference provider depends on the model, token distribution, latency requirements, batch eligibility, and measured cache-hit rate. The following table compares five production scenarios using publicly listed prices and clearly defined workload assumptions.

Prices are listed in US$ / 1 million tokens. Estimates do not include taxes, failed or retried requests, network fees, vector storage costs, or negotiated discounts. Prices and model availability are subject to change by the provider. Verified linked pricing pages for up-to-date prices.

DigitalOcean is competitive for real-time open-model inference or any application looking to colocate inference, storage, databases, and retrieval infrastructure. Fireworks is well-suited for batch jobs and repeated prefixes. Together offers an expansive model catalog along with serverless and dedicated deployment paths.

Modal and Nebius matter if you want to actually deploy and optimize a model onto metered GPU infrastructure rather than buying a token-priced API. Baseten offers both per-token Model APIs and paths for dedicated and self-hosted deployment. OpenRouter is an aggregation/routing layer whose total cost depends on the selected model/provider price plus its platform-fee rules. OpenAI provides direct access to proprietary GPT models and is retained below only as a pricing context.

The table below therefore, compares purchasing models and directs readers to each provider’s live pricing page.

OpenAI and Anthropic are good conceptual price anchors for proprietary models; however, they are contextual model-lab comparisons rather than members of the primary inference-platform competitor set used here.

The graphic below breaks down how text-generation API pricing is calculated based on tokens consumed as input/output. Additional calculations are shown based on caching tokens and using other service features.

Keep this formula in mind when shopping around for LLM inference providers, but make sure to review each provider’s up-to-date pricing for caching, tooling/function calls, web searches, storage, regional usage, and dedicated infrastructure.

Consider gpt-oss-120b.The cheapest LLM API depends on the balance between input and output tokens. Using 10,000 input tokens and 100 output tokens, DigitalOcean would cost about $0.00107, which is about 31% cheaper than Together AI / Fireworks at $0.00156. Using 100 input tokens and 10,000 output tokens, Together AI / Fireworks would cost approximately $0.00 6015, which is about 14% cheaper than DigitalOcean at $0.00701.

DigitalOcean has a lower cost for input-intensive workloads, and Together AI and Fireworks have a lower cost for output-intensive workloads. The providers reach the break-even point when the input-token volume is twice the output-token volume.

The table provides a comparison of typical serverless input/output prices for DigitalOcean, Together AI, Fireworks AI, and Baseten, directly listing the same model. Modal and Nebius are included in the table above for platforms, as their deployment economics change based on metered compute utilization; OpenRouter is included as an example routing layer, as its effective price is based on the selected model/provider route and platform fee rules. OpenAI is shown only in a proprietary model pricing context. Prices were verified on July 30, 2026, and may change over time.

Important pricing qualifications

• The three numbers in Fireworks’ pricing indicate uncached input / cached input/output.
• For GPT-5.6 Luna, Terra, and Sol, the displayed prices apply to prompts of no more than 272,000 tokens.
• DigitalOcean states that its OpenAI (commercial-model) integration uses customers’ OpenAI API keys, with billing done by OpenAI.
• Claude Sonnet 5’s $2/M input and $10/M output prices are introductory. On September 1, 2026, Anthropic’s published standard prices become $3/M input and $15/M output.

This comparison uses directly published serverless list prices and excludes batch discounts, priority tiers, negotiated contracts, taxes, storage, network charges, failed requests, and dedicated GPU deployments. Verify the linked pricing pages before making purchasing or architecture decisions.

Note that this table is not attempting to be a quality ranking. Two providers may be serving checkpoints with different quantization, context windows, throughput, or reliability. Identical model names from the same provider do not guarantee operational equivalence. Record complete model identifiers and benchmark with the same prompts, sampling parameters, output length caps, concurrency, and evaluation dataset.

Batch inference processes files or groups of requests asynchronously. Results do not need to be returned immediately, so providers can schedule work more optimally and charge less. Batch works well for classification, metadata extraction, moderation, evaluations, offline summarization, synthetic-data generation, embedding pipelines – basically anything that’s not interactive chat or latency-sensitive agents.

DigitalOcean’s Inference pricing documentation explicitly states “up to 50%” for supported OpenAI and Anthropic models. Their Batch Inference guide details the workflow, the 24-hour expected completion, and explicitly states the commercial models’ supported scope. The Serverless Inference overview plainly states token-based serverless billing and when you’d want to use that over dedicated inference. Fireworks AI states that batch input/output is priced at 50% of serverless pricing, making it cheaper than a provider’s real-time price. OpenAI’s Batch API similarly offers 50% lower costs than synchronous API processing for supported models and endpoints.

When you’re evaluating operationally, compare more than the percentage. Test file size limits, job quotas, completion timeouts, cancellation, partial failures, ordering of results, idempotency, retries, observability, etc.

The following brief example demonstrates how you might use the prices from the comparison table above with an actual workload. For instructions and guidance on when and how to use batch vs real-time inference, see DigitalOcean’s Introducing Unified Batch Inference and The LLM Inference Trilemma: Throughput, Latency, Cost. These articles explain batch implementation and the trade-offs involved in choosing an inference endpoint, while this page provides current pricing comparisons and workload calculations.

Suppose a company must classify one million support tickets before the next business day. The model returns a category, urgency score, and short explanation.

Assumptions:

• 1,000,000 documents
• 800 input tokens and 30 output tokens per document
• 800 million input tokens and 30 million output tokens total
• gpt-oss-120b
• No prompt-cache discount
• Fireworks uses batch rates
• DigitalOcean and Together use standard serverless rates because the DigitalOcean open-model batch discount is not assumed.

The following example demonstrates the cost savings of classifying 1 million documents with gpt-oss-120b under standard serverless rates and discounted batch inference pricing. Fireworks Batch reduces the total price to $69, given the workload assumptions below.

If you’re a researcher performing inference over large datasets, Fireworks Batch offers the lowest estimated cost for this example. This means that batch inference can have significant cost savings for research use cases that aren’t time-sensitive (such as classifying datasets, generating synthetic data, model evaluation, or large-scale document analysis). However, researchers should always evaluate token price alongside model versions, output quality, reproducibility, time-to-completion, failure recovery, rate limiting, and more.

Let’s work through another example using a customer-support chatbot that needs to stream responses. We can’t use batch inference in this case because customers demand responses immediately.

Assumptions:

• 10,000 requests per day * 30 days = 300,000 requests per month
• 1,200 input tokens per request * 300,000 requests per month = 360 million input tokens per month
• 300 output tokens per request * 300,000 requests per month = 90 million output tokens per month
• gpt-oss-120b

Without prompt caching

A chatbot accepting 10,000 requests/day using gpt-oss-120b will cost $99/month on DigitalOcean and $108/month on Together or Fireworks before caching, tools, retries, and the infrastructure around that.

How prompt caching changes the winner

Chatbot inputs include repeated system instructions, user safety policy, tool definitions, output schema, example interactions, and product context. Assume 70% of the 360 million monthly input tokens qualify for Fireworks’ published $0.015 cached-input rate:

• 108 million uncached input tokens
• 252 million cached input tokens
• 90 million output tokens

Prompt caching can significantly reduce your LLM inference by reusing prompts with common content such as system instructions, safety policies, tool definitions, and output schema definitions. Below, we demonstrate how a 70% cache-hit rate reduces a chatbot’s estimated monthly cost from $108 to $73.98.

This is not intended to be an apples-to-apples cache comparison; It reveals sensitivity to a published discount. You must verify each provider’s cache support for the specific model, minimum prefix length, lifetime, routing behavior, cache write cost, and whether prefixes can be shared between users. Note the resulting cache-hit ratio. A timestamp that frequently updates, user-specific data, or a dynamically generated list of tools near the start of the prompt can inhibit reuse.

A retrieval-augmented generation system has three main costs:

1. Converting documents into embeddings.
2. Storing and searching those embeddings in a vector database.
3. Sending the retrieved information to an LLM to generate answers.

Consider you have 100,000 documents with 1,000 tokens each. This equals 100 million tokens. At $0.009 per million tokens for all- MiniLM-L6-v2, the initial embedding cost would be: 100 million tokens×$0.009=$0.90

Creating embeddings for all 100k documents only costs $0.90! This doesn’t mean the entire RAG system will cost $0.90. Additional expenses may include Document storage, vector database hosting, query embeddings, reranking, LLM input/output tokens, and document re-indexing.

Let’s pretend your application receives 1 million questions per month, with each question averaging 20 tokens. The query embeddings will embed 20 million tokens per month: 20×$0.009=$0.18

Most of the cost will probably come from using the LLM. If the RAG system successfully retrieves 2,000 tokens for each question the user asks, then those million questions will send two billion retrieved tokens to the model. At $.10 / million tokens for input, the retrieved context will cost: 2,000×$0.10=$200

This $200 only applies to the retrieved context sent to the model. The price of the user’s questions and the tokens the model generates are not included.

Retrieval settings can thus impact the final bill. The more documents you retrieve, and the larger and more overlapping your chunks are, the more tokens you send to the LLM. Metadata filters and reranking can reduce irrelevant output before generation occurs. Evaluate retrieval accuracy, answer quality, and cost together.

Verdict: Generating embeddings for 100,000 documents with 100 million tokens only costs $0.90 here. The LLM generation and vector-database infrastructure will likely cost far more in a production RAG system than this initial upfront cost for document embeddings.

For Llama 3.3 70B, DigitalOcean charges $0.65 per million input tokens and $0.65 per million output tokens. Together AI charges $1.04 per million tokens for both input and output. Let’s imagine our chatbot uses 360 million input tokens and generates 90 million output tokens per month. Our monthly total volume would be: 360+90=450 million tokens

Because each provider bills input and output tokens at the same rate, our monthly price for each price can be easily calculated:

DigitalOcean: 450×$0.65=$292.50

Together AI: 450×$1.04=$468

DigitalOcean would cost $175.50 less per month for this workload: $468−$292.50=$175.50. This translates to savings of 37.5% compared with Together AI’s $468/month price tag. However, this calculation only compares token prices. It does not indicate if both providers meet the same latency, throughput, reliability, or quality of generated text. To properly compare vendors, the same model version, prompts, generation settings, and workload should be run on both.

DeepSeek could refer to the distilled version trained on top of Llama, the large mixture-of-experts model, the Flash version, or the Pro version. For instance, here are the prices DigitalOcean lists for the DeepSeek model:

• DeepSeek R1 Distill Llama 70B: $0.99 input and $0.99 output per million tokens.
• DeepSeek V4 Flash: $0.112 input and $0.224 output.
• DeepSeek V4 Pro: $1.392 input and $2.784 output.

Note that these models all have different capabilities, architectures, and prices and therefore should not be clustered into a generic “DeepSeek” label. Remember to log the full model identifier + version when comparing providers.

The model with the lowest cost per token may not result in the lowest cost per completed task. A cheaper but less capable model may produce incorrect answers, require longer prompts, take multiple retries, or need human intervention. Benchmarking by price per token is important, but users should also consider total cost to get an acceptable result when comparing providers. Infographic showing how to measure true cost per successful AI task by accounting for input, output, retry, and tool costs:

For sustained use cases, dedicated inference could outperform token billing. DigitalOcean is advertising an AMD MI300X at $2.59/hour and an NVIDIA H100 at $4.41/hour.

However, a GPU-hour price doesn’t tell you much without measured throughput and utilization. A helpful break-even calculation is to divide the hourly cost of an endpoint by measuring successful tasks per hour to compare against the serverless cost per successful task.

Price alone shouldn’t determine your choice of inference provider. Consider how the following aspects of their operations might affect your production cost and performance more heavily:

• Rate limits and capacity: Low prices aren’t valuable if your application can’t get enough requests or tokens per minute. Look into defaults, approved quotas, burst behavior, concurrency limits, and whether higher quotas require committed usage. Microsoft Azure AI Together provides dynamic, per-model limits that scale with sustained traffic. Batch queues may have different file and job quotas.
• Latency and cold starts: Measure time to first token for interactive applications, including inter-token latency, end-to-end latency, P95 and P99, timeouts, and cold starts. Good average latency may obscure a damaging tail. For example, a one-second delay may be catastrophic in a chat interface but irrelevant for overnight batch jobs.
• Reliability and quality: Track completed transactions rather than HTTP 200 responses. Count rate-limit retrials, invalid JSON, incorrectly formed tool calls, empty completions, safety blocks, hallucinations, and failures at your application layer. Compare suppliers using the same evaluation set. Test repeatedly at concurrency levels you expect to see in production.
• Model and serving differences: The same family name can mask different versions, quantization formats, context windows, kernels, and decoding defaults. Pin full identifiers when possible. Providers may update aliases or retire checkpoints, so maintain regression tests and a controlled upgrade procedure.

Practical cost optimization spans pricing decisions, workload design, model routing, retrieval quality, measurement, and operational discipline. Here is a practical framework to help reduce expenses without compromising reliability or quality of answers.

There is no cheapest LLM API across all production workloads in 2026. As of price verification on July 29th, DigitalOcean has the lowest listed standard input rate for gpt-oss-120b among providers, compared to a significant listed-price advantage for Llama 3.3 70B. That example chatbot costs $99/month on DigitalOcean vs. $108/mo on Together or Fireworks prior to caching effects. For chatbot volume on Llama 3.3 70B, DigitalOcean is $292.50 compared to Together’s $468.

Fireworks offers unbeatable value when your eligible workloads can use its batch or cached-input pricing tiers. The 1 million-document example cost drops from $138 at standard Fireworks rates to $69 with Batch Pricing, and assuming a 70% cache-hit rate, reduces the chatbot estimate to $73.98. Together remains competitive in catalog and deployment flexibility, and it comes out ahead on selected models like Qwen 3.7 Plus. Baseten fits in this direct Model API comparison, where it lists the same model. Modal and Nebius need utilization-aware compute benchmarking. OpenRouter requires route- and fee-aware costing. OpenAI is being used as a proprietary model price anchor, not as a primary inference-platform competitor in this comparison.

Once you have measurable constraints representative of production, the durable purchasing principle is simple: optimize for cost per successful task rather than cost per million tokens in isolation. Measure model version, input/output mix, cache-hit ratio, latency distribution, retries, quality, surrounding infrastructure, and run a small production-shaped benchmark. Choose the provider (or routing strategy) that gives you the lowest reliable price under actual application constraints.

What is the cheapest LLM API in 2026?

There is no universally cheapest API. In this comparison, DigitalOcean is the cheapest for the stated real-time gpt-oss-120b and Llama 3.3 70B workloads; Fireworks is the cheapest for the eligible gpt-oss-120b batch and high-cache scenarios; Together wins selected catalog rows. Baseten is directly comparable on overlapping Model APIs; Modal and Nebius must be evaluated from measured compute utilization; OpenRouter must be evaluated using its selected route and applicable fee; and OpenAI is appropriate when proprietary GPT capabilities are required.

How should I compare LLM API prices?

Multiply input and output volumes by their respective rates, add cached tokens, retries, tools, and infrastructure, then divide by successful tasks. Use identical prompts, model versions, output limits, concurrency, and quality thresholds across providers.

Can batch inference cut LLM costs by 50%?

Yes, for eligible models and workloads. Fireworks publishes a 50% batch discount on serverless input and output. OpenAI publishes separate batch rates at half standard rates for supported models. DigitalOcean advertises up to 50% on supported OpenAI and Anthropic models, not every open model.

When is dedicated inference cheaper than serverless?

Dedicated capacity can be cheaper under sustained, predictable demand and high utilization. Benchmark successful tasks per GPU-hour on the target hardware, including idle time and operations, and compare that value with the serverless cost per successful task.

• Inference Pricing
• Scalable, Cost-Efficient AI: Introducing Unified Batch Inference on DigitalOcean
• Together AI Pricing In 2026: Models, Costs, And How To Manage Your Bill