Best OpenAI-compatible inference APIs: drop-in alternatives for 2026

An inference API is OpenAI-compatible when it accepts the same requests and returns the same responses as OpenAI’s API. In practice, that means three things: it uses the same request and response format as OpenAI’s /v1/chat/completions endpoint (and ideally /v1/embeddings and /v1/models too), it handles authentication the same way (an API key sent as a bearer token in the Authorization header), and as a result the official OpenAI SDKs work against it without any changes, you just point them at a different server. Switching means changing two values:

client = OpenAI(
    base_url="https://<provider-endpoint>/v1/",  # changed
    api_key=os.getenv("PROVIDER_API_KEY"),        # changed
)
# everything else stays the same

That’s the promise. In reality, no provider is 100% compatible. Some support more of OpenAI’s API than others, and the gaps usually show up in edge cases, not in basic requests. Three areas break most often in production:

Tool calling works differently across providers. Tool calling (also called function calling) is the feature where you describe your application’s functions to the model as JSON schemas, and the model responds with “call this function with these arguments” instead of plain text. It’s the mechanism that powers agents. It’s also the newest and most complex part of the OpenAI API, which makes it the part providers copy least consistently: OpenAI keeps changing it (strict structured-output mode, parallel tool calls, how arguments stream chunk by chunk), and each provider’s copy reflects a different point in time. A concrete example: your agent framework sets strict: true in a tool definition, which on OpenAI guarantees the model’s arguments exactly match your schema. A provider that silently ignores that flag returns arguments with a missing or mistyped field. The request doesn’t error, but your code crashes when it tries to use the result. If you run agents, this is the first thing to test.

Streaming responses don’t behave identically. Streaming is how chat apps show text appearing word by word: instead of waiting for the full answer, the server sends it in small chunks over one connection (a format called server-sent events). All providers use the same basic chunk format, but the details around the edges differ.

Three examples. OpenAI historically didn’t include token counts (usage stats) in a streamed response unless you asked for them with stream_options={"include_usage": true}, while some providers always send them, extra data your code may not expect. If you set a stop sequence (a string that tells the model where to stop, like "###"), some providers include that string at the end of the returned text while OpenAI cuts it off, so your output has trailing characters you didn’t want. And OpenAI now has a newer endpoint called the Responses API alongside the original Chat Completions endpoint; support for it varies (some providers offer both, others only Chat Completions), so if your code is written against Responses, check before assuming it will work.

Not every endpoint and parameter exists everywhere. OpenAI’s API is more than chat: there are separate endpoints for embeddings (turning text into vectors for search), batch processing (submitting many requests at once at a discount), and audio (speech-to-text and text-to-speech). Every provider covers chat; coverage of the rest varies, so check each endpoint you use. Embeddings deserve a closer look: most providers offer them, but with different models, and not all support the dimensions parameter for setting vector size. If embeddings are part of your move, check the specific model and parameters separately from chat.

The same applies to individual request parameters. Three that commonly fail: logprobs (returns probability scores for each generated token), logit_bias (makes specific tokens, word pieces identified by ID, more or less likely to appear), and n greater than 1 (asks for multiple answers to one prompt). When a provider doesn’t support a parameter, it fails in one of two ways: some reject the request with a 400 error (Groq does this, which at least tells you immediately), others accept the request and silently ignore the parameter, which is worse because your code runs but doesn’t do what you think.

A simple rule of thumb: if your application only sends chat requests with standard settings (temperature, max tokens) and doesn’t stream, every provider below is a true drop-in. Each extra feature you depend on (streaming tool calls, guaranteed JSON output, embeddings, probability scores) shrinks the list of providers that will work without changes.

Every provider below is judged on the same six things, listed in the order that most often decides a switch:

1. How compatible it really is: how much of OpenAI’s API the provider supports, and whether it clearly documents what it doesn’t. A provider that publishes its gaps is safer than one that claims full compatibility.
2. Speed: two numbers matter: how long before the first piece of the answer appears (time to first token, or TTFT) and how fast the rest of the answer arrives (tokens per second, or TPS). Test these yourself with your own prompts, since published numbers vary by model, region, and how busy the servers are.
3. Clear pricing: does the provider publish its per-token prices on a public page, or do you have to talk to sales? Are there extra fees on top of the token price?
4. Which models you can use: some providers only serve open models (like Llama or GPT-OSS, which anyone can download and host); others also resell closed models (GPT, Claude, Gemini, which only their owners serve).
5. How much work the switch takes: is it truly just changing the server address and API key, or will you need to change your code too?
6. Room to grow: how many requests you can send per minute on the default plan, and what the provider offers when you need more (dedicated capacity, reserved throughput, enterprise tiers).

Providers are listed alphabetically. Pricing comes from each provider’s official pricing page, checked in July 2026, using the same two models across providers so the numbers can be compared directly: GPT-OSS-120B and Llama 3.3 70B. Speed and latency figures are median measurements from Artificial Analysis’ GPT-OSS-120B provider benchmark (10K-token input) where available; Nebius pricing and speed figures are as listed in its Token Factory console (July 2026). Prices and performance change often, so check the linked sources before you commit.

A note on the latency figures: GPT-OSS-120B is a reasoning model, so Artificial Analysis’ “time to first answer token” includes the model’s thinking time before the answer begins, which is why the figures are in whole seconds rather than milliseconds. DigitalOcean’s figure is also an Artificial Analysis measurement, but on a different model (DeepSeek V3.2, same 10K-token input) because DO is not among the providers in AA’s GPT-OSS-120B panel; treat it as a rough comparison, not an exact one.

Two things stand out from the table. First, no provider is 100% drop-in: everyone differs from OpenAI’s API in some way, and providers that document those differences openly are easier to move to than ones where you discover gaps in production.

Second, prices for the same open model are close across providers, and open models cost far less than the big closed models (Llama 3.3 70B runs $0.13 to $1.04 per million input tokens across these providers); in practice, the bigger differences tend to be which models a provider offers and how much of OpenAI’s API it supports. For closed models resold through a compatible endpoint, pricing usually stays close to the model owner’s rates, so the main benefit there is having everything behind one endpoint rather than saving money.

Providers appear in the same alphabetical order as the table.

DigitalOcean Inference Engine (Serverless Inference)

DigitalOcean’s Serverless Inference gives you a single OpenAI-compatible endpoint (https://inference.do-ai.run/v1/) that serves both open-weight models (Llama, Mistral, DeepSeek, GPT-OSS) and closed models from OpenAI and Anthropic, making it one of the few providers where a base-URL swap gets you Claude and GPT-class models alongside open ones, per DigitalOcean’s documentation.

Every model has a per-token rate listed on DO’s pricing page, and for commercial models, those rates match what the model’s owner charges directly: GPT-OSS-120B runs $0.10/$0.70 per million input/output tokens, Llama 3.3 70B is $0.65 flat, and closed models are billed at the model owner’s rates. On speed, Artificial Analysis measured DigitalOcean at 230 tokens per second and 0.96s time to first token on DeepSeek V3.2 with 10K-token inputs, the top output speed among the 12 providers tested on that model, as reported in DO’s engineering blog with charts sourced to Artificial Analysis’ DeepSeek V3.2 benchmark. It also covers more endpoints than most, per DO’s API endpoints documentation: Chat Completions, the newer Responses API, embeddings, image generation, and text-to-speech all work as OpenAI-style endpoints, plus an Anthropic-compatible /v1/messages endpoint for Claude Code and similar agent tools. The OpenAI Python SDK, LangChain, and LlamaIndex work without changes. Two gaps to note: speech-to-text (transcription) is not among the listed endpoints, and video generation and fal models use DO-specific asynchronous endpoints rather than OpenAI-style ones.

Fireworks AI

Fireworks serves open models (Llama, Qwen, DeepSeek, Kimi, GLM, GPT-OSS) at https://api.fireworks.ai/inference/v1, and it clearly documents where it differs from OpenAI, an honest approach worth rewarding. Two documented differences: where OpenAI returns an error if your prompt plus max_tokens is larger than the model’s context window, Fireworks by default lowers max_tokens to fit (you can bring back OpenAI’s error behavior with the context_length_exceeded_behavior setting), and Fireworks includes token usage stats in streaming responses, in the final chunk, where OpenAI historically didn’t.

Per Fireworks’ serverless pricing, GPT-OSS-120B runs $0.15/$0.60 per million tokens on the Standard path ($0.18/$0.72 on Priority), while models without an individual price, including Llama 3.3 70B, are priced by size ($0.90 flat for models over 16B parameters). Cached input is discounted per model, and batch requests are billed at 50% of the normal rates. On speed, Artificial Analysis measures Fireworks at 651.8 tokens per second on GPT-OSS-120B, the fastest of the providers compared here.

Groq

Groq runs open models on its own custom chip, the LPU (Language Processing Unit), built specifically for fast inference instead of GPUs, behind the endpoint https://api.groq.com/openai/v1. Its OpenAI compatibility documentation says plainly what’s missing: logprobs, logit_bias, top_logprobs, and messages[].name returns a 400 error, and n must equal 1.

One smaller detail from the same page: a temperature of exactly 0 is converted to a very small number (1e-8) rather than used as-is. Per Groq’s pricing page, GPT-OSS-120B is $0.15/$0.60 and Llama 3.3 70B Versatile is $0.59/$0.79 per million input/output tokens; Artificial Analysis measures Groq at 482.1 tokens per second on GPT-OSS-120B. The catalog is smaller than Together’s or Fireworks’. Groq tunes a chosen set of models for its hardware rather than hosting everything.

Nebius Token Factory

Nebius Token Factory serves 60+ open models through an OpenAI-compatible API, with per-model rates published in the Token Factory console and dedicated endpoints with a 99.9% uptime guarantee (SLA) for production use. Per the console (July 2026), GPT-OSS-120B runs $0.15/$0.60 per million input/output tokens at a listed 40 tokens per second, and Llama 3.3 70B Instruct runs $0.13/$0.40 at 25 tokens per second, the lowest Llama 3.3 70B price among the providers compared here. Each model comes in two versions, Base and Fast: the Fast version uses smaller batches, more compute per request, and speculative decoding to cut waiting time, at a higher per-token price, and you pick it by simply adding -fast to the model name in the API, per Nebius’s documentation. One thing that stands out: the API accepts the full set of vLLM settings (vLLM is the open-source serving engine Nebius builds on), which is more than OpenAI’s API accepts. That’s useful for teams that want controls OpenAI doesn’t offer, though anything vLLM-specific won’t carry back over. Like Fireworks, the catalog is open to models only.

OpenRouter

OpenRouter is a different kind of service: not a host but a router. It sits in front of dozens of other providers and gives you 300+ models, open and closed, behind one OpenAI-compatible endpoint (https://openrouter.ai/api/v1) and one API key. Per-token prices pass through from the underlying providers unchanged.

OpenRouter makes its money on a 5.5% fee when you buy credits (with a $0.80 minimum, so a $100 top-up leaves about $94.50 for inference, while small top-ups pay proportionally more) and a 5% fee on bring-your-own-key requests beyond 1M per month, per OpenRouter’s pricing page. The trade-off is the extra layer in the middle: because each request may be served by a different underlying provider, speed, exact streaming behavior, and how tool calling behaves can vary from request to request, though routing preferences let you pin specific providers.

Together AI

Together AI serves 200+ models across text, image, video, code, and audio through an OpenAI-compatible endpoint, with published pricing for every model: per token for text models, and per image, video, or character for the rest. GPT-OSS-120B runs $0.15/$0.60 and Llama 3.3 70B $1.04 flat per million tokens, per Together’s pricing page. Artificial Analysis measures Together at 581.6 tokens per second on GPT-OSS-120B, with the lowest time to first answer token (3.96s) of the open-model hosts compared here.

Its chat catalog centers on open-weight models, with no GPT- or Claude-class closed text models, though it does serve proprietary image, video, and audio models (GPT Image, Veo, Sora) alongside the open ones. What sets it apart is speed engineering, including ATLAS, its speculative-decoding system that learns from live traffic; a batch API at discounted rates; and a provisioned-throughput tier (PTUs), which is reserved capacity you buy ahead of time. Together also offers LoRA and full fine-tuning (supervised and DPO) priced per training token, and the fine-tuned model can then be served on Together, which matters if you plan to eventually customize an open model.

The code change itself is two lines. Using DigitalOcean as the example (the pattern is identical for every provider above, just swap the base URL and key):

from openai import OpenAI
import os

client = OpenAI(
    base_url="https://inference.do-ai.run/v1/",
    api_key=os.getenv("MODEL_ACCESS_KEY"),
)

response = client.chat.completions.create(
    model="openai-gpt-oss-120b",
    messages=[{"role": "user", "content": "Hello"}],
)

The rest of the work is where migrations actually take time:

Model name mapping. gpt-4o doesn’t exist on an alternative provider. You’ll map to a model of similar capability (llama-3.3-70b-instruct, gpt-oss-120b, etc.), and model IDs differ by provider, so query the provider’s /v1/models endpoint rather than guessing names.

Parameter check. Search your codebase for logprobs, logit_bias, n=, response_format, and tools. Each hit needs a check against the new provider’s documented support. Providers fail differently: some return a 400 error on unsupported parameters (Groq), others quietly adjust them (Fireworks’ max_tokens behavior).

Streaming checks. If your code makes its own assumptions when reading streaming chunks (rebuilding tool-call arguments from partial pieces is the common case), test that path specifically. This is the most common source of bugs after a switch.

Test for output changes. The same API shape does not mean the same model behavior. Run your existing test set (or at least a saved set of typical prompts) against the new provider before moving traffic over. Prompts tuned for one model family often need adjusting.

A sensible rollout: send a small share of production traffic to the new provider behind a feature flag, compare outputs and error rates for a week, then switch.

The less obvious risk isn’t switching to a compatible provider. It’s what happens after, when your application grows into parts of the OpenAI spec your new provider doesn’t support. Agent frameworks are the sharpest example. Tool-calling schemas, parallel tool calls, strict structured outputs, and the Responses API are where the spec is changing fastest and where alternative providers lag longest. An application that’s a clean drop-in today can get stuck tomorrow: you adopt an agent framework that sends strict: true tool schemas, your provider ignores or rejects them, and now you’re maintaining provider-specific workarounds, a quiet form of lock-in that’s harder to see than a proprietary API.

Two ways to protect yourself. First, prefer providers that document their differences over ones that claim full compatibility; a published gap list means you can check before you build, not after. Second, keep a small compatibility test suite, a dozen requests covering streaming tool calls, JSON mode, and your longest context, and run it against your provider on a schedule. The spec is a moving target; your provider’s coverage of it should be something you monitor, not something you assume.

OpenAI’s API has quietly become what the ecosystem builds against: the common interface of AI infrastructure, implemented by every serious provider. That changes the question. When switching costs drop to two lines of code, the decision is no longer whether you can leave a provider but what you should optimize for once you can. Speed, catalog, price, and completeness of compatibility pull in different directions, and the right answer depends on how your application actually works. The comparison above points to a few clear matches:

If you want open and closed models behind one endpoint, DigitalOcean and OpenRouter are the only two options here. DigitalOcean hosts the models itself with per-token rates that match each model owner’s pricing; OpenRouter routes to other providers and adds a fee on credits, with behavior that can vary by which provider serves the request.

If you want the fastest open-model serving, Fireworks, Together, and Groq lead the Artificial Analysis benchmarks, and each gets there differently: Fireworks with the highest measured output speed, Together with the lowest time to first answer token, Groq with custom LPU hardware. Test all three on your own prompts, since the gap between them is smaller than the gap between any published number and your production traffic.

If you’re optimizing for cost on open models, Nebius listed the lowest Llama 3.3 70B price in this comparison, and the size-tiered pricing at Fireworks and flat rates elsewhere are close enough that your token mix (input-heavy vs. output-heavy) matters more than the headline rate.

Whichever provider you pick, treat compatibility as something you verify, not something you assume. Run the parameter check and streaming tests from the migration section before cutting over, keep a small compatibility test suite running on a schedule, and re-check pricing pages before you commit real volume, because every number in this article has a date on it.

If your shortlist includes running open and closed models behind one endpoint, DigitalOcean’s Inference Engine offers per-token pricing with no minimum commitment and works with the OpenAI SDK out of the box. The quickstart takes a base URL, a model access key, and about five minutes.

Sources

All pricing figures were checked against official provider pricing pages in July 2026:

• DigitalOcean Gradient AI Platform Pricing · Serverless Inference with the OpenAI SDK · DO inference benchmark blog
• Together AI Pricing
• Fireworks Serverless Pricing · Fireworks OpenAI Compatibility
• Groq Pricing · Groq OpenAI Compatibility
• OpenRouter Pricing
• Nebius Token Factory Docs · Token Factory Pricing Console
• Speed and latency: Artificial Analysis, GPT-OSS-120B provider benchmark (median, 10K-token input, retrieved July 2026)